Detectability, Uniqueness, and Reliability of Eigen Windows for Stable Verification of Partially Occluded Objects

Authors:
Kohtaro Ohba;Katsushi Ikeuchi
Affiliations:
Ministry of International Trade and Industries, Tsukuba, Japan;Univ. of Tokyo, Tokyo, Japan
Venue:
IEEE Transactions on Pattern Analysis and Machine Intelligence
Year:
1997

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Abstract

This paper describes a method for recognizing partially occluded objects for bin-picking tasks using eigenspace analysis, referred to as the "eigen window" method, that stores multiple partial appearances of an object in an eigenspace. Such partial appearances require a large amount of memory space. Three measurements, detectability, uniqueness, and reliability, on windows are developed to eliminate redundant windows and thereby reduce memory requirements. Using a pose clustering technique, the method determines the pose of an object and the object type itself. We have implemented the method and verified its validity.