Cellular automata in pattern recognition
Information Sciences: an International Journal
On-Line and Off-Line Handwriting Recognition: A Comprehensive Survey
IEEE Transactions on Pattern Analysis and Machine Intelligence
Illustrating evolutionary computation with Mathematica
Illustrating evolutionary computation with Mathematica
Cellular Automata-Based Recursive Pseudoexhaustive Test Pattern Generator
IEEE Transactions on Computers
A new kind of science
IEEE Transactions on Pattern Analysis and Machine Intelligence
Theory and application of cellular automata for pattern classification
Fundamenta Informaticae - Special issue on cellular automata
Handwritten digit classification using higher order singular value decomposition
Pattern Recognition
Simulation of forest fire fronts using cellular automata
Advances in Engineering Software
A trainable feature extractor for handwritten digit recognition
Pattern Recognition
Parallel evolutionary modelling of geological processes
Parallel Computing
On-line handwritten digit recognition based on trajectory and velocity modeling
Pattern Recognition Letters
Non-uniform cellular automata based associative memory: Evolutionary design and basins of attraction
Information Sciences: an International Journal
Evolutionary Cellular Automata Based-Approach for Edge Detection
WILF '07 Proceedings of the 7th international workshop on Fuzzy Logic and Applications: Applications of Fuzzy Sets Theory
On solving edge detection by emergence
IEA/AIE'06 Proceedings of the 19th international conference on Advances in Applied Artificial Intelligence: industrial, Engineering and Other Applications of Applied Intelligent Systems
Training cellular automata for image processing
SCIA'05 Proceedings of the 14th Scandinavian conference on Image Analysis
IEEE Transactions on Evolutionary Computation
Learning cellular automata rules for binary classification problem
The Journal of Supercomputing
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One of the contexts in which cellular automata have clearly demonstrated their effectiveness has been in problems involving strong and explicit spatial constraints, as happens in pattern formation and growth. By analogy, attempts to use cellular automata in pattern recognition have also been used in the literature and some progress has been made. However, in general, they still represent more of an unfulfilled promise, due to the lack of a recognition model which cellular automata would naturally fit in, the lack of effective ways to implement it, and the lack of generality of the available approaches. Here, experimental results are reported in the direction of using cellular automata in the task of handwritten digit recognition, in which an evolutionary algorithm searches for two-dimensional cellular automata rules that would transform a given digit image into a match, as close as possible, to a prototype image of that family, so that, the closer the match, the better the recognition of the input image. Although the results reported might still fall shorter than consolidated commercial techniques for the task, the approach presented is quite attractive in terms of the efficacy level it allowed to achieve, and because of its simplicity, which suggests a potential generality from the perspective of its use in other domains.