Active shape models—their training and application
Computer Vision and Image Understanding
Object Matching Using Deformable Templates
IEEE Transactions on Pattern Analysis and Machine Intelligence
Recognizing and Tracking Human Action
ECCV '02 Proceedings of the 7th European Conference on Computer Vision-Part I
Deformable templates for face recognition
Journal of Cognitive Neuroscience
An efficient garment visual search based on shape context
MUSP'09 Proceedings of the 9th WSEAS international conference on Multimedia systems & signal processing
An efficient garment visual search based on shape context
WSEAS Transactions on Computers
Generic object class detection using boosted configurations of oriented edges
ACCV'10 Proceedings of the 10th Asian conference on Computer vision - Volume Part II
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We present an algorithm for recognizing object categories as opposed to specific instances, based on matching prototypical object shapes to gray-level images. The central part of the algorithm is the establishment of correspondence between prototype template and image based on finding qualitative shape invariants in the form of order types of sets of points and lines. A central problem of any matching algorithm like this is the rejection of background and foreground clutter in the image resulting in erroneous matches. By deforming the prototype and iterating the computation of correspondence we reject outliers and improve the quality of the matching. Experimental results in terms of locating examples of specific object classes in real gray-level images are presented. The results demonstrate the robustness of the algorithm and make it an interesting candidate for any categorical recognition system such as database indexing.