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fbtwitterlinkedinvimeoflicker grey 14rssslideshare1
Publisher: IEEE
Languages: English
Types: Article
Subjects: QA75
Feature matching is a challenging problem at the heart of numerous computer graphics and computer vision applications. We present the SuperMatching algorithm for finding correspondences between two sets of features. It does so by considering triples or higher order tuples of points, going beyond the pointwise and pairwise approaches typically used. SuperMatching is formulated using a supersymmetric tensor representing an affinity metric that takes into account feature similarity and geometric constraints between features: Feature matching is cast as a higher order graph matching problem. SuperMatching takes advantage of supersymmetry to devise an efficient sampling strategy to estimate the affinity tensor, as well as to store the estimated tensor compactly. Matching is performed by an efficient higher order power iteration approach that takes advantage of this compact representation. Experiments on both synthetic and real data show that SuperMatching provides more accurate feature matching than other state-of-the-art approaches for a wide range of 2D and 3D features, with competitive computational cost.
  • The results below are discovered through our pilot algorithms. Let us know how we are doing!

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    • Zhi-Quan Cheng received a BSc, MSc, and PhD degree from Computer School at National University of Defense Technology in 2000, 2002 and 2008, respectively. He is lecturer at the PDL Laboratory, Computer School, National University of Defense Technology (NUDT), and the leader of visual computing team at NUDT. His research interests include computer graphics, and digital geometry processing.
    • Aiping Wang received a BSc, MSc, and PhD degree from Computer School at National University of Defense Technology in 2004, 2006 and 2011, respectively. He is lecturer Computer School, National University of Defense Technology (NUDT). His research interests include computer graphics and vision.
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