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Author Fahad Shahbaz Khan; Joost Van de Weijer; Maria Vanrell
Title Top-Down Color Attention for Object Recognition Type Conference Article
Year 2009 Publication 12th International Conference on Computer Vision Abbreviated Journal
Volume Issue Pages 979 - 986
Keywords
Abstract Generally the bag-of-words based image representation follows a bottom-up paradigm. The subsequent stages of the process: feature detection, feature description, vocabulary construction and image representation are performed independent of the intentioned object classes to be detected. In such a framework, combining multiple cues such as shape and color often provides below-expected results. This paper presents a novel method for recognizing object categories when using multiple cues by separating the shape and color cue. Color is used to guide attention by means of a top-down category-specific attention map. The color attention map is then further deployed to modulate the shape features by taking more features from regions within an image that are likely to contain an object instance. This procedure leads to a category-specific image histogram representation for each category. Furthermore, we argue that the method combines the advantages of both early and late fusion. We compare our approach with existing methods that combine color and shape cues on three data sets containing varied importance of both cues, namely, Soccer ( color predominance), Flower (color and shape parity), and PASCAL VOC Challenge 2007 (shape predominance). The experiments clearly demonstrate that in all three data sets our proposed framework significantly outperforms the state-of-the-art methods for combining color and shape information.
Address Kyoto, Japan
Corporate Author Thesis
Publisher Place of Publication Editor
Language Summary Language Original Title
Series Editor Series Title Abbreviated Series Title
Series Volume Series Issue Edition
ISSN 1550-5499 ISBN 978-1-4244-4420-5 Medium
Area Expedition Conference ICCV
Notes CIC Approved (up) no
Call Number CAT @ cat @ SWV2009 Serial 1196
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Author Ivan Huerta; Michael Holte; Thomas B. Moeslund; Jordi Gonzalez
Title Detection and Removal of Chromatic Moving Shadows in Surveillance Scenarios Type Conference Article
Year 2009 Publication 12th International Conference on Computer Vision Abbreviated Journal
Volume Issue Pages 1499 - 1506
Keywords
Abstract Segmentation in the surveillance domain has to deal with shadows to avoid distortions when detecting moving objects. Most segmentation approaches dealing with shadow detection are typically restricted to penumbra shadows. Therefore, such techniques cannot cope well with umbra shadows. Consequently, umbra shadows are usually detected as part of moving objects. In this paper we present a novel technique based on gradient and colour models for separating chromatic moving cast shadows from detected moving objects. Firstly, both a chromatic invariant colour cone model and an invariant gradient model are built to perform automatic segmentation while detecting potential shadows. In a second step, regions corresponding to potential shadows are grouped by considering “a bluish effect” and an edge partitioning. Lastly, (i) temporal similarities between textures and (ii) spatial similarities between chrominance angle and brightness distortions are analysed for all potential shadow regions in order to finally identify umbra shadows. Unlike other approaches, our method does not make any a-priori assumptions about camera location, surface geometries, surface textures, shapes and types of shadows, objects, and background. Experimental results show the performance and accuracy of our approach in different shadowed materials and illumination conditions.
Address Kyoto, Japan
Corporate Author Thesis
Publisher Place of Publication Editor
Language Summary Language Original Title
Series Editor Series Title Abbreviated Series Title
Series Volume Series Issue Edition
ISSN 1550-5499 ISBN 978-1-4244-4420-5 Medium
Area Expedition Conference ICCV
Notes Approved (up) no
Call Number ISE @ ise @ HHM2009 Serial 1213
Permanent link to this record