Object detection in cluttered range images using edgel geometry (特集 ビジョン技術によるイノベーション) Object Detection in Cluttered Range Images Using Edgel Geometry

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Abstract

In this paper, we present an object detection technique that uses scale invariant local edgel structures and their properties to locate multiple object categories within a range image in the presence of partial occlusion, cluttered background, and significant scale changes. The fragmented local edgels (<i>key-edgel</i>, <i>e<sub>k</sub></i>) are efficiently extracted from a 3D edge map by separating them at their corner points. The 3D edge maps are reliably constructed by combining both boundary and fold edges of 3D range images. Each key-edgel is described using our scale invariant descriptors that encode local geometric configuration by joining the edgel to adjacent edgels at its start and end points. Using key-edgels and their descriptors, our model generates promising hypothetical locations in the image. These hypotheses are then verified using more discriminative features. The discriminative feature consists of a bag-of-words histogram constructed by key-edgels and their descriptors, and a pyramid histogram of orientation gradients. To find the similarities between different feature types in a discriminative stage, we use an exponential χ<sup>2</sup> merging kernel function. Our merging kernel outperforms the conventional <i>rbf</i> kernel of the SVM classifier. The approach is evaluated based on ten diverse object categories in a real-world environment.

Journal

  • IEEJ Transactions on Electronics, Information and Systems

    IEEJ Transactions on Electronics, Information and Systems 130(9), 1572-1580, 2010-09-01

    The Institute of Electrical Engineers of Japan

References:  24

Codes

  • NII Article ID (NAID)
    10026579851
  • NII NACSIS-CAT ID (NCID)
    AN10065950
  • Text Lang
    ENG
  • Article Type
    ART
  • ISSN
    03854221
  • NDL Article ID
    10800316
  • NDL Source Classification
    ZN31(科学技術--電気工学・電気機械工業)
  • NDL Call No.
    Z16-795
  • Data Source
    CJP  NDL  J-STAGE 
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