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Download the Paper (115 K, PDF file) - January 1, 2006 Tell a colleague about it. Printing Tips: Select 'print as image' in the Acrobat print dialog if you have trouble printing.

ABSTRACT:
We describe a hierarchical compositional system for detecting de- formable objects in images. Objects are represented by graphical models. The algorithm uses a hierarchical tree where the root of the tree corre- sponds to the full object and lower-level elements of the tree correspond to simpler features. The algorithm proceeds by passing simple messages up and down the tree. The method works rapidly, in under a second, on 320 × 240 images. We demonstrate the approach on detecting cat- s, horses, and hands. The method works in the presence of background clutter and occlusions. Our approach is contrasted with more traditional methods such as dynamic programming and belief propagation.

SUGGESTED CITATION:
Long Zhu and Alan L. Yuille, "A Hierarchical Compositional System for Rapid Object Detection" (January 1, 2006). Department of Statistics, UCLA. Department of Statistics Papers. Paper 2005010108.
http://repositories.cdlib.org/uclastat/papers/2005010108