TY - GEN
T1 - Complexity reduction of neural network model for local motion detection in motion stereo vision
AU - Akima, Hisanao
AU - Kawakami, Susumu
AU - Madrenas, Jordi
AU - Moriya, Satoshi
AU - Yano, Masafumi
AU - Nakajima, Koji
AU - Sakuraba, Masao
AU - Sato, Shige
N1 - Funding Information:
Acknowledgments. This work was partly supported by JSPS KAKENHI Grant Number 15K18044. We would like to thank Editage (www.editage.jp) for English language editting.
Publisher Copyright:
© Springer International Publishing AG 2017.
PY - 2017
Y1 - 2017
N2 - Spatial perception, in which objects’ motion and positional relationship are recognized, is necessary for applications such as a walking robot and an autonomous car. One of the demanding features of spatial perception in real world applications is robustness. Neural network-based approaches, in which perception results are obtained by voting among a large number of neuronal activities, seem to be promising. We focused on a neural network model for motion stereo vision proposed by Kawakami et al. In this model, local motion in each small region of the visual field, which comprises optical flow, is detected by hierarchical neural network. Implementation of this model into a VLSI is required for real-time operation with low power consumption. In this study, we reduced the computational complexity of this model and showed cell responses of the reduced model by numerical simulation.
AB - Spatial perception, in which objects’ motion and positional relationship are recognized, is necessary for applications such as a walking robot and an autonomous car. One of the demanding features of spatial perception in real world applications is robustness. Neural network-based approaches, in which perception results are obtained by voting among a large number of neuronal activities, seem to be promising. We focused on a neural network model for motion stereo vision proposed by Kawakami et al. In this model, local motion in each small region of the visual field, which comprises optical flow, is detected by hierarchical neural network. Implementation of this model into a VLSI is required for real-time operation with low power consumption. In this study, we reduced the computational complexity of this model and showed cell responses of the reduced model by numerical simulation.
KW - Hough transform
KW - Local motion detection
KW - Motion stereo vision
KW - VLSI
UR - http://www.scopus.com/inward/record.url?scp=85035081072&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85035081072&partnerID=8YFLogxK
U2 - 10.1007/978-3-319-70136-3_88
DO - 10.1007/978-3-319-70136-3_88
M3 - Conference contribution
AN - SCOPUS:85035081072
SN - 9783319701356
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 830
EP - 839
BT - Neural Information Processing - 24th International Conference, ICONIP 2017, Proceedings
A2 - Liu, Derong
A2 - Xie, Shengli
A2 - Zhao, Dongbin
A2 - Li, Yuanqing
A2 - El-Alfy, El-Sayed M.
PB - Springer Verlag
T2 - 24th International Conference on Neural Information Processing, ICONIP 2017
Y2 - 14 November 2017 through 18 November 2017
ER -