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<p><b>Abstract</b>—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, <it>detectability</it>, <it>uniqueness</it>, and <it>reliability</it>, 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.</p>
Object recognition, multiple objects, eigenspace, detectability, uniqueness, reliability.

K. Ohba and K. Ikeuchi, "Detectability, Uniqueness, and Reliability of Eigen Windows for Stable Verification of Partially Occluded Objects," in IEEE Transactions on Pattern Analysis & Machine Intelligence, vol. 19, no. , pp. 1043-1048, 1997.
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