Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: PT-ResNet: Perspective Transformation-Based Residual Network for
  Semantic Road Image Segmentation   Semantic road region segmentation is a high-level task, which paves the way
towards road scene understanding. This paper presents a residual network
trained for semantic road segmentation. Firstly, we represent the projections
of road disparities in the v-disparity map as a linear model, which can be
estimated by optimizing the v-disparity map using dynamic programming. This
linear model is then utilized to reduce the redundant information in the left
and right road images. The right image is also transformed into the left
perspective view, which greatly enhances the road surface similarity between
the two images. Finally, the processed stereo images and their disparity maps
are concatenated to create a set of 3D images, which are then utilized to train
our neural network. The experimental results illustrate that our network
achieves a maximum F1-measure of approximately 91.19% when analyzing the images
from the KITTI road dataset.

28
The categories of this paper are: alg-geom
37
The categories of this paper are: supr-con
24
The categories of this paper are: dg-ga
14
The categories of this paper are: math-ph
18
The categories of this paper are: econ
9
The categories of this paper are: nlin
11
The categories of this paper are: hep-lat
16
The categories of this paper are: stat
21
The categories of this paper are: adap-org
0
The categories of this paper are: hep-ph
5
The categories of this paper are: gr-qc
15
The categories of this paper are: nucl-ex
13
The categories of this paper are: quant-ph
12
The categories of this paper are: nucl-th
23
The categories of this paper are: solv-int
25
The categories of this paper are: patt-sol