Cellular Automata for Pattern Recognition

Cellular Automata Architectures for Pattern Recognition

Expert Systems ◽

10.1016/b978-012443880-4/50068-5 ◽

2002 ◽

pp. 875-907

Author(s):

P TZIONAS ◽

I ANDREADIS

Keyword(s):

Pattern Recognition ◽

Cellular Automata

Memristor cellular automata for image pattern recognition and clinical applications

2016 IEEE International Symposium on Circuits and Systems (ISCAS) ◽

10.1109/iscas.2016.7527506 ◽

2016 ◽

Cited By ~ 4

Author(s):

Jacopo Secco ◽

Marco Farina ◽

Danilo Demarchi ◽

Fernando Corinto ◽

Marco Gilli

Keyword(s):

Pattern Recognition ◽

Cellular Automata ◽

Clinical Applications ◽

Image Pattern

CHARACTERIZING THE ABILITY OF PARALLEL ARRAY GENERATORS ON REVERSIBLE PARTITIONED CELLULAR AUTOMATA

International Journal of Pattern Recognition and Artificial Intelligence ◽

10.1142/s0218001499000318 ◽

1999 ◽

Vol 13 (04) ◽

pp. 523-538 ◽

Cited By ~ 3

Author(s):

KENICHI MORITA ◽

SATOSHI UENO ◽

KATSUNOBU IMAI

Keyword(s):

Pattern Recognition ◽

Cellular Automata ◽

Cellular Automaton ◽

Inverse System ◽

Two Dimensional ◽

Turing Machines ◽

Recognition Mechanism ◽

Parallel Array ◽

Parallel Pattern ◽

Monotonic Constraint

A PCAAG introduced by Morita and Ueno is a parallel array generator on a partitioned cellular automaton (PCA) that generates an array language (i.e. a set of symbol arrays). A "reversible" PCAAG (RPCAAG) is a backward deterministic PCAAG, and thus parsing of two-dimensional patterns can be performed without backtracking by an "inverse" system of the RPCAAG. Hence, a parallel pattern recognition mechanism on a deterministic cellular automaton can be directly obtained from a RPCAAG that generates the pattern set. In this paper, we investigate the generating ability of RPCAAGs and their subclass. It is shown that the ability of RPCAAGs is characterized by two-dimensional deterministic Turing machines, i.e. they are universal in their generating ability. We then investigate a monotonic RPCAAG (MRPCAAG), which is a special type of an RPCAAG that satisfies monotonic constraint. We show that the generating ability of MRPCAAGs is exactly characterized by two-dimensional deterministic linear-bounded automata.

Characterization of Non-linear Cellular Automata Model for Pattern Recognition

Advances in Soft Computing — AFSS 2002 - Lecture Notes in Computer Science ◽

10.1007/3-540-45631-7_29 ◽

2002 ◽

pp. 214-220 ◽

Cited By ~ 4

Author(s):

Niloy Ganguly ◽

Pradipta Maji ◽

Arijit Das ◽

Biplab K. Sikdar ◽

P. Pal Chaudhuri

Keyword(s):

Pattern Recognition ◽

Cellular Automata ◽

Cellular Automata Model ◽

Non Linear

Associative memories based on cellular automata: an application to pattern recognition

Applied Mathematical Sciences ◽

10.12988/ams.2013.13077 ◽

2013 ◽

Vol 7 ◽

pp. 857-866 ◽

Cited By ~ 2

Author(s):

B. Luna-Benoso ◽

R. Flores-Carapia ◽

C. Yanez-Marquez

Keyword(s):

Pattern Recognition ◽

Cellular Automata ◽

Associative Memories

Evolving cellular automata model for pattern recognition and classification

IEEE International Conference on Systems, Man and Cybernetics ◽

10.1109/icsmc.2002.1167958 ◽

2005 ◽

Cited By ~ 1

Author(s):

S. Saha ◽

P. Maji ◽

N. Ganguly ◽

B.K. Sikdar ◽

P.P. Chaudhuri

Keyword(s):

Pattern Recognition ◽

Cellular Automata ◽

Cellular Automata Model

SIMULATION OF NONLOCAL COMPUTATION AMONG NEURONAL MODULES WITH QUANTUM CELLULAR AUTOMATA

Journal of Biological System ◽

10.1142/s0218339093000057 ◽

1993 ◽

Vol 01 (01) ◽

pp. 59-68 ◽

Cited By ~ 1

Author(s):

G. GRÖSSING

Keyword(s):

Pattern Recognition ◽

Cellular Automata ◽

Emergent Property ◽

Evolutionary Strategies ◽

Quantum Cellular Automata ◽

Periodic Oscillations ◽

Neuronal Systems

The concept of Nonlocal Computation as emergent property of coupled neuronal modules is introduced with the aid of quantum cellular automata (QCA). Various features of QCA evolution indicate their possible relevance for realistic simulations of neuronal systems. To these features belong the appearance of periodic oscillations in a system of even less than 10 modules, evolutionary strategies like selection and equifinality, the multiplexing of “temporal codes”, and high fault tolerances in pattern recognition tasks.

Cellular automata in pattern recognition

Information Sciences ◽

10.1016/0020-0255(93)90052-n ◽

1993 ◽

Vol 70 (1-2) ◽

pp. 145-177 ◽

Cited By ~ 18

Author(s):

Raghu Raghavan

Keyword(s):

Pattern Recognition ◽

Cellular Automata

Evolving Cellular Automata Based Associative Memory for Pattern Recognition

High Performance Computing — HiPC 2001 - Lecture Notes in Computer Science ◽

10.1007/3-540-45307-5_11 ◽

2001 ◽

pp. 115-124 ◽

Cited By ~ 5

Author(s):

Niloy Ganguly ◽

Arijit Das ◽

Pradipta Maji ◽

Biplab K. Sikdar ◽

P. Pal Chaudhuri

Keyword(s):

Pattern Recognition ◽

Cellular Automata ◽

Associative Memory

Supervised Factorial Learning

Neural Computation ◽

10.1162/neco.1993.5.5.750 ◽

1993 ◽

Vol 5 (5) ◽

pp. 750-766 ◽

Cited By ~ 20

Author(s):

A. Norman Redlich

Keyword(s):

Neural Networks ◽

Pattern Recognition ◽

Functional Connectivity ◽

Cellular Automata ◽

Sensory Processing ◽

Learning Problems ◽

Local Algorithms ◽

Sensory Image ◽

Nonlinear Technique ◽

Reversible Cellular Automata

Factorial learning, finding a statistically independent representation of a sensory “image”—a factorial code—is applied here to solve multilayer supervised learning problems that have traditionally required backpropagation. This lends support to Barlow's argument for factorial sensory processing, by demonstrating how it can solve actual pattern recognition problems. Two techniques for supervised factorial learning are explored, one of which gives a novel distributed solution requiring only positive examples. Also, a new nonlinear technique for factorial learning is introduced that uses neural networks based on almost reversible cellular automata. Due to the special functional connectivity of these networks—which resemble some biological microcircuits—learning requires only simple local algorithms. Also, supervised factorial learning is shown to be a viable alternative to backpropagation. One significant advantage is the existence of a measure for the performance of intermediate learning stages.