Abstract
This paper proposes a model of the binocular fusion based on the psychological experimental results and the physiological knowledge. Considering the psychological results and the physiological structure, we assume that the binocular information are processed by several binocular channel having different spatial characteristics from low spatial frequency to high spatial frequency. In order to examine the mechanism of the binocular fusion, we construct a five layer neural network model, and train it by the back-propagation learning algorithm with use of psychological experimental data. After completion of learning, the generalization capability of the network are examined. Further, the response functions of the hidden units have been examined, which suggested that the hidden units have spatial selective characteristic. In other words, the binocular information is considered to be pressed of parallel channels with different spatial characteristics.
| Original language | English |
|---|---|
| Title of host publication | IEEE International Conference on Neural Networks - Conference Proceedings |
| Publisher | IEEE |
| Pages | 4169-4174 |
| Number of pages | 6 |
| Volume | 6 |
| Publication status | Published - 1994 |
| Externally published | Yes |
| Event | Proceedings of the 1994 IEEE International Conference on Neural Networks. Part 1 (of 7) - Orlando, FL, USA Duration: Jun 27 1994 → Jun 29 1994 |
Other
| Other | Proceedings of the 1994 IEEE International Conference on Neural Networks. Part 1 (of 7) |
|---|---|
| City | Orlando, FL, USA |
| Period | 6/27/94 → 6/29/94 |
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ASJC Scopus subject areas
- Software
Cite this
Neural network model of the binocular fusion in the human vision. / Wu, Jinglong; Nishikawa, Yoshikazu.
IEEE International Conference on Neural Networks - Conference Proceedings. Vol. 6 IEEE, 1994. p. 4169-4174.Research output: Chapter in Book/Report/Conference proceeding › Conference contribution
}
TY - GEN
T1 - Neural network model of the binocular fusion in the human vision
AU - Wu, Jinglong
AU - Nishikawa, Yoshikazu
PY - 1994
Y1 - 1994
N2 - This paper proposes a model of the binocular fusion based on the psychological experimental results and the physiological knowledge. Considering the psychological results and the physiological structure, we assume that the binocular information are processed by several binocular channel having different spatial characteristics from low spatial frequency to high spatial frequency. In order to examine the mechanism of the binocular fusion, we construct a five layer neural network model, and train it by the back-propagation learning algorithm with use of psychological experimental data. After completion of learning, the generalization capability of the network are examined. Further, the response functions of the hidden units have been examined, which suggested that the hidden units have spatial selective characteristic. In other words, the binocular information is considered to be pressed of parallel channels with different spatial characteristics.
AB - This paper proposes a model of the binocular fusion based on the psychological experimental results and the physiological knowledge. Considering the psychological results and the physiological structure, we assume that the binocular information are processed by several binocular channel having different spatial characteristics from low spatial frequency to high spatial frequency. In order to examine the mechanism of the binocular fusion, we construct a five layer neural network model, and train it by the back-propagation learning algorithm with use of psychological experimental data. After completion of learning, the generalization capability of the network are examined. Further, the response functions of the hidden units have been examined, which suggested that the hidden units have spatial selective characteristic. In other words, the binocular information is considered to be pressed of parallel channels with different spatial characteristics.
UR - http://www.scopus.com/inward/record.url?scp=0028706640&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=0028706640&partnerID=8YFLogxK
M3 - Conference contribution
VL - 6
SP - 4169
EP - 4174
BT - IEEE International Conference on Neural Networks - Conference Proceedings
PB - IEEE
ER -