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Corner detectors for affine invariant salient regions: is color important?

Nicu Sebe 1 Theo Gevers 1 Joost van de Weijer 2 Sietse Dijkstra 1
2 LEAR - Learning and recognition in vision
GRAVIR - IMAG - Laboratoire d'informatique GRAphique, VIsion et Robotique de Grenoble, Inria Grenoble - Rhône-Alpes, CNRS - Centre National de la Recherche Scientifique : FR71
Abstract : Recently, a lot of research has been done on the matching of images and their structures. Although the approaches are very different, most methods use some kind of point selection from which descriptors or a hierarchy are derived. We focus here on the methods that are related to the detection of points and regions that can be detected in an affine invariant way. Most of the previous research concentrated on intensity based methods. However, we show in this work that color information can make a significant contribution to feature detection and matching. Our color based detection algorithms detect the most distinctive features and the experiments suggest that to obtain optimal performance, a tradeoff should be made between invariance and distinctiveness by an appropriate weighting of the intensity and color information.
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Nicu Sebe, Theo Gevers, Joost van de Weijer, Sietse Dijkstra. Corner detectors for affine invariant salient regions: is color important?. International Conference on Image and Video Retrieval (CIVR '06), Jul 2006, Tempe, United States. pp.61--71, ⟨10.1007/11788034_7⟩. ⟨inria-00548578⟩

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