Object Recognition in Image Sequences with Hopfield Neural Network
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概要
- 論文の詳細を見る
In case of object recognition using 3-D configuration data, the scale and poses of the object are important factors. If they are not known, we can not compare the object with the models in the database. Hence we propose a strategy for object recognition independently of its scale and poses, which is based on Hopfield neural network. And we also propose a strategy for estimation of the camera motion to reconstruct 3-D configuration of the object. In this strategy, the camera motion is estimated only with the sequential images taken by a moving camera. Consequently, the 3-D configuration of the object is reconstructed only with the sequential images. And we adopt the multiple regression analysis for estimation of the camera motion parameters so as to reduce the errors of them.
- 社団法人電子情報通信学会の論文
- 1995-08-25
著者
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Fukunaga Kunio
College Of Engineering University Of Osaka Prefecture
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Fukunaga Kunio
College Of Engineering Osaka Prefecture University
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Izumi M
Osaka Prefecture Univ. Sakai‐shi Jpn
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Izumi Masao
College Of Engineering Osaka Prefecture University
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Nishimura Kouichirou
College of Engineering, University of Osaka Prefecture
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Nishimura K
Keio Univ. Yokohama‐shi Jpn
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