Dataset Viewer (First 5GB)
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2003.00467
|
NeuroTac: A Neuromorphic Optical Tactile Sensor applied to Texture Recognition
|
Developing artificial tactile sensing capabilities that rival human touch is a long-term goal in robotics and prosthetics. Gradually more elaborate biomimetic tactile sensors are being developed and applied to grasping and manipulation tasks to help achieve this goal. Here we present the neuroTac, a novel neuromorphic optical tactile sensor. The neuroTac combines the biomimetic hardware design from the TacTip sensor which mimicks the layered papillae structure of human glabrous skin, with an event-based camera (DAVIS240, iniVation) and algorithms which transduce contact information in the form of spike trains. The performance of the sensor is evaluated on a texture classification task, with four spike coding methods being implemented and compared: Intensive, Spatial, Temporal and Spatiotemporal. We found timing-based coding methods performed with the highest accuracy over both artificial and natural textures. The spike-based output of the neuroTac could enable the development of biomimetic tactile perception algorithms in robotics as well as non-invasive and invasive haptic feedback methods in prosthetics.
| Not supported with pagination yet | null |
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"caption": [
"The neuroTac sensor. The tip contains internal pins treated as mechanoreceptors, which produce pixel events at the event-based camera. These are pooled and converted to taxel events (akin to biological spikes) upstream.",
"Sensor operation. Pixel events produced by the camera (iniVation, DAVIS240) are initially filtered, then pooled into a single taxel event. Finally, the position of taxels is updated (see Section III-A).",
"Experimental setup: The NeuroTac is attached to a 6-dof industrial robot arm (ABB, IRB120) which is used to slide the sensor horizontally across the 3d-printed textures.",
"Natural textures used for classification. 20 textures were used, with a full list presented in Table II.",
"Examples of the spike trains produced by the NeuroTac when sliding across three different 3d-printed textures. From left to right, textures are 0 mm grid (smooth), 2.5 mm grid and 5 mm grid, with the corresponding spike trains, spatial and temporal distributions displayed below them.",
"Examples of the spike trains produced by the NeuroTac when sliding across three different 3d-printed textures. From left to right, textures are 0 mm grid (smooth), 2.5 mm grid and 5 mm grid, with the corresponding spike trains, spatial and temporal distributions displayed below them.",
"Examples of the spike trains produced by the NeuroTac when sliding across three different 3d-printed textures. From left to right, textures are 0 mm grid (smooth), 2.5 mm grid and 5 mm grid, with the corresponding spike trains, spatial and temporal distributions displayed below them.",
"Examples of the spike trains produced by the NeuroTac when sliding across three different 3d-printed textures. From left to right, textures are 0 mm grid (smooth), 2.5 mm grid and 5 mm grid, with the corresponding spike trains, spatial and temporal distributions displayed below them.",
"Examples of the spike trains produced by the NeuroTac when sliding across three different 3d-printed textures. From left to right, textures are 0 mm grid (smooth), 2.5 mm grid and 5 mm grid, with the corresponding spike trains, spatial and temporal distributions displayed below them.",
"Examples of the spike trains produced by the NeuroTac when sliding across three different 3d-printed textures. From left to right, textures are 0 mm grid (smooth), 2.5 mm grid and 5 mm grid, with the corresponding spike trains, spatial and temporal distributions displayed below them.",
"Examples of the spike trains produced by the NeuroTac when sliding across three different 3d-printed textures. From left to right, textures are 0 mm grid (smooth), 2.5 mm grid and 5 mm grid, with the corresponding spike trains, spatial and temporal distributions displayed below them.",
"Examples of the spike trains produced by the NeuroTac when sliding across three different 3d-printed textures. From left to right, textures are 0 mm grid (smooth), 2.5 mm grid and 5 mm grid, with the corresponding spike trains, spatial and temporal distributions displayed below them.",
"Examples of the spike trains produced by the NeuroTac when sliding across three different 3d-printed textures. From left to right, textures are 0 mm grid (smooth), 2.5 mm grid and 5 mm grid, with the corresponding spike trains, spatial and temporal distributions displayed below them.",
"Examples of the spike trains produced by the NeuroTac when sliding across three different 3d-printed textures. From left to right, textures are 0 mm grid (smooth), 2.5 mm grid and 5 mm grid, with the corresponding spike trains, spatial and temporal distributions displayed below them.",
"Examples of the spike trains produced by the NeuroTac when sliding across three different 3d-printed textures. From left to right, textures are 0 mm grid (smooth), 2.5 mm grid and 5 mm grid, with the corresponding spike trains, spatial and temporal distributions displayed below them.",
"Examples of the spike trains produced by the NeuroTac when sliding across three different 3d-printed textures. From left to right, textures are 0 mm grid (smooth), 2.5 mm grid and 5 mm grid, with the corresponding spike trains, spatial and temporal distributions displayed below them.",
"Confusion matrices for artificial texture classification with each of the 4 encoding methods: Intensive (Top Left), Spatial (Top Right), Temporal (Bottom Left) and Spatiotemporal (Bottom Right).",
"Confusion matrices for artificial texture classification with each of the 4 encoding methods: Intensive (Top Left), Spatial (Top Right), Temporal (Bottom Left) and Spatiotemporal (Bottom Right).",
"Confusion matrices for artificial texture classification with each of the 4 encoding methods: Intensive (Top Left), Spatial (Top Right), Temporal (Bottom Left) and Spatiotemporal (Bottom Right).",
"Confusion matrices for artificial texture classification with each of the 4 encoding methods: Intensive (Top Left), Spatial (Top Right), Temporal (Bottom Left) and Spatiotemporal (Bottom Right).",
"Confusion matrices for natural texture classification with each of the 4 encoding methods: Intensive (Top Left), Spatial (Top Right), Temporal (Bottom Left) and Spatiotemporal (Bottom Right).",
"Confusion matrices for natural texture classification with each of the 4 encoding methods: Intensive (Top Left), Spatial (Top Right), Temporal (Bottom Left) and Spatiotemporal (Bottom Right).",
"Confusion matrices for natural texture classification with each of the 4 encoding methods: Intensive (Top Left), Spatial (Top Right), Temporal (Bottom Left) and Spatiotemporal (Bottom Right).",
"Confusion matrices for natural texture classification with each of the 4 encoding methods: Intensive (Top Left), Spatial (Top Right), Temporal (Bottom Left) and Spatiotemporal (Bottom Right)."
]
}
|
[
"cs.RO"
] | null |
ICRA
|
2003.01936
|
Automatic Signboard Detection and Localization in Densely Populated Developing Cities
|
Most city establishments of developing cities are digitally unlabeled because of the lack of automatic annotation systems. Hence location and trajectory services such as Google Maps, Uber etc remain underutilized in such cities. Accurate signboard detection in natural scene images is the foremost task for error-free information retrieval from such city streets. Yet, developing accurate signboard localization system is still an unresolved challenge because of its diverse appearances that include textual images and perplexing backgrounds. We present a novel object detection approach that can detect signboards automatically and is suitable for such cities. We use Faster R-CNN based localization by incorporating two specialized pretraining methods and a run time efficient hyperparameter value selection algorithm. We have taken an incremental approach in reaching our final proposed method through detailed evaluation and comparison with baselines using our constructed SVSO (Street View Signboard Objects) signboard dataset containing signboard natural scene images of six developing countries. We demonstrate state-of-the-art performance of our proposed method on both SVSO dataset and Open Image Dataset. Our proposed method can detect signboards accurately (even if the images contain multiple signboards with diverse shapes and colours in a noisy background) achieving 0.90 <MATH> mAP </MATH> (mean average precision) score on SVSO independent test set. Our implementation is available at: https://github.com/sadrultoaha/Signboard-Detection
| Not supported with pagination yet | null |
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"caption": [
"Some sample images from SVSO dataset",
"Faster R-CNN architecture: The single unified network for object detection. The RPN module acts as the ‘attention’ and the Fast R-CNN module serves as the ‘detector’ of this architecture.",
"Signboard and non-signboard image generation using Object Region Extractor (ORE) method",
"Generation of RPN layer anchor boxes: (A) - ROI proposals based on default anchor ratios and scales. (B) - ROI proposals based on proposed ARAS algorithm.",
"Step 6 & 7 ARAS algorithm: final anchor ratios (AR) and anchor scales (AS) are determined by including max aspect ratio and max height",
"Anchor box sizes and shapes according to anchor ratios (AR) and anchor scales (AS), which are generated from ARAS algorithm.",
"Unified architecture of our proposed object detection algorithm.",
"Training and validation loss-accuracy graph of VGG16 CNN model pretraining which has been used later as CNN backbone of Faster R-CNN",
"Training loss graph of the RPN & Fast R-CNN layer’s pretrained model.",
"Validation loss and <MATH> mAP </MATH> score graph of our proposed algorithm mainstream training.",
"Validation <MATH> mAP </MATH> score vs training epoch number curves: comparison after inclusion of proposed modifications",
"Validation <MATH> mAP </MATH> score vs training epoch number curves: comparative analysis of our proposed model with current state-of-the-art methods",
"Detection performance of YOLOv4, YOLOv5, and our proposed algorithm on a sample SVSO test set image",
"Detection performance of YOLOv4, YOLOv5, and our proposed algorithm on a sample SVSO test set image",
"Detection performance of YOLOv4, YOLOv5, and our proposed algorithm on a sample SVSO test set image",
"Detection result of our final model on sample SVSO test set images: (a): signboards detected and localized accurately (b): a banner has been wrongly classified as signboard."
