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A Boundary-Fragment-Model for Object Detection

  • Andreas Opelt
  • Axel Pinz
  • Andrew Zisserman
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3952)

Abstract

The objective of this work is the detection of object classes, such as airplanes or horses. Instead of using a model based on salient image fragments, we show that object class detection is also possible using only the object’s boundary. To this end, we develop a novel learning technique to extract class-discriminative boundary fragments. In addition to their shape, these “codebook” entries also determine the object’s centroid (in the manner of Leibe et al. [19]). Boosting is used to select discriminative combinations of boundary fragments (weak detectors) to form a strong “Boundary-Fragment-Model” (BFM) detector. The generative aspect of the model is used to determine an approximate segmentation.

We demonstrate the following results: (i) the BFM detector is able to represent and detect object classes principally defined by their shape, rather than their appearance; and (ii) in comparison with other published results on several object classes (airplanes, cars-rear, cows) the BFM detector is able to exceed previous performances, and to achieve this with less supervision (such as the number of training images).

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Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Andreas Opelt
    • 1
  • Axel Pinz
    • 1
  • Andrew Zisserman
    • 2
  1. 1.Vision-based Measurement Group, Inst. of El. Measurement and Meas. Sign. Proc.University of TechnologyGrazAustria
  2. 2.Visual Geometry Group, Department of Engineering ScienceUniversity of OxfordUK

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