著者

仁科 繁明 乾 敏郎

出版者

一般社団法人電子情報通信学会

雑誌

電子情報通信学会技術研究報告. HIP, ヒューマン情報処理

巻号頁・発行日

vol.96, no.306, pp.7-12, 1996-10-17

参考文献数

14

被引用文献数

2

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視点依存表現に基づく物体認識の理論では, 一つの物体に対していくつかの視点からのviewを記憶することによって任意の視点からの認識が達成されると考えられている. そこでは3次元的な構造情報の利用は全く考慮されていない. 本研究では2次元情報のみでは識別が困難であるように作成した刺激の組を用いて, 人間の物体認識システムが3次元構造情報をどのように利用しているかを検討した. 得られた結果はGRBF的な2次元的比較モジュールと3次元的比較モジュールの2つのモジュールが同時並行的に働いていることを示唆した. さらに, 試行を繰り返すことによって見いだせる学習の効果は特に3次元的比較プロセスで大きいことを示した.

著者

仁科 繁明 乾 敏郎

出版者

日本認知科学会

雑誌

認知科学 (ISSN:13417924)

巻号頁・発行日

vol.6, no.4, pp.432-443, 1999-12-01 (Released:2008-10-03)

参考文献数

15

被引用文献数

1

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Two major problems in human object recognition are how we recognize objects from various viewpoints and how we memorize the shape of many objects. View-based object recognition theories have proposed that viewpoint independent recognition can be achieved by obtaining a certain number of views of an object. These theories do not involve the use of 3-dimensional information. In our previous research, however, we showed that the 3-dimensional structural information of objects could be utilized for recognition if enough time is available. The generalization from a familiar view to unknown views improved after trials only under the long reaction time conditions. According to the results, we supposed that there are two kinds of modules that compare the internal representation of objects and the input images. One is a 2-dimensional module that simply matches the images, and the other is a 3-dimensional module that involves transformation between relatively far viewpoints.In this study, we first showed that 3-dimensional complexity of the objects affects the generalization range. The effect was seen only under the long reaction time conditions. This result strongly supports the above hypothesis. In the second part of this study, we replicated our previous result that the generalization range was broadened as a subject becomes more familiar with objects. And we found that the improvement mainly depends on familiarity with each category of objects rather than each object itself. These results cannot be explained by purely view-based theories which are modeled simply with the GRBF (Generalized Radial Basis Functions) network or its derivations, because in such theories each view of each object is independently acquired.