]
}
|
[
"cs.CV"
] | null | null |
2004.03378
|
Error-Corrected Margin-Based Deep Cross-Modal Hashing for Facial Image Retrieval
|
Cross-modal hashing facilitates mapping of heterogeneous multimedia data into a common Hamming space, which can be utilized for fast and flexible retrieval across different modalities. In this paper, we propose a novel cross-modal hashing architecture-deep neural decoder cross-modal hashing (DNDCMH), which uses a binary vector specifying the presence of certain facial attributes as an input query to retrieve relevant face images from a database. The DNDCMH network consists of two separate components: an attribute-based deep cross-modal hashing (ADCMH) module, which uses a margin (m)-based loss function to efficiently learn compact binary codes to preserve similarity between modalities in the Hamming space, and a neural error correcting decoder (NECD), which is an error correcting decoder implemented with a neural network. The goal of NECD network in DNDCMH is to error correct the hash codes generated by ADCMH to improve the retrieval efficiency. The NECD network is trained such that it has an error correcting capability greater than or equal to the margin (m) of the margin-based loss function. This results in NECD can correct the corrupted hash codes generated by ADCMH up to the Hamming distance of m. We have evaluated and compared DNDCMH with state-of-the-art cross-modal hashing methods on standard datasets to demonstrate the superiority of our method.
| Not supported with pagination yet | null |
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"caption": [
"Schematic illustration of our DNDCMH. It consists of two networks, the ADCMH and NECD. The ADCMH network consists of the Image CNN and the Attribute-MLP. The training of the DNDCMH is performed in 2 stages. The first stage is divided into: Stage 1(a) and Stage 1(b). In Stage 1(a), the Attribute-MLP and Image CNN of the ADCMH network are trained together with the quantization loss, entropy maximization and distance-based logistic loss with margin <MATH> m </MATH> . In Stage 1(b), NECD with an error-correcting capability of <MATH> e\\geq m </MATH> is trained using the cross-entropy loss. In Stage 2, the trained ADCMH from Stage 1(a) and trained NECD from Stage 1(b) are used (this is indicated by the red arrows). In Stage 2, the same training data as used in Stage 1(a) is passed through the trained ADCMH and the real-valued output of the ADCMH is then passed through the trained NECD to provide the final hash codes. Next, these final hash codes are used as target outputs (final hash codes and target outputs are the same as indicated by the red arrows) to optimize the trained ADCMH network. The optimization of the ADCMH network in this Stage is performed by using cross-entropy loss. The blue arrows indicates the inputs and the connections between the network and the losses. This is only the training process, the testing process is shown in Fig. 5",
"Diagram for the first stage showing the importance of margin-based distance logistic loss.",
"Relating the error-correcting capability of ECC to the margin of DLL. Error-Correcting capability",
"Relating the error-correcting capability of ECC to the margin of DLL. Margin of DLL",
"Relating the error-correcting capability of ECC to the margin of DLL. Margin of DLL and the error-correcting capability",
"Illustration of the testing process. The gallery of test images is passed through the Image-CNN of the ADCMH network to generate the image hash code. The attribute query binary vector is passed through the trained Attribute-MLP to generate the attribute hash codes. The Hamming distance (HD) between the attribute hash code and the image hash code is computed. The HD between the each of the gallery image hash code and the attribute hash code is shown under the corresponding image hash code. The green color represents the lowest HD image hash code. The red color in the attribute query indicates the three attributes that we are interested in. It is a triple attribute query example.",
"Ranking performance on the CelebA dataset. Due to space restriction, the legend is shown in the box on the left.",
"Ranking performance on the CelebA dataset. Due to space restriction, the legend is shown in the box on the left. Single",
"Ranking performance on the CelebA dataset. Due to space restriction, the legend is shown in the box on the left. Double",
"Ranking performance on the CelebA dataset. Due to space restriction, the legend is shown in the box on the left. Triple",
"Ranking performance on the LFW dataset. Due to space restriction, the legend is shown in the box on the left.",
"Ranking performance on the LFW dataset. Due to space restriction, the legend is shown in the box on the left. Single",
"Ranking performance on the LFW dataset. Due to space restriction, the legend is shown in the box on the left. Double",
"Ranking performance on the LFW dataset. Due to space restriction, the legend is shown in the box on the left. Triple",
"Ranking performance on the YouTube dataset. Due to space restriction, the legend is shown in the box on the left.",
"Ranking performance on the YouTube dataset. Due to space restriction, the legend is shown in the box on the left. Single",
"Ranking performance on the YouTube dataset. Due to space restriction, the legend is shown in the box on the left. Double",
"Ranking performance on the YouTube dataset. Due to space restriction, the legend is shown in the box on the left. Triple",
"Influence of Hyper-parameters on P-R curves for CelebA dataset. P-R curve for <MATH> \\theta </MATH>",
"Influence of Hyper-parameters on P-R curves for CelebA dataset. P-R curve for <MATH> \\lambda </MATH>",
"Influence of Hyper-parameters on P-R curves for CelebA dataset. P-R curve for <MATH> \\gamma </MATH>",
"Influence of Hyper-parameters on P-R curves for LFW dataset. P-R curve for <MATH> \\theta </MATH>",
"Influence of Hyper-parameters on P-R curves for LFW dataset. P-R curve for <MATH> \\lambda </MATH>",
"Influence of Hyper-parameters on P-R curves for LFW dataset. P-R curve for <MATH> \\gamma </MATH>",
"Influence of Hyper-parameters on P-R curves for YouTube dataset. P-R curve for <MATH> \\theta </MATH>",
"Influence of Hyper-parameters on P-R curves for YouTube dataset. P-R curve for <MATH> \\lambda </MATH>",
"Influence of Hyper-parameters on P-R curves for YouTube dataset. P-R curve for <MATH> \\gamma </MATH>",
"Qualitative results: Retrieved images using DNDCMH and ADCMH by giving different combinations of facial attributes as a query. Tick and cross symbols indicate the correct and wrong image retrieval from the testing set, respectively.",
"Tanner Graph for the parity check matrix shown in (9)."
]
}
|
[
"cs.CV"
] | null | null |
2004.09199
|
Generative Feature Replay For Class-Incremental Learning
|
Humans are capable of learning new tasks without forgetting previous ones, while neural networks fail due to catastrophic forgetting between new and previously-learned tasks. We consider a class-incremental setting which means that the task-ID is unknown at inference time. The imbalance between old and new classes typically results in a bias of the network towards the newest ones. This imbalance problem can either be addressed by storing exemplars from previous tasks, or by using image replay methods. However, the latter can only be applied to toy datasets since image generation for complex datasets is a hard problem. We propose a solution to the imbalance problem based on generative feature replay which does not require any exemplars. To do this, we split the network into two parts: a feature extractor and a classifier. To prevent forgetting, we combine generative feature replay in the classifier with feature distillation in the feature extractor. Through feature generation, our method reduces the complexity of generative replay and prevents the imbalance problem. Our approach is computationally efficient and scalable to large datasets. Experiments confirm that our approach achieves state-of-the-art results on CIFAR-100 and ImageNet, while requiring only a fraction of the storage needed for exemplar-based continual learning. Code available at https://github.com/xialeiliu/GFR-IL. <NOTE> footnotetext: Both authors contributed equally. </NOTE>
| Not supported with pagination yet | null |
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"caption": [
"Canonical Correlation Analysis (CCA) similarity of different continual learning methods performed on equally distributed 4-task scenario on CIFAR-100. The vertical axis shows the evolution over time of the correlation for given task activations. The horizontal axis shows correlation at different layers of the network.",
"Proposed framework. Distillation and feature generation are used during training to prevent forgetting previous tasks. Once the task is learned, the feature generator is updated with adversarial training and distillation to prevent forgetting in the generator.",
"Comparison in the average accuracy (Top) and the average forgetting (Bottom) with various methods on ImageNet-Subset. The first task has the half number of classes, and the remaining classes are divided into 5, 10, 25 tasks respectively. The lines with symbols are methods without using any exemplars, and without symbols are methods with 2000 exemplars. (Joint Training: 77.6)",
"Comparison in the average accuracy (Top) and the average forgetting (Bottom) with various methods on ImageNet-Subset. The first task has the half number of classes, and the remaining classes are divided into 5, 10, 25 tasks respectively. The lines with symbols are methods without using any exemplars, and without symbols are methods with 2000 exemplars. (Joint Training: 77.6)",
"Comparison in the average accuracy (Top) and the average forgetting (Bottom) with various methods on ImageNet-Subset. The first task has the half number of classes, and the remaining classes are divided into 5, 10, 25 tasks respectively. The lines with symbols are methods without using any exemplars, and without symbols are methods with 2000 exemplars. (Joint Training: 77.6)",
"Comparison in the average accuracy (Top) and the average forgetting (Bottom) with various methods on ImageNet-Subset. The first task has the half number of classes, and the remaining classes are divided into 5, 10, 25 tasks respectively. The lines with symbols are methods without using any exemplars, and without symbols are methods with 2000 exemplars. (Joint Training: 77.6)",
"Comparison in the average accuracy (Top) and the average forgetting (Bottom) with various methods on ImageNet-Subset. The first task has the half number of classes, and the remaining classes are divided into 5, 10, 25 tasks respectively. The lines with symbols are methods without using any exemplars, and without symbols are methods with 2000 exemplars. (Joint Training: 77.6)",
"Comparison in the average accuracy (Top) and the average forgetting (Bottom) with various methods on ImageNet-Subset. The first task has the half number of classes, and the remaining classes are divided into 5, 10, 25 tasks respectively. The lines with symbols are methods without using any exemplars, and without symbols are methods with 2000 exemplars. (Joint Training: 77.6)",
"Comparison in the average accuracy (Top) and the average forgetting (Bottom) with various methods on CIFAR-100. The lines with symbols are methods without using any exemplars, and without symbols are methods with 2000 exemplars. (Joint Training: 72.0)",
"Comparison in the average accuracy (Top) and the average forgetting (Bottom) with various methods on CIFAR-100. The lines with symbols are methods without using any exemplars, and without symbols are methods with 2000 exemplars. (Joint Training: 72.0)",
"Comparison in the average accuracy (Top) and the average forgetting (Bottom) with various methods on CIFAR-100. The lines with symbols are methods without using any exemplars, and without symbols are methods with 2000 exemplars. (Joint Training: 72.0)",
"Comparison in the average accuracy (Top) and the average forgetting (Bottom) with various methods on CIFAR-100. The lines with symbols are methods without using any exemplars, and without symbols are methods with 2000 exemplars. (Joint Training: 72.0)",
"Comparison in the average accuracy (Top) and the average forgetting (Bottom) with various methods on CIFAR-100. The lines with symbols are methods without using any exemplars, and without symbols are methods with 2000 exemplars. (Joint Training: 72.0)",
"Comparison in the average accuracy (Top) and the average forgetting (Bottom) with various methods on CIFAR-100. The lines with symbols are methods without using any exemplars, and without symbols are methods with 2000 exemplars. (Joint Training: 72.0)",
"Comparison in the average accuracy (Top) and the average forgetting (Bottom) with various methods on ImageNet-1000. The first task has the half number of classes, and the remaining classes are divided into 5, 10 and 25 respectively. The lines with symbols are methods without using any exemplars, and without symbols are methods with 20000 exemplars.",
"Comparison in the average accuracy (Top) and the average forgetting (Bottom) with various methods on ImageNet-1000. The first task has the half number of classes, and the remaining classes are divided into 5, 10 and 25 respectively. The lines with symbols are methods without using any exemplars, and without symbols are methods with 20000 exemplars.",
"Comparison in the average accuracy (Top) and the average forgetting (Bottom) with various methods on ImageNet-1000. The first task has the half number of classes, and the remaining classes are divided into 5, 10 and 25 respectively. The lines with symbols are methods without using any exemplars, and without symbols are methods with 20000 exemplars.",
"Comparison in the average accuracy (Top) and the average forgetting (Bottom) with various methods on ImageNet-1000. The first task has the half number of classes, and the remaining classes are divided into 5, 10 and 25 respectively. The lines with symbols are methods without using any exemplars, and without symbols are methods with 20000 exemplars.",
"Comparison in the average accuracy (Top) and the average forgetting (Bottom) with various methods on ImageNet-1000. The first task has the half number of classes, and the remaining classes are divided into 5, 10 and 25 respectively. The lines with symbols are methods without using any exemplars, and without symbols are methods with 20000 exemplars.",
"Comparison in the average accuracy (Top) and the average forgetting (Bottom) with various methods on ImageNet-1000. The first task has the half number of classes, and the remaining classes are divided into 5, 10 and 25 respectively. The lines with symbols are methods without using any exemplars, and without symbols are methods with 20000 exemplars.",
"Real features (Red) and Generated features (Blue) on ImageNet-Subset of first task after training all tasks in 5, 10 and 25 tasks setting, respectively."
