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A Constrained Alternating Optimization Framework for Feature Matching
Liu, Xiabi ; Jia, Yunde
The 5th International Conference on Computer Vision Systems, 2007
Bielefeld, 21. - 24. März 2007
This paper proposes a constrained alternating optimization framework to tackle the feature matching problem with partial matching and multiple matching. We model the difference between pairing features as the result of a transformation followed an uncertainty distribution. Based on this modeling, transformation estimation and feature matching are performed alternately from initial matching: the transformation is updated according to the matching, and the matching is updated according to the transformation and the uncertainty distribution. A pruning operation is further presented to reduce the search space of initial matching. In the proposed framework, we develop a B-spline curve feature matching algorithm for hand-gesture based text input, and a line feature matching algorithm which is tested for three applications: model-based recognition, image registration, and stereo matching. The experimental results for two algorithms are reported.
||Technische Fakultät, Arbeitsgruppen der Informatik