Title :
CNN state equations for binocular stereo vision
Author :
Tanaka, Mamoru ; Awata, Mitsuhiko ; Kanaya, Mitsuhisa
Author_Institution :
Dept. of Electr. Eng., Sophia Univ., Tokyo, Japan
fDate :
27 Jun-2 Jul 1994
Abstract :
This paper describes CNN (cellular neural network) state equations to perform direct dynamic halftoning and dynamic depth extraction for binocular stereo visual information. The quantization for the funneling information is done by parallel neurons without the use of multiple bit analog-to-digital converters, and, the correspondence problem can be solved by pattern recognition for analog images reconstructed from the transmitted halftoning images. Computer simulation shows the verification for dynamic halftoning and extraction process for analog stereo images
Keywords :
cellular neural nets; image recognition; stereo image processing; analog stereo images; binocular stereo vision; cellular neural network state equations; correspondence problem; direct dynamic halftoning; dynamic depth extraction; extraction process; funneling information; parallel neurons; pattern recognition; quantization; Analog-digital conversion; Cellular neural networks; Computer simulation; Data mining; Equations; Image reconstruction; Neurons; Pattern recognition; Quantization; Stereo vision;
Conference_Titel :
Neural Networks, 1994. IEEE World Congress on Computational Intelligence., 1994 IEEE International Conference on
Conference_Location :
Orlando, FL
Print_ISBN :
0-7803-1901-X
DOI :
10.1109/ICNN.1994.374455
