Dithering-based Sampling and Weighted α-shapes for Local Feature Detection
نویسندگان
چکیده
منابع مشابه
Dithering-based Sampling and Weighted α-shapes for Local Feature Detection
Local feature detection has been an essential part of many methods for computer vision applications like large scale image retrieval, object detection, or tracking. Recently, structure-guided feature detectors have been proposed, exploiting image edges to accurately capture local shape. Among them, the WαSH detector [Varytimidis et al., 2012] starts from sampling binary edges and exploits α-sha...
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Depending on the application, local feature detectors should comply with properties that are often contradictory, e.g . distinctiveness vs. robustness. Providing a good balance is a standing problem in the field. In this direction, we propose a novel approach for local feature detection starting from sampled edges. The detector is based on shape stability measures across the weighted α-filtrati...
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The recent trend of structure-guided feature detectors, as opposed to blob and corner detectors, has led to a family of methods that exploit image edges to accurately capture local shape. Among them, the WαSH detector combines binary edge sampling with gradient strength and computational geometry representations towards distinctive and repeatable local features. In this work, we provide alterna...
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ژورنال
عنوان ژورنال: IPSJ Transactions on Computer Vision and Applications
سال: 2015
ISSN: 1882-6695
DOI: 10.2197/ipsjtcva.7.189