Appearance-based visual learning and object recognition with illumination invariance |
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Authors: | Kohtaro Ohba Yoichi Sato Katsusi Ikeuchi |
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Affiliation: | (1) Mechanical Engineering Laboratory, MITI, 1-2 Namiki, Tsukuba 305-8564, Japan; Tel: +81-298-61-7264, Fax: +81-298-61-7201, e-mail: kohba@mel.go.jp, JP;(2) Institute of Industrial Science, The University of Tokyo, 7-22-1 Roppongi, Minato-ku, Tokyo 106-0032, Japan, JP |
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Abstract: | This paper describes a method for recognizing partially occluded objects under different levels of illumination brightness
by using the eigenspace analysis. In our previous work, we developed the “eigenwindow” method to recognize the partially occluded
objects in an assembly task, and demonstrated with sufficient high performance for the industrial use that the method works
successfully for multiple objects with specularity under constant illumination. In this paper, we modify the eigenwindow method
for recognizing objects under different illumination conditions, as is sometimes the case in manufacturing environments, by
using additional color information. In the proposed method, a measured color in the RGB color space is transformed into one
in the HSV color space. Then, the hue of the measured color, which is invariant to change in illumination brightness and direction,
is used for recognizing multiple objects under different illumination conditions. The proposed method was applied to real
images of multiple objects under various illumination conditions, and the objects were recognized and localized successfully. |
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Keywords: | : Assembly tasks – Object recognition – Visual learning – Eigenspace – Illumination invariance |
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