Image Recognition Neural Network: IRNN

Abstract Artificial neural network models are becoming very attractive in image processing where high computational performance and parallel architectures are required. Recently, many papers appeared on applications of neural networks to problems where some degree of intelligence or human-like performance is desired. This paper describes a novel neural network architecture for image recognition and classification. The proposed neural network, called an image recognition neural network (IRNN), is designed to recognize an object or to estimate an attribute of an object. IRNN takes an analog gray level image as an input and produces an appropriate recognition code at the output.

[1]  Stephen Grossberg,et al.  A massively parallel architecture for a self-organizing neural pattern recognition machine , 1988, Comput. Vis. Graph. Image Process..

[2]  Inho Shin.  Piece-Wise Linear Estimation of Mechanical Properties of Materials with Neural Networks , 1992 .

[3]  M. Arbib.  Brains, Machines, and Mathematics , 1987, Springer US.

[4]  David Zipser,et al.  Feature Discovery by Competive Learning , 1986, Cogn. Sci..

[5]  K. Fukushima.  Neural network model for selective attention in visual pattern recognition and associative recall. , 1987, Applied optics.

[6]  D H HUBEL,et al.  RECEPTIVE FIELDS AND FUNCTIONAL ARCHITECTURE IN TWO NONSTRIATE VISUAL AREAS (18 AND 19) OF THE CAT. , 1965, Journal of neurophysiology.

[7]  P. Werbos,et al.  Beyond Regression : "New Tools for Prediction and Analysis in the Behavioral Sciences , 1974 .

[8]  Bernard Widrow,et al.  Adaptive switching circuits , 1988 .

[9]  Bernard Widrow,et al.  Neural nets for adaptive filtering and adaptive pattern recognition , 1988, Computer.

[10]  W. A. Clark,et al.  Simulation of self-organizing systems by digital computer , 1954, Trans. IRE Prof. Group Inf. Theory.

[11]  King-Sun Fu,et al.  Handbook of pattern recognition and image processing , 1986 .

[12]  Kunihiko Fukushima,et al.  Neocognitron: A hierarchical neural network capable of visual pattern recognition , 1988, Neural Networks.

[13]  R. Desimone,et al.  Visual properties of neurons in a polysensory area in superior temporal sulcus of the macaque. , 1981, Journal of neurophysiology.

[14]  Geoffrey E. Hinton,et al.  Learning representations by back-propagating errors , 1986, Nature.

[15]  Krzysztof J. Cios,et al.  Application of neural networks to prediction of advanced composite structures mechanical response and behavior , 1992 .

[16]  A G Barto,et al.  Learning by statistical cooperation of self-interested neuron-like computing elements. , 1985, Human neurobiology.

[17]  E. Caianiello.  Outline of a theory of thought-processes and thinking machines. , 1961, Journal of theoretical biology.

[18]  T. Kohonen.  Adaptive, associative, and self-organizing functions in neural computing. , 1987, Applied optics.