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Complexity Reduction of Neural Network Model for Local Motion Detection in Motion Stereo Vision

  • Hisanao Akima
  • Susumu Kawakami
  • Jordi Madrenas
  • Satoshi Moriya
  • Masafumi Yano
  • Koji Nakajima
  • Masao Sakuraba
  • Shige Sato
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10639)

Abstract

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.

Keywords

Motion stereo vision Local motion detection Hough transform VLSI 

Notes

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.

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Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  • Hisanao Akima
    • 1
  • Susumu Kawakami
    • 1
  • Jordi Madrenas
    • 2
  • Satoshi Moriya
    • 1
  • Masafumi Yano
    • 1
  • Koji Nakajima
    • 1
  • Masao Sakuraba
    • 1
  • Shige Sato
    • 1
  1. 1.Research Institute of Electrical CommunicationTohoku UniversityAoba-ku, SendaiJapan
  2. 2.Department of Electronic EngineeringUniversitat Politècnica de CatalunyaBarcelonaSpain

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