]
}
|
[
"cs.CV",
"cs.LG"
] | null |
CVPR (workshop)
|
2005.01351
|
Anchors Based Method for Fingertips Position Estimation from a Monocular RGB Image using Deep Neural Network
|
In Virtual, augmented, and mixed reality, the use of hand gestures is increasingly becoming popular to reduce the difference between the virtual and real world. The precise location of the fingertip is essential/crucial for a seamless experience. Much of the research work is based on using depth information for the estimation of the fingertips position. However, most of the work using RGB images for fingertips detection is limited to a single finger. The detection of multiple fingertips from a single RGB image is very challenging due to various factors. In this paper, we propose a deep neural network (DNN) based methodology to estimate the fingertips position. We christened this methodology as an Anchor based Fingertips Position Estimation (ABFPE), and it is a two-step process. The fingertips location is estimated using regression by computing the difference in the location of a fingertip from the nearest anchor point. The proposed framework performs the best with limited dependence on hand detection results. In our experiments on the SCUT-Ego-Gesture dataset, we achieved the fingertips detection error of 2.3552 pixels on a video frame with a resolution of <MATH> 640\times 480 </MATH> and about <MATH> 92.98\% </MATH> of test images have average pixel errors of five pixels.
| Not supported with pagination yet | null |
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],
"caption": [
"The proposed framework for the estimation of fingertip(s) position.",
"The scatter plot showing the distribution of fingertips location for various gestures made using single hand. The data of fingertip(s) location is from SCUT-Ego-Gesture [20] dataset. Gesture two",
"The scatter plot showing the distribution of fingertips location for various gestures made using single hand. The data of fingertip(s) location is from SCUT-Ego-Gesture [20] dataset. Gesture three",
"The scatter plot showing the distribution of fingertips location for various gestures made using single hand. The data of fingertip(s) location is from SCUT-Ego-Gesture [20] dataset. Gesture four",
"The scatter plot showing the distribution of fingertips location for various gestures made using single hand. The data of fingertip(s) location is from SCUT-Ego-Gesture [20] dataset. Gesture five",
"Instead of using regression to directly estimate the positions of fingertips (marked with red square boxes), we estimate the error ‘d’ of a fingertip from the nearest anchors point (marked with black dot).",
"The architecture used for estimation of fingertips position. The backbone model can be any state-of-the-art model (e.g. VGG16 [31], ResNet [30], etc.).",
"Some instances of hand region detection using the YOLOv3 [23] model.",
"Some samples results of fingertips detection results on SCUT-Ego-Gesture [20] dataset. SpatialNet [41]",
"Some samples results of fingertips detection results on SCUT-Ego-Gesture [20] dataset. YOLSE [20]",
"Some samples results of fingertips detection results on SCUT-Ego-Gesture [20] dataset. FPEFI [27]",
"Some samples results of fingertips detection results on SCUT-Ego-Gesture [20] dataset. The proposed method (ABFPE)",
"Some samples results of fingertips detection results on HGR [37] dataset. SpatialNet [41]",
"Some samples results of fingertips detection results on HGR [37] dataset. YOLSE [20]",
"Some samples results of fingertips detection results on HGR [37] dataset. FPEFI [27]",
"Some samples results of fingertips detection results on HGR [37] dataset. The proposed method (ABFPE)",
"Comparison of the cumulative distribution curves of the proposed methodology with other existing algorithms on different datasets (a) SCUT-Ego-Gesture dataset [20], (b) SCUT-Ego-Finger dataset [1], and (c) HGR dataset [37]. SCUT-Ego-Gesture dataset [20]",
"Comparison of the cumulative distribution curves of the proposed methodology with other existing algorithms on different datasets (a) SCUT-Ego-Gesture dataset [20], (b) SCUT-Ego-Finger dataset [1], and (c) HGR dataset [37]. SCUT- Ego-Finger dataset [1]",
"Comparison of the cumulative distribution curves of the proposed methodology with other existing algorithms on different datasets (a) SCUT-Ego-Gesture dataset [20], (b) SCUT-Ego-Finger dataset [1], and (c) HGR dataset [37]. HGR dataset [37]",
"There is no effect on performance of the proposed model due to the rotation of the input image."
]
}
|
[
"cs.CV",
"cs.HC",
"eess.IV"
] | null | null |
2006.05873
|
WasteNet: Waste Classification at the Edge for Smart Bins
|
Smart Bins have become popular in smart cities and campuses around the world. These bins have a compaction mechanism that increases the bins’ capacity as well as automated real-time collection notifications. In this paper, we propose WasteNet, a waste classification model based on convolutional neural networks that can be deployed on a low power device at the edge of the network, such as a Jetson Nano. The problem of segregating waste is a big challenge for many countries around the world. Automated waste classification at the edge allows for fast intelligent decisions in smart bins without needing access to the cloud. Waste is classified into six categories: paper, cardboard, glass, metal, plastic and other. Our model achieves a 97% prediction accuracy on the test dataset. This level of classification accuracy will help to alleviate some common smart bin problems, such as recycling contamination, where different types of waste become mixed with recycling waste causing the bin to be contaminated. It also makes the bins more user friendly as citizens do not have to worry about disposing their rubbish in the correct bin as the smart bin will be able to make the decision for them.
| Not supported with pagination yet | null |
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],
"caption": [
"Urban Smart Bin",
"Transfer Learning Categories",
"Deep Transfer Learning Approaches",
"Deep Transfer Learning Approaches",
"Deep Transfer Learning Approaches",
"Images of Waste Categories",
"Images of Waste Categories",
"Images of Waste Categories",
"Images of Waste Categories",
"Images of Waste Categories",
"Images of Waste Categories",
"Images of Waste Categories",
"Images of Waste Categories",
"Images of Waste Categories",
"Loss Over Epochs",
"Accuracy Over Epochs",
"Waste Classification Confusion Matrix",
"Prediction/Actual Loss/Probability test",
"Prediction/Actual Loss/Probability",
"Prediction/Actual Loss/Probability",
"Prediction/Actual Loss/Probability",
"Prediction/Actual Loss/Probability",
"Prediction/Actual Loss/Probability"
]
}
|
[
"cs.CV",
"cs.CY"
] | null | null |
2006.09917
|
FISHING Net: Future Inference of Semantic Heatmaps In Grids
|
For autonomous robots to navigate a complex environment, it is crucial to understand the surrounding scene both geometrically and semantically. Modern autonomous robots employ multiple sets of sensors, including lidars, radars, and cameras. Managing the different reference frames and characteristics of the sensors, and merging their observations into a single representation complicates perception. Choosing a single unified representation for all sensors simplifies the task of perception and fusion. In this work, we present an end-to-end pipeline that performs semantic segmentation and short term prediction using a top-down representation. Our approach consists of an ensemble of neural networks which take in sensor data from different sensor modalities and transform them into a single common top-down semantic grid representation. We find this representation favorable as it is agnostic to sensor-specific reference frames and captures both the semantic and geometric information for the surrounding scene. Because the modalities share a single output representation, they can be easily aggregated to produce a fused output. In this work we predict short-term semantic grids but the framework can be extended to other tasks. This approach offers a simple, extensible, end-to-end approach for multi-modal perception and prediction.
| Not supported with pagination yet | null |
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],
"caption": [
"Top-down view ground truth semantic grid. (Vehicles: Green; VRU: Red; Background: Blue).",
"Top-Down Reference Frame",
"Subset of lidar features extracted at current timestep. (a) Lidar occupancy, white is occupied and black is available. (b) Lidar density. (c) Maximum <MATH> Z </MATH> measurement. (d) Maximum <MATH> Z </MATH> measurement for 1st height slice",
"Subset of lidar features extracted at current timestep. (a) Lidar occupancy, white is occupied and black is available. (b) Lidar density. (c) Maximum <MATH> Z </MATH> measurement. (d) Maximum <MATH> Z </MATH> measurement for 1st height slice",
"Subset of lidar features extracted at current timestep. (a) Lidar occupancy, white is occupied and black is available. (b) Lidar density. (c) Maximum <MATH> Z </MATH> measurement. (d) Maximum <MATH> Z </MATH> measurement for 1st height slice",
"Subset of lidar features extracted at current timestep. (a) Lidar occupancy, white is occupied and black is available. (b) Lidar density. (c) Maximum <MATH> Z </MATH> measurement. (d) Maximum <MATH> Z </MATH> measurement for 1st height slice",
"Quiver plot for a single frame velocity features from radars",
"Vision Input",
"Vision architecture on top. Lidar and Radar Architecture on bottom.",
"Vision architecture on top. Lidar and Radar Architecture on bottom.",
"Precision and Recall Metrics for NuScenes dataset",
"Precision and Recall Metrics for NuScenes dataset",
"Precision and Recall Metrics for NuScenes dataset",
"Precision and Recall Metrics for NuScenes dataset",
"Precision and Recall Metrics for Purpose-Built dataset",
"Precision and Recall Metrics for Purpose-Built dataset",
"Precision and Recall Metrics for Purpose-Built dataset",
"Precision and Recall Metrics for Purpose-Built dataset",
"Label (lower left), input (top) and predictions (bottom) for lidar radar and vision. One of the six input cameras is visualized."
]
}
|
[
"cs.CV",
"cs.LG"
] | null | null |
2006.14787
|
On Equivariant and Invariant Learning of Object Landmark Representations
|
Given a collection of images, humans are able to discover landmarks by modeling the shared geometric structure across instances. This idea of geometric equivariance has been widely used for the unsupervised discovery of object landmark representations. In this paper, we develop a simple and effective approach by combining instance-discriminative and spatially-discriminative contrastive learning. We show that when a deep network is trained to be invariant to geometric and photometric transformations, representations emerge from its intermediate layers that are highly predictive of object landmarks. Stacking these across layers in a “hypercolumn” and projecting them using spatially-contrastive learning further improves their performance on matching and few-shot landmark regression tasks. We also present a unified view of existing equivariant and invariant representation learning approaches through the lens of contrastive learning, shedding light on the nature of invariances learned. Experiments on standard benchmarks for landmark learning, as well as a new challenging one we propose, show that the proposed approach surpasses prior state-of-the-art.
