距離アスペクト画像照合による高効率なロバスト探索

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A novel approach of object recognition is proposed, by which multiple three dimensional objects of range contours with occlusion and/or observation defects are efficiently and robustly recognized in cluttered scene. In our approach, in order to make the object recognition insensitive to peculiar features, such as edges and surface color, a uniform voxel framing has been utilized to define local coordination and to generate local depth structures on the objects, which are called depth aspect images DAI. In this paper, some techniques for efficient object recognition using DAI matching are designed. The first one is hashing of DAI images based on two dimensional hash table by use of local geometric key features, which enables fast search in the DAI database. The second one in recognition is random sampling for the voxel coordination, followed by parameter design with probabilistic modeling of sampling. By use of those two techniques, many redundant candidates for objects can be prevented from matching computation, realizing efficient and robust recognition of multiple objects in the complicated scene. The third one is introduction of support spaces for DAI calculation whichcooperates with a fast statistical evaluation of the similarity. We demonstrate recognition examinations to clutter scenes with a large DAI database involving eight models and verify the effectiveness and efficiency of these techniques through analysis of recognition on 100 scenes.
社団法人電気学会の論文
2004-03-01

距離アスペクト画像照合による高効率なロバスト探索

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