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Unidimensional multiscale local features for object detection under rotation and mild occlusions

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Villamizar Vergel, Michael Alejandro
Sanfeliu Cortés, AlbertoMés informacióMés informacióMés informació
Andrade-Cetto, JuanMés informacióMés informacióMés informació
Document typePart of book or chapter of book
Defense date2007
PublisherSpringer Verlag
Rights accessOpen Access
All rights reserved. This work is protected by the corresponding intellectual and industrial property rights. Without prejudice to any existing legal exemptions, reproduction, distribution, public communication or transformation of this work are prohibited without permission of the copyright holder
Abstract
In this article, scale and orientation invariant object detection is performed by matching intensity level histograms. Unlike other global measurement methods, the present one uses a local feature description that allows small changes in the histogram signature, giving robustness to partial occlusions. Local features over the object histogram are extracted during a Boosting learning phase, selecting the most discriminant features within a training histogram image set. The Integral Histogram has been used to compute local histograms in constant time.
CitationVillamizar, Michael; Sanfeliu, Alberto; Andrade-Cetto, Juan. "Unidimensional multiscale local features for object detection under rotation and mild occlusions". 3rd Iberian Conference on Pattern Recognition and Image Analysis (IbPRIA), Girona, Catalunya, 2007. A: Lecture Notes in Computer Science, vol. 4477. Berlin, Alemanya: Springer Verlag, 2007, p. 645 - 651. 
URIhttp://hdl.handle.net/2117/2683
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  • IRI - Institut de Robòtica i Informàtica Industrial, CSIC-UPC - Capítols de llibre [48]
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