| Not supported with pagination yet | null |
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"caption": [
"Visualization of landmark matching with cosine distance using 3840-D hypercolumn features and 256-D features projected from hypercolumn. Failure cases of using hypercolumn includes (Left) mismatching between two eyes and (Middle) lack of robustness to large viewpoint or (Right) appearance changes across different identities. The proposed feature projection method alleviates these issues.",
"Visualization of landmark matching with cosine distance using 3840-D hypercolumn features and 256-D features projected from hypercolumn. Failure cases of using hypercolumn includes (Left) mismatching between two eyes and (Middle) lack of robustness to large viewpoint or (Right) appearance changes across different identities. The proposed feature projection method alleviates these issues.",
"Visualization of landmark matching with cosine distance using 3840-D hypercolumn features and 256-D features projected from hypercolumn. Failure cases of using hypercolumn includes (Left) mismatching between two eyes and (Middle) lack of robustness to large viewpoint or (Right) appearance changes across different identities. The proposed feature projection method alleviates these issues.",
"Visualization of landmark matching with cosine distance using 3840-D hypercolumn features and 256-D features projected from hypercolumn. Failure cases of using hypercolumn includes (Left) mismatching between two eyes and (Middle) lack of robustness to large viewpoint or (Right) appearance changes across different identities. The proposed feature projection method alleviates these issues.",
"Visualization of landmark matching with cosine distance using 3840-D hypercolumn features and 256-D features projected from hypercolumn. Failure cases of using hypercolumn includes (Left) mismatching between two eyes and (Middle) lack of robustness to large viewpoint or (Right) appearance changes across different identities. The proposed feature projection method alleviates these issues.",
"Visualization of landmark matching with cosine distance using 3840-D hypercolumn features and 256-D features projected from hypercolumn. Failure cases of using hypercolumn includes (Left) mismatching between two eyes and (Middle) lack of robustness to large viewpoint or (Right) appearance changes across different identities. The proposed feature projection method alleviates these issues.",
"Visualization of landmark matching with cosine distance using 3840-D hypercolumn features and 256-D features projected from hypercolumn. Failure cases of using hypercolumn includes (Left) mismatching between two eyes and (Middle) lack of robustness to large viewpoint or (Right) appearance changes across different identities. The proposed feature projection method alleviates these issues.",
"Visualization of landmark matching with cosine distance using 3840-D hypercolumn features and 256-D features projected from hypercolumn. Failure cases of using hypercolumn includes (Left) mismatching between two eyes and (Middle) lack of robustness to large viewpoint or (Right) appearance changes across different identities. The proposed feature projection method alleviates these issues.",
"Visualization of landmark matching with cosine distance using 3840-D hypercolumn features and 256-D features projected from hypercolumn. Failure cases of using hypercolumn includes (Left) mismatching between two eyes and (Middle) lack of robustness to large viewpoint or (Right) appearance changes across different identities. The proposed feature projection method alleviates these issues.",
"Visualization of landmark matching with cosine distance using 3840-D hypercolumn features and 256-D features projected from hypercolumn. Failure cases of using hypercolumn includes (Left) mismatching between two eyes and (Middle) lack of robustness to large viewpoint or (Right) appearance changes across different identities. The proposed feature projection method alleviates these issues.",
"Visualization of landmark matching with cosine distance using 3840-D hypercolumn features and 256-D features projected from hypercolumn. Failure cases of using hypercolumn includes (Left) mismatching between two eyes and (Middle) lack of robustness to large viewpoint or (Right) appearance changes across different identities. The proposed feature projection method alleviates these issues.",
"Visualization of landmark matching with cosine distance using 3840-D hypercolumn features and 256-D features projected from hypercolumn. Failure cases of using hypercolumn includes (Left) mismatching between two eyes and (Middle) lack of robustness to large viewpoint or (Right) appearance changes across different identities. The proposed feature projection method alleviates these issues.",
"Visualization of landmark matching with cosine distance using 3840-D hypercolumn features and 256-D features projected from hypercolumn. Failure cases of using hypercolumn includes (Left) mismatching between two eyes and (Middle) lack of robustness to large viewpoint or (Right) appearance changes across different identities. The proposed feature projection method alleviates these issues.",
"Visualization of landmark matching with cosine distance using 3840-D hypercolumn features and 256-D features projected from hypercolumn. Failure cases of using hypercolumn includes (Left) mismatching between two eyes and (Middle) lack of robustness to large viewpoint or (Right) appearance changes across different identities. The proposed feature projection method alleviates these issues.",
"Visualization of landmark matching with cosine distance using 3840-D hypercolumn features and 256-D features projected from hypercolumn. Failure cases of using hypercolumn includes (Left) mismatching between two eyes and (Middle) lack of robustness to large viewpoint or (Right) appearance changes across different identities. The proposed feature projection method alleviates these issues.",
"Visualization of landmark matching with cosine distance using 3840-D hypercolumn features and 256-D features projected from hypercolumn. Failure cases of using hypercolumn includes (Left) mismatching between two eyes and (Middle) lack of robustness to large viewpoint or (Right) appearance changes across different identities. The proposed feature projection method alleviates these issues.",
"Visualization of landmark matching with cosine distance using 3840-D hypercolumn features and 256-D features projected from hypercolumn. Failure cases of using hypercolumn includes (Left) mismatching between two eyes and (Middle) lack of robustness to large viewpoint or (Right) appearance changes across different identities. The proposed feature projection method alleviates these issues.",
"Visualization of landmark matching with cosine distance using 3840-D hypercolumn features and 256-D features projected from hypercolumn. Failure cases of using hypercolumn includes (Left) mismatching between two eyes and (Middle) lack of robustness to large viewpoint or (Right) appearance changes across different identities. The proposed feature projection method alleviates these issues.",
"Visualization of landmark matching with cosine distance using 3840-D hypercolumn features and 256-D features projected from hypercolumn. Failure cases of using hypercolumn includes (Left) mismatching between two eyes and (Middle) lack of robustness to large viewpoint or (Right) appearance changes across different identities. The proposed feature projection method alleviates these issues.",
"Visualization of landmark matching with cosine distance using 3840-D hypercolumn features and 256-D features projected from hypercolumn. Failure cases of using hypercolumn includes (Left) mismatching between two eyes and (Middle) lack of robustness to large viewpoint or (Right) appearance changes across different identities. The proposed feature projection method alleviates these issues.",
"Visualization of landmark matching with cosine distance using 3840-D hypercolumn features and 256-D features projected from hypercolumn. Failure cases of using hypercolumn includes (Left) mismatching between two eyes and (Middle) lack of robustness to large viewpoint or (Right) appearance changes across different identities. The proposed feature projection method alleviates these issues.",
"Visualization of landmark matching with cosine distance using 3840-D hypercolumn features and 256-D features projected from hypercolumn. Failure cases of using hypercolumn includes (Left) mismatching between two eyes and (Middle) lack of robustness to large viewpoint or (Right) appearance changes across different identities. The proposed feature projection method alleviates these issues.",
"Visualization of landmark matching with cosine distance using 3840-D hypercolumn features and 256-D features projected from hypercolumn. Failure cases of using hypercolumn includes (Left) mismatching between two eyes and (Middle) lack of robustness to large viewpoint or (Right) appearance changes across different identities. The proposed feature projection method alleviates these issues.",
"Visualization of landmark matching with cosine distance using 3840-D hypercolumn features and 256-D features projected from hypercolumn. Failure cases of using hypercolumn includes (Left) mismatching between two eyes and (Middle) lack of robustness to large viewpoint or (Right) appearance changes across different identities. The proposed feature projection method alleviates these issues.",
"Visualization of landmark matching with cosine distance using 3840-D hypercolumn features and 256-D features projected from hypercolumn. Failure cases of using hypercolumn includes (Left) mismatching between two eyes and (Middle) lack of robustness to large viewpoint or (Right) appearance changes across different identities. The proposed feature projection method alleviates these issues.",
"Visualization of landmark matching with cosine distance using 3840-D hypercolumn features and 256-D features projected from hypercolumn. Failure cases of using hypercolumn includes (Left) mismatching between two eyes and (Middle) lack of robustness to large viewpoint or (Right) appearance changes across different identities. The proposed feature projection method alleviates these issues.",
"Visualization of landmark matching with cosine distance using 3840-D hypercolumn features and 256-D features projected from hypercolumn. Failure cases of using hypercolumn includes (Left) mismatching between two eyes and (Middle) lack of robustness to large viewpoint or (Right) appearance changes across different identities. The proposed feature projection method alleviates these issues.",
"Visualization of landmark matching with cosine distance using 3840-D hypercolumn features and 256-D features projected from hypercolumn. Failure cases of using hypercolumn includes (Left) mismatching between two eyes and (Middle) lack of robustness to large viewpoint or (Right) appearance changes across different identities. The proposed feature projection method alleviates these issues.",
"Visualization of landmark matching with cosine distance using 3840-D hypercolumn features and 256-D features projected from hypercolumn. Failure cases of using hypercolumn includes (Left) mismatching between two eyes and (Middle) lack of robustness to large viewpoint or (Right) appearance changes across different identities. The proposed feature projection method alleviates these issues.",
"Visualization of landmark matching with cosine distance using 3840-D hypercolumn features and 256-D features projected from hypercolumn. Failure cases of using hypercolumn includes (Left) mismatching between two eyes and (Middle) lack of robustness to large viewpoint or (Right) appearance changes across different identities. The proposed feature projection method alleviates these issues.",
"Visualization of landmark matching with cosine distance using 3840-D hypercolumn features and 256-D features projected from hypercolumn. Failure cases of using hypercolumn includes (Left) mismatching between two eyes and (Middle) lack of robustness to large viewpoint or (Right) appearance changes across different identities. The proposed feature projection method alleviates these issues.",
"Visualization of landmark matching with cosine distance using 3840-D hypercolumn features and 256-D features projected from hypercolumn. Failure cases of using hypercolumn includes (Left) mismatching between two eyes and (Middle) lack of robustness to large viewpoint or (Right) appearance changes across different identities. The proposed feature projection method alleviates these issues.",
"Visualization of landmark matching with cosine distance using 3840-D hypercolumn features and 256-D features projected from hypercolumn. Failure cases of using hypercolumn includes (Left) mismatching between two eyes and (Middle) lack of robustness to large viewpoint or (Right) appearance changes across different identities. The proposed feature projection method alleviates these issues.",
"Visualization of landmark matching with cosine distance using 3840-D hypercolumn features and 256-D features projected from hypercolumn. Failure cases of using hypercolumn includes (Left) mismatching between two eyes and (Middle) lack of robustness to large viewpoint or (Right) appearance changes across different identities. The proposed feature projection method alleviates these issues.",
