方向選択性局所特徴を用いた統計学習による多視点物体認識システム

概要

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Object recognition system based on local descriptors is increasingly used recently because of their perceived robustness with respect to occlusions and to global geometrical deformations. Such a descriptor — based on a set of oriented Gaussian derivative filters—is used in our recognition system. In this paper, we explore the multiview 3D object recognition and multiview face identification. Basic idea is to find discriminant features to describe an object across different views. Boosting framework is used to select features out of huge feature pool created by collecting the local features from the positive training examples. We conduct experiments on 3D objects and face images and get excellent recognition rate. Comparison to SVM is also noted in the paper.
2009-05-01