Probabilistic cellular automata in pattern recognition

Brady,; Raghavan,; Slawny,

doi:10.1109/ijcnn.1989.118577

Cited by 6 publications

(3 citation statements)

References 8 publications

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“…Section 4 describes some preliminary experimental results, and Section 5 is a conclusion which offers some comments on remaining problems. This paper is a continuation of work reported in [BRS89]. Conference reports concerning applications to image recognition are available, e.g.…”

Section: Introductionmentioning

confidence: 93%

<title>Image recognition, learning, and control in a cellular automata network</title>

Raghavan¹

1991

SPIE Proceedings

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The theory of control is being widely used in optimization of dynamical systems. Learning algorithms in neural nets or in statistics have, however, seldom used the techniques of control. One reason for this is that the neural network parameters (synaptic weights) are used quasi-statically during processing after a learning phase, while control theory determines an optimal trajectory in time for the parameters. We address this issue in the context of a neural network dynamics that we have introduced in previous publications as part of an image recognition system designed to integrate modeibased and datadriven approaches in a connectionist framework. An important feature of this approach is that recognition must be achieved explicitly through the short-, rather than the long-time behavior of the dynamics of the system. Our dynamics arises naturally from requirements on the system which include: incorporation of prior knowledge such as in inference rules, locality of inferences, and full parallelism. We have shown this system to be effective in image recognition.In this paper, after reviewing the dynamical system, we compare new algorithms for learning the dynamics with Boltzmann-machine-like formulas. We aiso point out some interesting implications of this approach, namely that of a processing strategy that uses a dynamics for the weights as well as the states of the neurons. We conclude by mentioning the difficulties that remain with a control-theoretic strategy.

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Section: Introductionmentioning

confidence: 93%

<title>Image recognition, learning, and control in a cellular automata network</title>

Raghavan¹

1991

SPIE Proceedings

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“…wheels from arcs). In fact, initial outputs for the higher level structures are usually unnecessary (see also [BRS89]). …”

Section: System Overviewmentioning

confidence: 98%

Image recognition and learning in parallel networks

Raghavan¹,

Adams²,

Nguyen³

et al. 1990

SPIE Proceedings

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We describe the design of an image-recognition system and its performance on multi-sensor imagery.The system satisfies a list of natural requirements, which includes locality of inferences (for efficient VLSI implementation), incorporation of prior knowledge, multi-level hierarchies, and iterative improvement. Two of the most important new features are: a uniform parallel architecture for low-, mid-and high-level vision; and achievement of recognition through short-, as opposed to its long-time behavior, of a dynamical system. Robustness depends on collective effects rather than high precision of the processing elements. Th resulting network displays a balance of high speed and small size. We also indicate how this architecture is related to the Dempster-Shafer calculus for combining evidence from multiple sources, and present novel methods of learning in such networks, including one that addresses the integration of model-based and data-driven approaches.

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“…Algoritmos de aprendizado para redes Booleanas trabalham basicamente trocando as conexões da rede (PATARNELLO & CARNEVALI, 1987, DOYLE, 1990, a função Booleana realizada pelos nós (KANERVA, 1988) ou os valores armazenados nos conteúdos de memória (BLEDSOE & BROWNING, 1966, ALEKSANDER, 1967, ARMSTRONG & GECSEI, 1979, KAN & ALEKSANDER, 1987, MYERS & ALEKSANDER, 1988, BRADY et al, 1989.…”

Section: Estratégias De Aprendizado Para Gsn ôunclassified

Sistema de reconhecimento de imagens, baseado no modelo GSN de rede neural

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Probabilistic cellular automata in pattern recognition

Cited by 6 publications

References 8 publications

<title>Image recognition, learning, and control in a cellular automata network</title>

<title>Image recognition, learning, and control in a cellular automata network</title>

Image recognition and learning in parallel networks

Sistema de reconhecimento de imagens, baseado no modelo GSN de rede neural

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