1 research outputs found
Novel Co-variant Feature Point Matching Based on Gaussian Mixture Model
The feature frame is a key idea of feature matching problem between two
images. However, most of the traditional matching methods only simply employ
the spatial location information (the coordinates), which ignores the shape and
orientation information of the local feature. Such additional information can
be obtained along with coordinates using general co-variant detectors such as
DOG, Hessian, Harris-Affine and MSER. In this paper, we develop a novel method
considering all the feature center position coordinates, the local feature
shape and orientation information based on Gaussian Mixture Model for
co-variant feature matching. We proposed three sub-versions in our method for
solving the matching problem in different conditions: rigid, affine and
non-rigid, respectively, which all optimized by expectation maximization
algorithm. Due to the effective utilization of the additional shape and
orientation information, the proposed model can significantly improve the
performance in terms of convergence speed and recall. Besides, it is more
robust to the outliers.Comment: arXiv admin note: text overlap with arXiv:0905.2635 by other author