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Psychophysical studies [9], [17] show that we can recognize objects using fragments of outline contour alone. This paper proposes a new automatic visual recognition system based only on local contour features, capable of localizing objects in space and scale. The system first builds a class-specific codebook of local fragments of contour using a novel formulation of chamfer matching. These local fragments allow recognition that is robust to within-class variation, pose changes, and articulation. Boosting combines these fragments into a cascaded sliding-window classifier, and mean shift is used to select strong responses as a final set of detections. We show how learning can be performed iteratively on both training and test sets to boot-strap an improved classifier. We compare with other methods based on contour and local descriptors in our detailed evaluation over 17 challenging categories, and obtain highly competitive results. The results confirm that contour is indeed a powerful cue for multi-scale and multi-class visual object recognition.
Edge and feature detection, Feature representation, Size and shape, Object recognition, Computer vision, Machine learning
Andrew Blake, Roberto Cipolla, Jamie Shotton, "Multiscale Categorical Object Recognition Using Contour Fragments", IEEE Transactions on Pattern Analysis & Machine Intelligence, vol. 30, no. , pp. 1270-1281, July 2008, doi:10.1109/TPAMI.2007.70772
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