"Visualization of landmark matching with cosine distance using 3840-D hypercolumn features and 256-D features projected from hypercolumn. Failure cases of using hypercolumn includes (Left) mismatching between two eyes and (Middle) lack of robustness to large viewpoint or (Right) appearance changes across different identities. The proposed feature projection method alleviates these issues.",
"Visualization of landmark matching with cosine distance using 3840-D hypercolumn features and 256-D features projected from hypercolumn. Failure cases of using hypercolumn includes (Left) mismatching between two eyes and (Middle) lack of robustness to large viewpoint or (Right) appearance changes across different identities. The proposed feature projection method alleviates these issues.",
"Detected landmarks (a) on faces (blue: predictions, green: ground truth) and (b) on CUB. Notice that our method localizes tails of birds (circled) much better. Zoom in for details.",
"Detected landmarks (a) on faces (blue: predictions, green: ground truth) and (b) on CUB. Notice that our method localizes tails of birds (circled) much better. Zoom in for details.",
"Detected landmarks (a) on faces (blue: predictions, green: ground truth) and (b) on CUB. Notice that our method localizes tails of birds (circled) much better. Zoom in for details.",
"Detected landmarks (a) on faces (blue: predictions, green: ground truth) and (b) on CUB. Notice that our method localizes tails of birds (circled) much better. Zoom in for details.",
"Detected landmarks (a) on faces (blue: predictions, green: ground truth) and (b) on CUB. Notice that our method localizes tails of birds (circled) much better. Zoom in for details.",
"Detected landmarks (a) on faces (blue: predictions, green: ground truth) and (b) on CUB. Notice that our method localizes tails of birds (circled) much better. Zoom in for details.",
"Detected landmarks (a) on faces (blue: predictions, green: ground truth) and (b) on CUB. Notice that our method localizes tails of birds (circled) much better. Zoom in for details.",
"Detected landmarks (a) on faces (blue: predictions, green: ground truth) and (b) on CUB. Notice that our method localizes tails of birds (circled) much better. Zoom in for details.",
"Detected landmarks (a) on faces (blue: predictions, green: ground truth) and (b) on CUB. Notice that our method localizes tails of birds (circled) much better. Zoom in for details.",
"Detected landmarks (a) on faces (blue: predictions, green: ground truth) and (b) on CUB. Notice that our method localizes tails of birds (circled) much better. Zoom in for details.",
"Detected landmarks (a) on faces (blue: predictions, green: ground truth) and (b) on CUB. Notice that our method localizes tails of birds (circled) much better. Zoom in for details.",
"Detected landmarks (a) on faces (blue: predictions, green: ground truth) and (b) on CUB. Notice that our method localizes tails of birds (circled) much better. Zoom in for details.",
"Detected landmarks (a) on faces (blue: predictions, green: ground truth) and (b) on CUB. Notice that our method localizes tails of birds (circled) much better. Zoom in for details.",
"Detected landmarks (a) on faces (blue: predictions, green: ground truth) and (b) on CUB. Notice that our method localizes tails of birds (circled) much better. Zoom in for details.",
"Detected landmarks (a) on faces (blue: predictions, green: ground truth) and (b) on CUB. Notice that our method localizes tails of birds (circled) much better. Zoom in for details.",
"Detected landmarks (a) on faces (blue: predictions, green: ground truth) and (b) on CUB. Notice that our method localizes tails of birds (circled) much better. Zoom in for details.",
"Detected landmarks (a) on faces (blue: predictions, green: ground truth) and (b) on CUB. Notice that our method localizes tails of birds (circled) much better. Zoom in for details.",
"Detected landmarks (a) on faces (blue: predictions, green: ground truth) and (b) on CUB. Notice that our method localizes tails of birds (circled) much better. Zoom in for details.",
"The effect of dataset size. (a) A comparison of our model with DVE [49] by varying the number of annotations for landmark regression on AFLWM dataset. Random-SmallNet†: is a randomly initialized “small network” taken from [49]. Ours-ResNet50: are based on hypercolumn, or its compact representations, or fourth-layer features trained using contrastive learning. (b) Similar results on CUB dataset. Random-ResNet18: is trained from scratch on the CUB dataset. (c) Results of landmark regression on AFLWM using different numbers of unlabeled images from CelebA for training.",
"The effect of dataset size. (a) A comparison of our model with DVE [49] by varying the number of annotations for landmark regression on AFLWM dataset. Random-SmallNet†: is a randomly initialized “small network” taken from [49]. Ours-ResNet50: are based on hypercolumn, or its compact representations, or fourth-layer features trained using contrastive learning. (b) Similar results on CUB dataset. Random-ResNet18: is trained from scratch on the CUB dataset. (c) Results of landmark regression on AFLWM using different numbers of unlabeled images from CelebA for training.",
"The effect of dataset size. (a) A comparison of our model with DVE [49] by varying the number of annotations for landmark regression on AFLWM dataset. Random-SmallNet†: is a randomly initialized “small network” taken from [49]. Ours-ResNet50: are based on hypercolumn, or its compact representations, or fourth-layer features trained using contrastive learning. (b) Similar results on CUB dataset. Random-ResNet18: is trained from scratch on the CUB dataset. (c) Results of landmark regression on AFLWM using different numbers of unlabeled images from CelebA for training.",
"Semantic parts distillation. The object parts distilled from our representation using NMF are semantically meaningful and consistent across different instances (left). The parts are also robust to geometric transformations (right).",
"Semantic parts distillation. The object parts distilled from our representation using NMF are semantically meaningful and consistent across different instances (left). The parts are also robust to geometric transformations (right).",
"Semantic parts distillation. The object parts distilled from our representation using NMF are semantically meaningful and consistent across different instances (left). The parts are also robust to geometric transformations (right).",
"Semantic parts distillation. The object parts distilled from our representation using NMF are semantically meaningful and consistent across different instances (left). The parts are also robust to geometric transformations (right).",
"Semantic parts distillation. The object parts distilled from our representation using NMF are semantically meaningful and consistent across different instances (left). The parts are also robust to geometric transformations (right).",
"Semantic parts distillation. The object parts distilled from our representation using NMF are semantically meaningful and consistent across different instances (left). The parts are also robust to geometric transformations (right).",
"Semantic parts distillation. The object parts distilled from our representation using NMF are semantically meaningful and consistent across different instances (left). The parts are also robust to geometric transformations (right).",
"Semantic parts distillation. The object parts distilled from our representation using NMF are semantically meaningful and consistent across different instances (left). The parts are also robust to geometric transformations (right).",
"Semantic parts distillation. The object parts distilled from our representation using NMF are semantically meaningful and consistent across different instances (left). The parts are also robust to geometric transformations (right).",
"Semantic parts distillation. The object parts distilled from our representation using NMF are semantically meaningful and consistent across different instances (left). The parts are also robust to geometric transformations (right).",
"Semantic parts distillation. The object parts distilled from our representation using NMF are semantically meaningful and consistent across different instances (left). The parts are also robust to geometric transformations (right).",
"Semantic parts distillation. The object parts distilled from our representation using NMF are semantically meaningful and consistent across different instances (left). The parts are also robust to geometric transformations (right).",
"Semantic parts distillation. The object parts distilled from our representation using NMF are semantically meaningful and consistent across different instances (left). The parts are also robust to geometric transformations (right).",
"Semantic parts distillation. The object parts distilled from our representation using NMF are semantically meaningful and consistent across different instances (left). The parts are also robust to geometric transformations (right).",
"Semantic parts distillation. The object parts distilled from our representation using NMF are semantically meaningful and consistent across different instances (left). The parts are also robust to geometric transformations (right).",
"Semantic parts distillation. The object parts distilled from our representation using NMF are semantically meaningful and consistent across different instances (left). The parts are also robust to geometric transformations (right).",
"Semantic parts distillation. The object parts distilled from our representation using NMF are semantically meaningful and consistent across different instances (left). The parts are also robust to geometric transformations (right).",
"Semantic parts distillation. The object parts distilled from our representation using NMF are semantically meaningful and consistent across different instances (left). The parts are also robust to geometric transformations (right).",
"Semantic parts distillation. The object parts distilled from our representation using NMF are semantically meaningful and consistent across different instances (left). The parts are also robust to geometric transformations (right).",
"Semantic parts distillation. The object parts distilled from our representation using NMF are semantically meaningful and consistent across different instances (left). The parts are also robust to geometric transformations (right).",
"Semantic parts distillation. The object parts distilled from our representation using NMF are semantically meaningful and consistent across different instances (left). The parts are also robust to geometric transformations (right).",
"Semantic parts distillation. The object parts distilled from our representation using NMF are semantically meaningful and consistent across different instances (left). The parts are also robust to geometric transformations (right).",
"Semantic parts distillation. The object parts distilled from our representation using NMF are semantically meaningful and consistent across different instances (left). The parts are also robust to geometric transformations (right).",
"Semantic parts distillation. The object parts distilled from our representation using NMF are semantically meaningful and consistent across different instances (left). The parts are also robust to geometric transformations (right).",
"Semantic parts distillation. The object parts distilled from our representation using NMF are semantically meaningful and consistent across different instances (left). The parts are also robust to geometric transformations (right).",
"Semantic parts distillation. The object parts distilled from our representation using NMF are semantically meaningful and consistent across different instances (left). The parts are also robust to geometric transformations (right).",
"Semantic parts distillation. The object parts distilled from our representation using NMF are semantically meaningful and consistent across different instances (left). The parts are also robust to geometric transformations (right).",
"PCA visualization of the hypercolumn representation. From left to right: input image, and the projection of hypercolumns on the first four PCA bases from a contrastively trained and a randomly initialized ResNet50.",
"PCA visualization of the hypercolumn representation. From left to right: input image, and the projection of hypercolumns on the first four PCA bases from a contrastively trained and a randomly initialized ResNet50.",
"PCA visualization of the hypercolumn representation. From left to right: input image, and the projection of hypercolumns on the first four PCA bases from a contrastively trained and a randomly initialized ResNet50.",
"PCA visualization of the hypercolumn representation. From left to right: input image, and the projection of hypercolumns on the first four PCA bases from a contrastively trained and a randomly initialized ResNet50.",
"PCA visualization of the hypercolumn representation. From left to right: input image, and the projection of hypercolumns on the first four PCA bases from a contrastively trained and a randomly initialized ResNet50.",
"PCA visualization of the hypercolumn representation. From left to right: input image, and the projection of hypercolumns on the first four PCA bases from a contrastively trained and a randomly initialized ResNet50.",
