1. Model-free Consensus Maximization for Non-Rigid Shapes
- Author
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Ajad Chhatkuli, Danda Pani Paudel, Luc Van Gool, Thomas Probst, Ferrari, Vittorio, Hebert, Martial, Sminchisescu, Cristian, and Weiss, Yair
- Subjects
FOS: Computer and information sciences ,Branch and bound ,Computer science ,Computer Vision and Pattern Recognition (cs.CV) ,Computer Science - Computer Vision and Pattern Recognition ,020207 software engineering ,Context (language use) ,02 engineering and technology ,Maximization ,RANSAC ,Transformation (function) ,Outlier ,0202 electrical engineering, electronic engineering, information engineering ,Graph (abstract data type) ,020201 artificial intelligence & image processing ,Non-rigid Matching ,Algorithm ,Integer (computer science) - Abstract
Many computer vision methods use consensus maximization to relate measurements containing outliers with the correct transformation model. In the context of rigid shapes, this is typically done using Random Sampling and Consensus (RANSAC) by estimating an analytical model that agrees with the largest number of measurements (inliers). However, small parameter models may not be always available. In this paper, we formulate the model-free consensus maximization as an Integer Program in a graph using ‘rules’ on measurements. We then provide a method to solve it optimally using the Branch and Bound (BnB) paradigm. We focus its application on non-rigid shapes, where we apply the method to remove outlier 3D correspondences and achieve performance superior to the state of the art. Our method works with outlier ratio as high as 80%. We further derive a similar formulation for 3D template to image matching, achieving similar or better performance compared to the state of the art., Lecture Notes in Computer Science, 11208, ISSN:0302-9743, ISSN:1611-3349, Computer Vision – ECCV 2018, ISBN:978-3-030-01224-3, ISBN:978-3-030-01225-0
- Published
- 2018
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