Abstract
In 3-D object recognition in human, the recognition performance across viewpoint changes is divided into 2 types: viewpoint-dependent and viewpoint-invariant. We analyzed the viewpoint dependency of objects under the theory of image-based onject representation in human brain (Poggio & Edelman 1990, Tarr 1995) using Support Vector Machines (Vapnik 1995). We suggest from such computational approach that the features of object images between different viewpoints are major factors for human performance in 3-D object recognition.
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Hayasaka, T., Ohnishi, E., Nakauchi, S., Usui, S. (2001). Analysis on the Viewpoint Dependency in 3-D Object Recognition by Support Vector Machines. In: Mira, J., Prieto, A. (eds) Bio-Inspired Applications of Connectionism. IWANN 2001. Lecture Notes in Computer Science, vol 2085. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-45723-2_21
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DOI: https://doi.org/10.1007/3-540-45723-2_21
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