Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: GLCM-based chi-square histogram distance for automatic detection of
  defects on patterned textures   Chi-square histogram distance is one of the distance measures that can be
used to find dissimilarity between two histograms. Motivated by the fact that
texture discrimination by human vision system is based on second-order
statistics, we make use of histogram of gray-level co-occurrence matrix (GLCM)
that is based on second-order statistics and propose a new machine vision
algorithm for automatic defect detection on patterned textures. Input defective
images are split into several periodic blocks and GLCMs are computed after
quantizing the gray levels from 0-255 to 0-63 to keep the size of GLCM compact
and to reduce computation time. Dissimilarity matrix derived from chi-square
distances of the GLCMs is subjected to hierarchical clustering to automatically
identify defective and defect-free blocks. Effectiveness of the proposed method
is demonstrated through experiments on defective real-fabric images of 2 major
wallpaper groups (pmm and p4m groups).

11
The categories of this paper are: hep-lat
23
The categories of this paper are: solv-int
14
The categories of this paper are: math-ph
30
The categories of this paper are: q-alg
6
The categories of this paper are: astro-ph
8
The categories of this paper are: hep-ex
19
The categories of this paper are: eess
13
The categories of this paper are: quant-ph
7
The categories of this paper are: hep-th
5
The categories of this paper are: gr-qc
35
The categories of this paper are: mtrl-th
36
The categories of this paper are: cmp-lg
12
The categories of this paper are: nucl-th
28
The categories of this paper are: alg-geom
20
The categories of this paper are: acc-phys
32
The categories of this paper are: atom-ph