Dense Features for Semi-Dense Stereo Correspondence
International Journal of Computer Vision
Shape Matching and Object Recognition Using Shape Contexts
IEEE Transactions on Pattern Analysis and Machine Intelligence
Natural, Salient Image Patches for Robot Localization
ICPR '04 Proceedings of the Pattern Recognition, 17th International Conference on (ICPR'04) Volume 4 - Volume 04
Feature Correspondence Via Graph Matching: Models and Global Optimization
ECCV '08 Proceedings of the 10th European Conference on Computer Vision: Part II
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In this paper, we consider the feature correspondence task as a graph matching problem. Our approach tends to maximize a similarity objective function, which consists of not only the feature vectors but also their corresponding constrained global spatial structures, by a new polynomial-time approximate optimization algorithm. This algorithm allows every node in a smaller graph to potentially be linked with any node in a larger graph, and thus it can handle one-to-one, many-to-one, and no match cases. Especially, our approach does not necessarily require a training set. We test on the "hotel" and "house" sequences. Matching a pair of frames takes on average 1.24 and 1.22 seconds respectively using a Matlab implementation without any optimization (over an order of magnitude speedup compared to [1]), and with a 2-frame interval our errors are merely 0.07% and 0.09% respectively. Even going up to a 25-frame gap, errors are only 5.66% and 5.00% respectively.