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Shin, Dongjoe; Tjahjadi, Tardi (2010)
Publisher: Pergamon
Languages: English
Types: Article
Subjects: TK, QA76

Classified by OpenAIRE into

arxiv: Computer Science::Computer Vision and Pattern Recognition
ACM Ref: ComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISION
Assuming that the image distortion between corresponding regions of a stereo pair of images with wide baseline can be approximated as an affine transformation if the regions are reasonably small, recent image matching algorithms have focused on affine invariant region (IR) detection and its description to increase the robustness in matching. However, the distinctiveness of an intensity-based region descriptor tends to deteriorate when an image includes homogeneous texture or repetitive pattern. To address this problem, we investigated the geometry of a local IR cluster (also called a clique) and propose a new clique-based image matching method. In the proposed method, the clique of an IR is estimated by Delaunay triangulation in a local affine frame and the Hausdorff distance is adopted for matching an inexact number of multiple descriptor vectors. We also introduce two adaptively weighted clique distances, where the neighbour distance in a clique is appropriately weighted according to characteristics of the local feature distribution. Experimental results show the clique-based matching method produces more tentative correspondences than variants of the SIFT-based method.
  • The results below are discovered through our pilot algorithms. Let us know how we are doing!

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  • Discovered through pilot similarity algorithms. Send us your feedback.

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