"PCA visualization of the hypercolumn representation. From left to right: input image, and the projection of hypercolumns on the first four PCA bases from a contrastively trained and a randomly initialized ResNet50.",
"PCA visualization of the hypercolumn representation. From left to right: input image, and the projection of hypercolumns on the first four PCA bases from a contrastively trained and a randomly initialized ResNet50.",
"PCA visualization of the hypercolumn representation. From left to right: input image, and the projection of hypercolumns on the first four PCA bases from a contrastively trained and a randomly initialized ResNet50.",
"PCA visualization of the hypercolumn representation. From left to right: input image, and the projection of hypercolumns on the first four PCA bases from a contrastively trained and a randomly initialized ResNet50.",
"PCA visualization of the hypercolumn representation. From left to right: input image, and the projection of hypercolumns on the first four PCA bases from a contrastively trained and a randomly initialized ResNet50.",
"PCA visualization of the hypercolumn representation. From left to right: input image, and the projection of hypercolumns on the first four PCA bases from a contrastively trained and a randomly initialized ResNet50.",
"PCA visualization of the hypercolumn representation. From left to right: input image, and the projection of hypercolumns on the first four PCA bases from a contrastively trained and a randomly initialized ResNet50.",
"PCA visualization of the hypercolumn representation. From left to right: input image, and the projection of hypercolumns on the first four PCA bases from a contrastively trained and a randomly initialized ResNet50.",
"PCA visualization of the hypercolumn representation. From left to right: input image, and the projection of hypercolumns on the first four PCA bases from a contrastively trained and a randomly initialized ResNet50.",
"PCA visualization of the hypercolumn representation. From left to right: input image, and the projection of hypercolumns on the first four PCA bases from a contrastively trained and a randomly initialized ResNet50.",
"PCA visualization of the hypercolumn representation. From left to right: input image, and the projection of hypercolumns on the first four PCA bases from a contrastively trained and a randomly initialized ResNet50.",
"PCA visualization of the hypercolumn representation. From left to right: input image, and the projection of hypercolumns on the first four PCA bases from a contrastively trained and a randomly initialized ResNet50.",
"Landmark matching performance as a function of the projection dimension. The mean pixel error of the raw hypercolumn representations is 6.16 (not shown), which is higher than the projected representation across all dimensions.",
"Images from bird datasets. Images in the CUB dataset (bottom) are iconic with birds more frequently in canonical poses and contain a single instance. On the other hand, iNaturalist images (top) are community driven and less curated. Often multiple birds are in a single image and are far away. This makes learning and transfer more challenging.",
"Images from bird datasets. Images in the CUB dataset (bottom) are iconic with birds more frequently in canonical poses and contain a single instance. On the other hand, iNaturalist images (top) are community driven and less curated. Often multiple birds are in a single image and are far away. This makes learning and transfer more challenging.",
"Images from bird datasets. Images in the CUB dataset (bottom) are iconic with birds more frequently in canonical poses and contain a single instance. On the other hand, iNaturalist images (top) are community driven and less curated. Often multiple birds are in a single image and are far away. This makes learning and transfer more challenging.",
"Images from bird datasets. Images in the CUB dataset (bottom) are iconic with birds more frequently in canonical poses and contain a single instance. On the other hand, iNaturalist images (top) are community driven and less curated. Often multiple birds are in a single image and are far away. This makes learning and transfer more challenging.",
"Images from bird datasets. Images in the CUB dataset (bottom) are iconic with birds more frequently in canonical poses and contain a single instance. On the other hand, iNaturalist images (top) are community driven and less curated. Often multiple birds are in a single image and are far away. This makes learning and transfer more challenging.",
"Images from bird datasets. Images in the CUB dataset (bottom) are iconic with birds more frequently in canonical poses and contain a single instance. On the other hand, iNaturalist images (top) are community driven and less curated. Often multiple birds are in a single image and are far away. This makes learning and transfer more challenging.",
"Images from bird datasets. Images in the CUB dataset (bottom) are iconic with birds more frequently in canonical poses and contain a single instance. On the other hand, iNaturalist images (top) are community driven and less curated. Often multiple birds are in a single image and are far away. This makes learning and transfer more challenging.",
"Images from bird datasets. Images in the CUB dataset (bottom) are iconic with birds more frequently in canonical poses and contain a single instance. On the other hand, iNaturalist images (top) are community driven and less curated. Often multiple birds are in a single image and are far away. This makes learning and transfer more challenging.",
"Images from bird datasets. Images in the CUB dataset (bottom) are iconic with birds more frequently in canonical poses and contain a single instance. On the other hand, iNaturalist images (top) are community driven and less curated. Often multiple birds are in a single image and are far away. This makes learning and transfer more challenging.",
"Images from bird datasets. Images in the CUB dataset (bottom) are iconic with birds more frequently in canonical poses and contain a single instance. On the other hand, iNaturalist images (top) are community driven and less curated. Often multiple birds are in a single image and are far away. This makes learning and transfer more challenging.",
"Images from bird datasets. Images in the CUB dataset (bottom) are iconic with birds more frequently in canonical poses and contain a single instance. On the other hand, iNaturalist images (top) are community driven and less curated. Often multiple birds are in a single image and are far away. This makes learning and transfer more challenging.",
"Images from bird datasets. Images in the CUB dataset (bottom) are iconic with birds more frequently in canonical poses and contain a single instance. On the other hand, iNaturalist images (top) are community driven and less curated. Often multiple birds are in a single image and are far away. This makes learning and transfer more challenging.",
"Images from bird datasets. Images in the CUB dataset (bottom) are iconic with birds more frequently in canonical poses and contain a single instance. On the other hand, iNaturalist images (top) are community driven and less curated. Often multiple birds are in a single image and are far away. This makes learning and transfer more challenging.",
"Images from bird datasets. Images in the CUB dataset (bottom) are iconic with birds more frequently in canonical poses and contain a single instance. On the other hand, iNaturalist images (top) are community driven and less curated. Often multiple birds are in a single image and are far away. This makes learning and transfer more challenging.",
"Images from bird datasets. Images in the CUB dataset (bottom) are iconic with birds more frequently in canonical poses and contain a single instance. On the other hand, iNaturalist images (top) are community driven and less curated. Often multiple birds are in a single image and are far away. This makes learning and transfer more challenging.",
"Images from bird datasets. Images in the CUB dataset (bottom) are iconic with birds more frequently in canonical poses and contain a single instance. On the other hand, iNaturalist images (top) are community driven and less curated. Often multiple birds are in a single image and are far away. This makes learning and transfer more challenging.",
"Images from bird datasets. Images in the CUB dataset (bottom) are iconic with birds more frequently in canonical poses and contain a single instance. On the other hand, iNaturalist images (top) are community driven and less curated. Often multiple birds are in a single image and are far away. This makes learning and transfer more challenging.",
"Images from bird datasets. Images in the CUB dataset (bottom) are iconic with birds more frequently in canonical poses and contain a single instance. On the other hand, iNaturalist images (top) are community driven and less curated. Often multiple birds are in a single image and are far away. This makes learning and transfer more challenging.",
"Figure-ground segmentation on CUB dataset with 10 annotated images as training data. We fine-tune the network end-to-end using the hypercolumn representation.",
"Figure-ground segmentation on CUB dataset with 10 annotated images as training data. We fine-tune the network end-to-end using the hypercolumn representation.",
"Figure-ground segmentation on CUB dataset with 10 annotated images as training data. We fine-tune the network end-to-end using the hypercolumn representation.",
"Figure-ground segmentation on CUB dataset with 10 annotated images as training data. We fine-tune the network end-to-end using the hypercolumn representation.",
"Figure-ground segmentation on CUB dataset with 10 annotated images as training data. We fine-tune the network end-to-end using the hypercolumn representation.",
"Figure-ground segmentation on CUB dataset with 10 annotated images as training data. We fine-tune the network end-to-end using the hypercolumn representation.",
"Figure-ground segmentation on CUB dataset with 10 annotated images as training data. We fine-tune the network end-to-end using the hypercolumn representation.",
"Figure-ground segmentation on CUB dataset with 10 annotated images as training data. We fine-tune the network end-to-end using the hypercolumn representation.",
"Figure-ground segmentation on CUB dataset with 10 annotated images as training data. We fine-tune the network end-to-end using the hypercolumn representation.",
"Figure-ground segmentation on CUB dataset with 10 annotated images as training data. We fine-tune the network end-to-end using the hypercolumn representation.",
"Figure-ground segmentation on CUB dataset with 10 annotated images as training data. We fine-tune the network end-to-end using the hypercolumn representation.",
"Figure-ground segmentation on CUB dataset with 10 annotated images as training data. We fine-tune the network end-to-end using the hypercolumn representation.",
"Figure-ground segmentation on CUB dataset with 10 annotated images as training data. We fine-tune the network end-to-end using the hypercolumn representation.",
"Figure-ground segmentation on CUB dataset with 10 annotated images as training data. We fine-tune the network end-to-end using the hypercolumn representation.",
"Figure-ground segmentation on CUB dataset with 10 annotated images as training data. We fine-tune the network end-to-end using the hypercolumn representation.",
"Figure-ground segmentation on CUB dataset with 10 annotated images as training data. We fine-tune the network end-to-end using the hypercolumn representation.",
"Figure-ground segmentation on CUB dataset with 10 annotated images as training data. We fine-tune the network end-to-end using the hypercolumn representation.",
"Figure-ground segmentation on CUB dataset with 10 annotated images as training data. We fine-tune the network end-to-end using the hypercolumn representation."
]
}
|
[
"cs.CV"
] | null | null |
2007.10891
|
Representative-Discriminative Learning for Open-set Land Cover Classification of Satellite Imagery
|
Land cover classification of satellite imagery is an important step toward analyzing the Earth’s surface. Existing models assume a closed-set setting where both the training and testing classes belong to the same label set. However, due to the unique characteristics of satellite imagery with extremely vast area of versatile cover materials, the training data are bound to be non-representative. In this paper, we study the problem of open-set land cover classification that identifies the samples belonging to unknown classes during testing, while maintaining performance on known classes. Although inherently a classification problem, both representative and discriminative aspects of data need to be exploited in order to better distinguish unknown classes from known. We propose a representative-discriminative open-set recognition (RDOSR) framework, which 1) projects data from the raw image space to the embedding feature space that facilitates differentiating similar classes, and further 2) enhances both the representative and discriminative capacity through transformation to a so-called abundance space. Experiments on multiple satellite benchmarks demonstrate effectiveness of the proposed method. We also show the generality of the proposed approach by achieving promising results on open-set classification tasks using RGB images.
