An Efficient Edge Detection Technique by Two Dimensional Rectangular Cellular Automata
This work addresses image processing for computer vision applications, but it appears incremental as it builds on known cellular automata methods.
The paper tackles edge detection in images by proposing a new pattern of two-dimensional rectangular cellular automata linear rules, achieving efficient results with an enhancement of edges compared to existing algorithms.
This paper proposes a new pattern of two dimensional cellular automata linear rules that are used for efficient edge detection of an image. Since cellular automata is inherently parallel in nature, it has produced desired output within a unit time interval. We have observed four linear rules among 512 total linear rules of a rectangular cellular automata in adiabatic or reflexive boundary condition that produces an optimal result. These four rules are directly applied once to the images and produced edge detected output. We compare our results with the existing edge detection algorithms and found that our results shows better edge detection with an enhancement of edges.