Autonomous Learning of Object Appearances using Colour Contour Frames

Per-Erik Forssén, Anders Moe
CRV06, Québec City, Québec, Canada
3rd Canadian Conference on Computer and Robot Vision
June, 2006

Abstract

In this paper we make use of the idea that a robot can autonomously discover objects and learn their appearances by poking and prodding at interesting parts of a scene. In order to make the resultant object recognition ability more robust, and discriminative, we replace earlier used colour histogram features with an invariant texture-patch method. The texture patches are extracted in a similarity invariant frame which is constructed from short colour contour segments. We demonstrate the robustness of our invariant frames with a repeatability test under general homography transformations of a planar scene. Through the repeatability test, we find that defining the frame using using ellipse segments instead of lines where this is appropriate improves repeatability. We also apply the developed features to autonomous learning of object appearances, and show how the learned objects can be recognised under out-of-plane rotation and scale changes.

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Bibtex entry

@InProceedings{fm06,
  author = 	 {Per-Erik Forss\'en and Anders Moe},
  title = 	 {Autonomous Learning of Object Appearances using Colour Contour Frames},
  booktitle = 	 {3rd Canadian Conference on Computer and Robot Vision},
  OPTpages = 	 {},
  year = 	 {2006},
  address = 	 {Qu\'ebec City, Qu\'ebec, Canada},
  month = 	 {June},
  publisher =    {{IEEE} Computer Society}
}