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Object Detection by Contour Segment Networks

Vittorio Ferrari, Tinne Tuytelaars, Luc Van Gool
Proceeding of the European Conference on Computer Vision
Graz, June 2006


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.

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  author = {Vittorio Ferrari and Tinne Tuytelaars and Luc Van Gool},
  title = {Object Detection by Contour Segment Networks},
  booktitle = {Proceeding of the European Conference on Computer Vision},
  year = {2006},
  month = {June},
  pages = {14-28},
  volume = {3953},
  series = {LNCS},
  publisher = {Elsevier},
  keywords = {object detection; shape matching; contour features}