Accurate Object Detection with Deformable Shape Models Learnt from ImagesReportar como inadecuado

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1 LEAR - Learning and recognition in vision Inria Grenoble - Rhône-Alpes, LJK - Laboratoire Jean Kuntzmann, INPG - Institut National Polytechnique de Grenoble

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 localize novel instances in the presence of intra-class variations, clutter, and scale changes. Like a shape matcher, it finds the accurate boundaries of the objects, rather than just their bounding-boxes. This is made possible by 1 a novel technique for learning a shape model of an object class given images of example instances; 2 the combination of 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, while needing no segmented examples for training only bounding-boxes.

Keywords : object detection deformable shape models shape learning

Autor: Vittorio Ferrari - Frédéric Jurie - Cordelia Schmid -



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