A Hierarchical Compositional System for Rapid Object Detection

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.

[1]  Pedro F. Felzenszwalb.  Representation and detection of deformable shapes , 2003, 2003 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2003. Proceedings..

[2]  Daniel Snow,et al.  Efficient Deformable Template Detection and Localization without User Initialization , 2000, Comput. Vis. Image Underst..

[3]  Paul A. Viola,et al.  Fast and Robust Classification using Asymmetric AdaBoost and a Detector Cascade , 2001, NIPS.

[4]  Takeo Kanade,et al.  A statistical method for 3D object detection applied to faces and cars , 2000, Proceedings IEEE Conference on Computer Vision and Pattern Recognition. CVPR 2000 (Cat. No.PR00662).

[5]  James M. Coughlan,et al.  Finding Deformable Shapes Using Loopy Belief Propagation , 2002, ECCV.

[6]  Jitendra Malik,et al.  Shape matching and object recognition using low distortion correspondences , 2005, 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05).

[7]  James M. Coughlan,et al.  Shape Matching with Belief Propagation: Using Dynamic Quantization to Accomodate Occlusion and Clutter , 2004, 2004 Conference on Computer Vision and Pattern Recognition Workshop.

[8]  Zhuowen Tu,et al.  Image Parsing: Unifying Segmentation, Detection, and Recognition , 2003, Proceedings Ninth IEEE International Conference on Computer Vision.

[9]  Pietro Perona,et al.  Object class recognition by unsupervised scale-invariant learning , 2003, 2003 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2003. Proceedings..

[10]  Alan L. Yuille,et al.  AdaBoost Learning for Detecting and Reading Text in City Scenes , 2004, CVPR 2004.

[11]  Alan L. Yuille,et al.  Detecting and reading text in natural scenes , 2004, CVPR 2004.

[12]  Björn Stenger,et al.  Shape context and chamfer matching in cluttered scenes , 2003, 2003 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2003. Proceedings..

[13]  Martin J. Wainwright,et al.  Tree-based reparameterization framework for analysis of sum-product and related algorithms , 2003, IEEE Trans. Inf. Theory.

[14]  Anand Rangarajan,et al.  A new algorithm for non-rigid point matching , 2000, Proceedings IEEE Conference on Computer Vision and Pattern Recognition. CVPR 2000 (Cat. No.PR00662).

[15]  David G. Lowe,et al.  Object recognition from local scale-invariant features , 1999, Proceedings of the Seventh IEEE International Conference on Computer Vision.