Abstract
We present an object class detection approach which fully integrates the complementary strengths offered by shape matchers. Like an object detector, it can learn class models directly from images, and can localize novel instances in the presence of intra-class variations, clutter, and scale changes. Like a shape matcher, it finds the boundaries of objects, rather than just their bounding-boxes. This is achieved by a novel technique for learning a shape model of an object class given images of example instances. Furthermore, we also integrate Hough-style voting with a non-rigid point matching algorithm to localize the model in cluttered images. As demonstrated by an extensive evaluation, our method can localize object boundaries accurately and does not need segmented examples for training (only bounding-boxes).
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This research was supported by the EADS foundation, INRIA, CNRS, and SNSF. V. Ferrari was funded by a fellowship of the EADS foundation and by SNSF.
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Ferrari, V., Jurie, F. & Schmid, C. From Images to Shape Models for Object Detection. Int J Comput Vis 87, 284–303 (2010). https://doi.org/10.1007/s11263-009-0270-9
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DOI: https://doi.org/10.1007/s11263-009-0270-9