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Object Detection by Contour Segment Networks

  • Vittorio Ferrari
  • Tinne Tuytelaars
  • Luc Van Gool
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3953)

Abstract

We propose a method for object detection in cluttered real images, given a single hand-drawn example as model. The image edges are partitioned into contour segments and organized in an image representation which encodes their interconnections: the Contour Segment Network. The object detection problem is formulated as finding paths through the network resembling the model outlines, and a computationally efficient detection technique is presented. An extensive experimental evaluation on detecting five diverse object classes over hundreds of images demonstrates that our method works in very cluttered images, allows for scale changes and considerable intra-class shape variation, is robust to interrupted contours, and is computationally efficient.

Keywords

Object Detection Model Segment Image Edge Contour Segment Object Contour 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Vittorio Ferrari
    • 1
  • Tinne Tuytelaars
    • 2
  • Luc Van Gool
    • 1
    • 2
  1. 1.Computer Vision Group (BIWI)ETH ZuerichSwitzerland
  2. 2.ESAT-PSIUniversity of LeuvenBelgium

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