Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: A Cellular Automata based Optimal Edge Detection Technique using
  Twenty-Five Neighborhood Model   Cellular Automata (CA) are common and most simple models of parallel
computations. Edge detection is one of the crucial task in image processing,
especially in processing biological and medical images. CA can be successfully
applied in image processing. This paper presents a new method for edge
detection of binary images based on two dimensional twenty five neighborhood
cellular automata. The method considers only linear rules of CA for extraction
of edges under null boundary condition. The performance of this approach is
compared with some existing edge detection techniques. This comparison shows
that the proposed method to be very promising for edge detection of binary
images. All the algorithms and results used in this paper are prepared in
MATLAB.

0
The categories of this paper are: hep-ph
14
The categories of this paper are: math-ph
21
The categories of this paper are: adap-org
31
The categories of this paper are: ao-sci
30
The categories of this paper are: q-alg
22
The categories of this paper are: chao-dyn
8
The categories of this paper are: hep-ex
7
The categories of this paper are: hep-th
37
The categories of this paper are: supr-con
2
The categories of this paper are: math
26
The categories of this paper are: bayes-an
15
The categories of this paper are: nucl-ex
24
The categories of this paper are: dg-ga
4
The categories of this paper are: cond-mat
17
The categories of this paper are: q-fin
6
The categories of this paper are: astro-ph