Title :
Silicon retina: image compression by associative neural network based on code and graph theories
Author_Institution :
Dept. of Electr. & Electron. Eng., Sophia Univ., Tokyo, Japan
Abstract :
An associative neural network (NN) is constructed on the basis of code and graph theories to realize a silicon retina. Each neuron is an EXCLUSIVE-OR unit in the digital NN (DNN) based on finite field GF(2). Each neuron is an adder unit in the analog NN (ANN) based on real field Rb. The network has the following features: no multiplier, sparsity, cellular structure, high concurrency, and high speed. The DNN and the ANN can be applied to data compression for binary and analog images, respectively. The S/N rate in the reproduction image depends on the network structure. Secret image communication and image recognition are possible
Keywords :
analogue computer circuits; computerised pattern recognition; computerised picture processing; content-addressable storage; data compression; digital circuits; learning systems; neural nets; parallel architectures; EXCLUSIVE-OR unit; S/N rate; SANNET; Sophia neural net; adder unit; analog images; analogue neural network; associative neural network; binary images; cellular structure; code theory; data compression; digital neural network; graph theories; high concurrency; high speed; image compression; image recognition; secret image communication; silicon retina; Adders; Artificial neural networks; Cellular networks; Galois fields; Graph theory; Image coding; Neural networks; Neurons; Retina; Silicon;
Conference_Titel :
Circuits and Systems, 1990., IEEE International Symposium on
Conference_Location :
New Orleans, LA
DOI :
10.1109/ISCAS.1990.112037
