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Robust and Efficient Detection of Salient Convex Groups
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ECCV '08 Proceedings of the 10th European Conference on Computer Vision: Part I
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ISVC '08 Proceedings of the 4th International Symposium on Advances in Visual Computing, Part II
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SSVM '09 Proceedings of the Second International Conference on Scale Space and Variational Methods in Computer Vision
A Bag of Strings Representation for Image Categorization
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Scalable learning for object detection with GPU hardware
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Computer Vision and Image Understanding
CLEF'08 Proceedings of the 9th Cross-language evaluation forum conference on Evaluating systems for multilingual and multimodal information access
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ACCV'10 Proceedings of the 10th Asian conference on Computer vision - Volume Part II
Spatiotemporal contour grouping using abstract part models
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Pattern Recognition Letters
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International Journal of Computer Vision
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ACCV'12 Proceedings of the 11th Asian conference on Computer Vision - Volume Part I
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Computer Vision and Image Understanding
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We present a family of scale-invariant local shape features formed by chains of k connected, roughly straight contour segments (kAS), and their use for object class detection. kAS are able to cleanly encode pure fragments of an object boundary, without including nearby clutter. Moreover, they offer an attractive compromise between information content and repeatability, and encompass a wide variety of local shape structures. We also define a translation and scale invariant descriptor encoding the geometric configuration of the segments within a kAS, making kAS easy to reuse in other frameworks, for example as a replacement or addition to interest points. Software for detecting and describing kAS is released on lear.inrialpes.fr/software. We demonstrate the high performance of kAS within a simple but powerful sliding-window object detection scheme. Through extensive evaluations, involving eight diverse object classes and more than 1400 images, we 1) study the evolution of performance as the degree of feature complexity k varies and determine the best degree; 2) show that kAS substantially outperform interest points for detecting shape-based classes; 3) compare our object detector to the recent, state-of-the-art system by Dalal and Triggs [4].