| Not supported with pagination yet | null |
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"caption": [
"Representative-discriminative learning through the transformation among 3 spaces: the raw image space, the embedding space, and the abundance space.",
"An overview of the proposed framework: i) Closed-set embedding learning: the classifier <MATH> F </MATH> is trained on the spectral domain <MATH> X </MATH> to produce latent discriminative embedding <MATH> \\mathbf{z_{F}} </MATH> . ii) Representative-discriminative feature learning: the encoder <MATH> E </MATH> takes the embedding feature <MATH> \\mathbf{z_{F}} </MATH> and derives the representative features <MATH> S </MATH> using a Dirichlet-Net. The classifier <MATH> C </MATH> applied on <MATH> S </MATH> enhances the discriminative aspect of <MATH> S </MATH> , and the reconstruction error between the decoder output ( <MATH> \\mathbf{\\hat{z}_{F}} </MATH> ) and input to encoder ( <MATH> \\mathbf{z_{F}} </MATH> ) enhances the representative aspect of <MATH> S </MATH> .",
"The flowchart of the multi-task representative-discriminative feature learning framework.",
"Receiver Operating Curve curves for open-set recognition for PU and PC datasets, for <MATH> L=7 </MATH> (openness= <MATH> 6.46\\% </MATH> ). Open-set detection on PU dataset",
"Receiver Operating Curve curves for open-set recognition for PU and PC datasets, for <MATH> L=7 </MATH> (openness= <MATH> 6.46\\% </MATH> ). Open-set detection on PC dataset",
"Reconstruction error distribution of known and unknown classes using the proposed method for PU and PC datasets, for <MATH> L=8 </MATH> . PU dataset",
"Reconstruction error distribution of known and unknown classes using the proposed method for PU and PC datasets, for <MATH> L=8 </MATH> . PC dataset",
"Ablation study of the proposed method on PU dataset",
"Mean of samples in space <MATH> X </MATH> , belonging to classes 1 to 9, using the PU dataset",
"Mean of samples in space <MATH> Z_{f} </MATH> , belonging to classes 1 to 9, using the PU dataset",
"Mean of samples in space <MATH> Z_{f} </MATH> , belonging to classes 1 to 9, using the PU dataset",
"Mean of samples in space <MATH> Z_{f} </MATH> , belonging to classes 1 to 9, using the PU dataset",
"Mean of samples in space <MATH> Z_{f} </MATH> , belonging to classes 1 to 9, using the PU dataset",
"Mean of samples in space <MATH> Z_{f} </MATH> , belonging to classes 1 to 9, using the PU dataset",
"Mean of samples in space <MATH> Z_{f} </MATH> , belonging to classes 1 to 9, using the PU dataset",
"Mean of samples in space <MATH> Z_{f} </MATH> , belonging to classes 1 to 9, using the PU dataset",
"Mean of samples in space <MATH> Z_{f} </MATH> , belonging to classes 1 to 9, using the PU dataset",
"Mean of samples in space <MATH> Z_{f} </MATH> , belonging to classes 1 to 9, using the PU dataset",
"Mean of samples in space <MATH> S </MATH> , belonging to classes 1 to 9, using the PU dataset",
"Mean of samples in space <MATH> S </MATH> , belonging to classes 1 to 9, using the PU dataset",
"Mean of samples in space <MATH> S </MATH> , belonging to classes 1 to 9, using the PU dataset",
"Mean of samples in space <MATH> S </MATH> , belonging to classes 1 to 9, using the PU dataset",
"Mean of samples in space <MATH> S </MATH> , belonging to classes 1 to 9, using the PU dataset",
"Mean of samples in space <MATH> S </MATH> , belonging to classes 1 to 9, using the PU dataset",
"Mean of samples in space <MATH> S </MATH> , belonging to classes 1 to 9, using the PU dataset",
"Mean of samples in space <MATH> S </MATH> , belonging to classes 1 to 9, using the PU dataset",
"Mean of samples in space <MATH> S </MATH> , belonging to classes 1 to 9, using the PU dataset"
]
}
|
[
"cs.CV"
] | null |
ECCV
|
2009.08511
|
Smartphone Camera De-identification while Preserving Biometric Utility
|
The principle of Photo Response Non Uniformity (PRNU) is often exploited to deduce the identity of the smartphone device whose camera or sensor was used to acquire a certain image. In this work, we design an algorithm that perturbs a face image acquired using a smartphone camera such that (a) sensor-specific details pertaining to the smartphone camera are suppressed (sensor anonymization); (b) the sensor pattern of a different device is incorporated (sensor spoofing); and (c) biometric matching using the perturbed image is not affected (biometric utility). We employ a simple approach utilizing Discrete Cosine Transform to achieve the aforementioned objectives. Experiments conducted on the MICHE-I and OULU-NPU datasets, which contain periocular and facial data acquired using 12 smartphone cameras, demonstrate the efficacy of the proposed de-identification algorithm on three different PRNU-based sensor identification schemes. This work has application in sensor forensics and personal privacy.
| Not supported with pagination yet | null |
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"caption": [
"Illustration of PRNU Anonymization. The DCT coefficients are arranged such that the top-left portion has the low frequency components while the bottom-right portion encapsulates the high frequency information. The PRNU anonymized image is the result of suppression of high frequency components (see Algorithm 1, here <MATH> \\eta=0.9 </MATH> ).",
"Illustration of PRNU Spoofing. The high frequency components in the original image are suppressed first, the residue being the low frequency components. The high frequency components of the target sensor are further computed from the candidate images, and added to the low frequency components of the original image, resulting in the PRNU spoofed image (see Algorithm 2, here <MATH> \\eta=0.7 </MATH> ).",
"Example images from the MICHE-I and the OULU-NPU datasets acquired using (a) Apple iPhone 5 Rear, (b) Samsung Galaxy S4 Front, (c) Samsung Galaxy S6 Edge Front, (d) HTC Desire EYE Front, (e) MEIZU X5 Front, (f) ASUS Zenfone Selfie Front, (g) Sony XPERIA C5 Ultra Dual Front and (h) OPPO N3 Front sensors.",
"Example images from the MICHE-I and the OULU-NPU datasets acquired using (a) Apple iPhone 5 Rear, (b) Samsung Galaxy S4 Front, (c) Samsung Galaxy S6 Edge Front, (d) HTC Desire EYE Front, (e) MEIZU X5 Front, (f) ASUS Zenfone Selfie Front, (g) Sony XPERIA C5 Ultra Dual Front and (h) OPPO N3 Front sensors.",
"Example images from the MICHE-I and the OULU-NPU datasets acquired using (a) Apple iPhone 5 Rear, (b) Samsung Galaxy S4 Front, (c) Samsung Galaxy S6 Edge Front, (d) HTC Desire EYE Front, (e) MEIZU X5 Front, (f) ASUS Zenfone Selfie Front, (g) Sony XPERIA C5 Ultra Dual Front and (h) OPPO N3 Front sensors.",
"Example images from the MICHE-I and the OULU-NPU datasets acquired using (a) Apple iPhone 5 Rear, (b) Samsung Galaxy S4 Front, (c) Samsung Galaxy S6 Edge Front, (d) HTC Desire EYE Front, (e) MEIZU X5 Front, (f) ASUS Zenfone Selfie Front, (g) Sony XPERIA C5 Ultra Dual Front and (h) OPPO N3 Front sensors.",
"Example images from the MICHE-I and the OULU-NPU datasets acquired using (a) Apple iPhone 5 Rear, (b) Samsung Galaxy S4 Front, (c) Samsung Galaxy S6 Edge Front, (d) HTC Desire EYE Front, (e) MEIZU X5 Front, (f) ASUS Zenfone Selfie Front, (g) Sony XPERIA C5 Ultra Dual Front and (h) OPPO N3 Front sensors.",
"Example images from the MICHE-I and the OULU-NPU datasets acquired using (a) Apple iPhone 5 Rear, (b) Samsung Galaxy S4 Front, (c) Samsung Galaxy S6 Edge Front, (d) HTC Desire EYE Front, (e) MEIZU X5 Front, (f) ASUS Zenfone Selfie Front, (g) Sony XPERIA C5 Ultra Dual Front and (h) OPPO N3 Front sensors.",
"Example images from the MICHE-I and the OULU-NPU datasets acquired using (a) Apple iPhone 5 Rear, (b) Samsung Galaxy S4 Front, (c) Samsung Galaxy S6 Edge Front, (d) HTC Desire EYE Front, (e) MEIZU X5 Front, (f) ASUS Zenfone Selfie Front, (g) Sony XPERIA C5 Ultra Dual Front and (h) OPPO N3 Front sensors.",
"Example images from the MICHE-I and the OULU-NPU datasets acquired using (a) Apple iPhone 5 Rear, (b) Samsung Galaxy S4 Front, (c) Samsung Galaxy S6 Edge Front, (d) HTC Desire EYE Front, (e) MEIZU X5 Front, (f) ASUS Zenfone Selfie Front, (g) Sony XPERIA C5 Ultra Dual Front and (h) OPPO N3 Front sensors.",
"ROC curves for matching PRNU Anonymized images. Each row corresponds to a different device identifier: (a) Device 1 UNIT I, (b) Device 1 UNIT II and (c) Device 2 UNIT I.",
"ROC curves for matching PRNU Anonymized images. Each row corresponds to a different device identifier: (a) Device 1 UNIT I, (b) Device 1 UNIT II and (c) Device 2 UNIT I.",
"ROC curves for matching PRNU Anonymized images. Each row corresponds to a different device identifier: (a) Device 1 UNIT I, (b) Device 1 UNIT II and (c) Device 2 UNIT I.",
"ROC curves for matching PRNU Anonymized images. Each row corresponds to a different device identifier: (a) Device 1 UNIT I, (b) Device 1 UNIT II and (c) Device 2 UNIT I.",
"ROC curves for matching PRNU Anonymized images. Each row corresponds to a different device identifier: (a) Device 1 UNIT I, (b) Device 1 UNIT II and (c) Device 2 UNIT I.",
"ROC curves for matching PRNU Anonymized images. Each row corresponds to a different device identifier: (a) Device 1 UNIT I, (b) Device 1 UNIT II and (c) Device 2 UNIT I.",
"ROC curves for matching PRNU Anonymized images. Each row corresponds to a different device identifier: (a) Device 1 UNIT I, (b) Device 1 UNIT II and (c) Device 2 UNIT I.",
"ROC curves for matching PRNU Anonymized images. Each row corresponds to a different device identifier: (a) Device 1 UNIT I, (b) Device 1 UNIT II and (c) Device 2 UNIT I.",
"ROC curves for matching PRNU Anonymized images. Each row corresponds to a different device identifier: (a) Device 1 UNIT I, (b) Device 1 UNIT II and (c) Device 2 UNIT I.",
"ROC curves for matching PRNU Anonymized images. Each row corresponds to a different device identifier: (a) Device 1 UNIT I, (b) Device 1 UNIT II and (c) Device 2 UNIT I.",
"ROC curves for matching PRNU Anonymized images. Each row corresponds to a different device identifier: (a) Device 1 UNIT I, (b) Device 1 UNIT II and (c) Device 2 UNIT I.",
"ROC curves for matching PRNU Anonymized images. Each row corresponds to a different device identifier: (a) Device 1 UNIT I, (b) Device 1 UNIT II and (c) Device 2 UNIT I.",
"ROC curves for matching PRNU Spoofed images. Here, the source sensor is Device 1 UNIT I. In this case, the target sensors are: (a) Device 1 UNIT II (top row) and (b) Device 2 UNIT I (bottom row).",
"ROC curves for matching PRNU Spoofed images. Here, the source sensor is Device 1 UNIT I. In this case, the target sensors are: (a) Device 1 UNIT II (top row) and (b) Device 2 UNIT I (bottom row).",
"ROC curves for matching PRNU Spoofed images. Here, the source sensor is Device 1 UNIT I. In this case, the target sensors are: (a) Device 1 UNIT II (top row) and (b) Device 2 UNIT I (bottom row).",
"ROC curves for matching PRNU Spoofed images. Here, the source sensor is Device 1 UNIT I. In this case, the target sensors are: (a) Device 1 UNIT II (top row) and (b) Device 2 UNIT I (bottom row).",
"ROC curves for matching PRNU Spoofed images. Here, the source sensor is Device 1 UNIT I. In this case, the target sensors are: (a) Device 1 UNIT II (top row) and (b) Device 2 UNIT I (bottom row).",
"ROC curves for matching PRNU Spoofed images. Here, the source sensor is Device 1 UNIT I. In this case, the target sensors are: (a) Device 1 UNIT II (top row) and (b) Device 2 UNIT I (bottom row).",
"ROC curves for matching PRNU Spoofed images. Here, the source sensor is Device 1 UNIT I. In this case, the target sensors are: (a) Device 1 UNIT II (top row) and (b) Device 2 UNIT I (bottom row).",
"ROC curves for matching PRNU Spoofed images. Here, the source sensor is Device 1 UNIT I. In this case, the target sensors are: (a) Device 1 UNIT II (top row) and (b) Device 2 UNIT I (bottom row).",
"ROC curves for matching PRNU Spoofed images. Here, the source sensor is Device 1 UNIT II. In this case, the target sensors are: (a) Device 1 UNIT I (top row) and (b) Device 2 UNIT I (bottom row).",
"ROC curves for matching PRNU Spoofed images. Here, the source sensor is Device 1 UNIT II. In this case, the target sensors are: (a) Device 1 UNIT I (top row) and (b) Device 2 UNIT I (bottom row).",
"ROC curves for matching PRNU Spoofed images. Here, the source sensor is Device 1 UNIT II. In this case, the target sensors are: (a) Device 1 UNIT I (top row) and (b) Device 2 UNIT I (bottom row).",
"ROC curves for matching PRNU Spoofed images. Here, the source sensor is Device 1 UNIT II. In this case, the target sensors are: (a) Device 1 UNIT I (top row) and (b) Device 2 UNIT I (bottom row).",
"ROC curves for matching PRNU Spoofed images. Here, the source sensor is Device 1 UNIT II. In this case, the target sensors are: (a) Device 1 UNIT I (top row) and (b) Device 2 UNIT I (bottom row).",
"ROC curves for matching PRNU Spoofed images. Here, the source sensor is Device 1 UNIT II. In this case, the target sensors are: (a) Device 1 UNIT I (top row) and (b) Device 2 UNIT I (bottom row).",
"ROC curves for matching PRNU Spoofed images. Here, the source sensor is Device 1 UNIT II. In this case, the target sensors are: (a) Device 1 UNIT I (top row) and (b) Device 2 UNIT I (bottom row).",
"ROC curves for matching PRNU Spoofed images. Here, the source sensor is Device 1 UNIT II. In this case, the target sensors are: (a) Device 1 UNIT I (top row) and (b) Device 2 UNIT I (bottom row).",
"ROC curves for matching PRNU Spoofed images. Here, the source sensor is Device 2 UNIT I. In this case, the target sensors are: (a) Device 1 UNIT I (top row) and (b) Device 1 UNIT II (bottom row).",
"ROC curves for matching PRNU Spoofed images. Here, the source sensor is Device 2 UNIT I. In this case, the target sensors are: (a) Device 1 UNIT I (top row) and (b) Device 1 UNIT II (bottom row).",
"ROC curves for matching PRNU Spoofed images. Here, the source sensor is Device 2 UNIT I. In this case, the target sensors are: (a) Device 1 UNIT I (top row) and (b) Device 1 UNIT II (bottom row).",
"ROC curves for matching PRNU Spoofed images. Here, the source sensor is Device 2 UNIT I. In this case, the target sensors are: (a) Device 1 UNIT I (top row) and (b) Device 1 UNIT II (bottom row).",
"ROC curves for matching PRNU Spoofed images. Here, the source sensor is Device 2 UNIT I. In this case, the target sensors are: (a) Device 1 UNIT I (top row) and (b) Device 1 UNIT II (bottom row).",
"ROC curves for matching PRNU Spoofed images. Here, the source sensor is Device 2 UNIT I. In this case, the target sensors are: (a) Device 1 UNIT I (top row) and (b) Device 1 UNIT II (bottom row).",
"ROC curves for matching PRNU Spoofed images. Here, the source sensor is Device 2 UNIT I. In this case, the target sensors are: (a) Device 1 UNIT I (top row) and (b) Device 1 UNIT II (bottom row).",
"ROC curves for matching PRNU Spoofed images. Here, the source sensor is Device 2 UNIT I. In this case, the target sensors are: (a) Device 1 UNIT I (top row) and (b) Device 1 UNIT II (bottom row)."
