Publication
Graph matching using SIFT descriptors - An application to pose recovery of a mobile robot
Conference Article
Conference
International Conference on Computer Vision Theory and Applications (VISAPP)
Edition
5th
Pages
249-254
Doc link
http://visapp.visigrapp.org/VISAPP2010/index.htm
File
Abstract
Image-feature matching based on Local Invariant Feature Extraction (LIFE) methods has proven to be successful, and SIFT is one of the most effective. SIFT matching uses only local texture information to compute the correspondences. A number of approaches have been presented aimed at enhancing the image-features matches computed using only local information such as SIFT. What most of these approaches have in common is that they use a higher level information such as spatial arrangement of the feature points to reject a subset of outliers. The main limitation of the outlier rejectors is that they are not able to enhance the configuration of matches by adding new useful ones. In the present work we propose a graph matching algorithm aimed not only at rejecting erroneous matches but also at selecting additional useful ones. We use both the graph structure to encode the geometrical information and the SIFT descriptors in the node’s attributes to provide local texture information. This algorithm is an ensemble of successful ideas previously reported by other researchers. We demonstrate the effectiveness of our algorithm in a pose recovery application.
Categories
image recognition, mobile robots, pattern recognition, robot vision.
Scientific reference
G. Sanroma, R. Alquézar Mancho and F. Serratosa i Casanelles. Graph matching using SIFT descriptors - An application to pose recovery of a mobile robot, 5th International Conference on Computer Vision Theory and Applications, 2010, Angers, France, pp. 249-254.
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