]
}
|
[
"cs.CV",
"eess.IV"
] |
Proc. of 10th IEEE International Conference on Biometrics: Theory,
Applications and Systems (BTAS), (Tampa, USA), September 2019
| null |
2009.14719
|
Between Shapes, Using the Hausdorff Distance
|
Given two shapes <MATH> A </MATH> and <MATH> B </MATH> in the plane with Hausdorff distance <MATH> 1 </MATH> , is there a shape <MATH> S </MATH> with Hausdorff distance <MATH> 1/2 </MATH> to and from <MATH> A </MATH> and <MATH> B </MATH> ? The answer is always yes, and depending on convexity of <MATH> A </MATH> and/or <MATH> B </MATH> , <MATH> S </MATH> may be convex, connected, or disconnected. We show that our result can be generalised to give an interpolated shape between <MATH> A </MATH> and <MATH> B </MATH> for any interpolation variable <MATH> \alpha </MATH> between 0 and 1, and prove that the resulting morph has a bounded rate of change with respect to <MATH> \alpha </MATH> . Finally, we explore a generalization of the concept of a Hausdorff middle to more than two input sets. We show how to approximate or compute this middle shape, and that the properties relating to the connectedness of the Hausdorff middle extend from the case with two input sets. We also give bounds on the Hausdorff distance between the middle set and the input.
| Not supported with pagination yet | null |
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"caption": [
"Three possible Hausdorff middles of <MATH> A </MATH> and <MATH> B </MATH> : two points, a line segment, and <MATH> S_{1/2} </MATH> .",
"Two different minimal sets achieving minimal Hausdorff distance to <MATH> A </MATH> and <MATH> B </MATH> . Both the two green dots in Figure (b) and the three green dots in Figure (c) minimise the Hausdorff distance to <MATH> A </MATH> and <MATH> B </MATH> .",
"An arbitrary point <MATH> a\\in A </MATH> with its closest point <MATH> b </MATH> on <MATH> B </MATH> . The point <MATH> s </MATH> has distance at most <MATH> \\alpha </MATH> to <MATH> a </MATH> , and distance at most <MATH> 1-\\alpha </MATH> to <MATH> b </MATH> .",
"Sets <MATH> A </MATH> and <MATH> B </MATH> for which <MATH> S_{1/2} </MATH> is disconnected. The shaded areas around <MATH> A </MATH> and <MATH> B </MATH> represent <MATH> A\\oplus D_{1/2} </MATH> and <MATH> B\\oplus D_{1/2} </MATH> , respectively.",
"Illustration of the proof showing that <MATH> S_{\\alpha} </MATH> is connected if <MATH> A </MATH> is convex (sketched for <MATH> \\alpha=3/4 </MATH> ). The shaded areas around <MATH> A </MATH> and <MATH> B </MATH> represent <MATH> A\\oplus D_{3/4} </MATH> and <MATH> B\\oplus D_{1/4} </MATH> , respectively, so that the doubly-shaded area is <MATH> S_{3/4} </MATH> .",
"Although <MATH> B_{2} </MATH> is a translate of <MATH> B_{1} </MATH> , the middle set between <MATH> A </MATH> and <MATH> B_{2} </MATH> is not a translate of the middle set between <MATH> A </MATH> and <MATH> B_{1} </MATH> .",
"When <MATH> \\alpha\\geq 1-\\alpha </MATH> , an arc <MATH> b </MATH> of <MATH> \\partial B^{\\oplus} </MATH> (blue) can only intersect <MATH> \\partial A^{\\oplus} </MATH> (red) twice.",
"When <MATH> \\alpha<1-\\alpha </MATH> , a single arc <MATH> b </MATH> of <MATH> \\partial B^{\\oplus} </MATH> , shown in blue, can have many intersections with <MATH> \\partial A^{\\oplus} </MATH> , but no other arc <MATH> b^{\\prime} </MATH> , shown as a dashed blue arc, can have many intersections with the same part of <MATH> \\partial A^{\\oplus} </MATH> . The intersections of <MATH> b </MATH> with <MATH> \\partial A^{\\oplus} </MATH> are shown in red.",
"Some examples of morphs <MATH> S_{\\alpha} </MATH> between two shapes <MATH> A </MATH> and <MATH> B </MATH> .",
"Figures (a) and (b) show the offsets of <MATH> A </MATH> , respectively <MATH> B </MATH> with distance <MATH> 1/2 </MATH> . Figure (c) shows the resulting <MATH> S_{1/2} </MATH> in green. Any connected shape must cross the vertical middle line or stay on one side of it. In both cases, the Hausdorff distance doubles.",
"The pairwise Hausdorff distance in this construction is <MATH> 1 </MATH> , and for any <MATH> \\alpha<1 </MATH> , <MATH> T_{\\alpha}^{\\oplus} </MATH> does not contain point <MATH> p </MATH> .",
"Three segments <MATH> A_{1} </MATH> , <MATH> A_{2} </MATH> , and <MATH> A_{3} </MATH> . Of these, <MATH> A_{3} </MATH> is the diameter of a circle with radius <MATH> r </MATH> ; the other two ( <MATH> A_{1} </MATH> and <MATH> A_{2} </MATH> ) are tangent to the circle and are copies of one another reflected through <MATH> A_{3} </MATH> , such that all pairwise Hausdorff distances are at most <MATH> 1 </MATH> (length of dashed segments). The top left vertex of <MATH> A_{3} </MATH> is furthest (at distance <MATH> r </MATH> ) from the middle set <MATH> T_{r} </MATH> (green), so <MATH> \\alpha(\\{A_{1},A_{2},A_{3}\\}) </MATH> is the radius <MATH> r </MATH> of the circle.",
"Derivation of the expression for <MATH> z </MATH> .",
"When the input sets are not convex, all sets may be necessary to realise the value of <MATH> \\alpha </MATH> . Figure 15(a) shows our input construction, along with the radius of the circle and the Hausdorff distance. Figure 15(b) shows that when all sets are present, the required value of <MATH> \\alpha </MATH> is <MATH> (1+\\varepsilon)/2 </MATH> . Figure 15(c) shows that with the red set removed, the required value of <MATH> \\alpha </MATH> is reduced to <MATH> (1+\\varepsilon/2)/2 </MATH> .",
"When the input sets are not convex, all sets may be necessary to realise the value of <MATH> \\alpha </MATH> . Figure 15(a) shows our input construction, along with the radius of the circle and the Hausdorff distance. Figure 15(b) shows that when all sets are present, the required value of <MATH> \\alpha </MATH> is <MATH> (1+\\varepsilon)/2 </MATH> . Figure 15(c) shows that with the red set removed, the required value of <MATH> \\alpha </MATH> is reduced to <MATH> (1+\\varepsilon/2)/2 </MATH> .",
"When the input sets are not convex, all sets may be necessary to realise the value of <MATH> \\alpha </MATH> . Figure 15(a) shows our input construction, along with the radius of the circle and the Hausdorff distance. Figure 15(b) shows that when all sets are present, the required value of <MATH> \\alpha </MATH> is <MATH> (1+\\varepsilon)/2 </MATH> . Figure 15(c) shows that with the red set removed, the required value of <MATH> \\alpha </MATH> is reduced to <MATH> (1+\\varepsilon/2)/2 </MATH> .",
"Left, two sets shown by red and blue line segments, and the construction of <MATH> T_{\\alpha} </MATH> from lines parallel to edges of <MATH> \\mathcal{M} </MATH> and circles centered at vertices of <MATH> \\mathcal{M} </MATH> . Right, construction of <MATH> T_{\\alpha}^{\\oplus} </MATH> from lines at distance <MATH> 2\\alpha </MATH> from edges of <MATH> \\mathcal{M} </MATH> , circles of radius <MATH> 2\\alpha </MATH> centered at vertices of <MATH> \\mathcal{M} </MATH> , and circles of radius <MATH> \\alpha </MATH> centered at certain vertices of <MATH> T_{\\alpha} </MATH> ."
]
}
|
[
"cs.CG"
] | null | null |
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