Partial similarity of shapes is a challenging problem arising in many important applications in computer vision,shape analysis,and graphics,e.g.when one has to deal with partial information and acquisition artifacts.T...Partial similarity of shapes is a challenging problem arising in many important applications in computer vision,shape analysis,and graphics,e.g.when one has to deal with partial information and acquisition artifacts.The problem is especially hard when the underlying shapes are non-rigid and are given up to a deformation.Partial matching is usually approached by computing local descriptors on a pair of shapes and then establishing a point-wise non-bijective correspondence between the two,taking into account possibly different parts.In this paper,we introduce an alternative correspondence-less approach to matching fragments to an entire shape undergoing a non-rigid deformation.We use region-wise local descriptors and optimize over the integration domains on which the integral descriptors of the two parts match.The problem is regularized using the Mumford-Shah functional.We show an efficient discretization based on the Ambrosio-Tortorelli approximation generalized to triangular point clouds and meshes,and present experiments demonstrating the success of the proposed method.展开更多
Detecting similarity between non-rigid shapes is one of the fundamental problems in computer vision.In order to measure the similarity the shapes must first be aligned.As opposite to rigid alignment that can be parame...Detecting similarity between non-rigid shapes is one of the fundamental problems in computer vision.In order to measure the similarity the shapes must first be aligned.As opposite to rigid alignment that can be parameterized using a small number of unknowns representing rotations,reflections and translations,non-rigid alignment is not easily parameterized.Majority of the methods addressing this problem boil down to a minimization of a certain distortion measure.The complexity of a matching process is exponential by nature,but it can be heuristically reduced to a quadratic or even linear for shapes which are smooth two-manifolds.Here we model the shapes using both local and global structures,employ these to construct a quadratic dissimilarity measure,and provide a hierarchical framework for minimizing it to obtain sparse set of corresponding points.These correspondences may serve as an initialization for dense linear correspondence search.展开更多
基金The author would like to thank the referees for the helpful suggestionsThis work has been supported in part by the Israeli Science Foundation grant 615/11+1 种基金the German-Israeli Foundation grant 2269/2010and the Swiss High Performance and High Productivity Computing(HP2C)grant.
文摘Partial similarity of shapes is a challenging problem arising in many important applications in computer vision,shape analysis,and graphics,e.g.when one has to deal with partial information and acquisition artifacts.The problem is especially hard when the underlying shapes are non-rigid and are given up to a deformation.Partial matching is usually approached by computing local descriptors on a pair of shapes and then establishing a point-wise non-bijective correspondence between the two,taking into account possibly different parts.In this paper,we introduce an alternative correspondence-less approach to matching fragments to an entire shape undergoing a non-rigid deformation.We use region-wise local descriptors and optimize over the integration domains on which the integral descriptors of the two parts match.The problem is regularized using the Mumford-Shah functional.We show an efficient discretization based on the Ambrosio-Tortorelli approximation generalized to triangular point clouds and meshes,and present experiments demonstrating the success of the proposed method.
基金This research was supported by European Community’s FP7-ERC program,grant agreement no.267414.
文摘Detecting similarity between non-rigid shapes is one of the fundamental problems in computer vision.In order to measure the similarity the shapes must first be aligned.As opposite to rigid alignment that can be parameterized using a small number of unknowns representing rotations,reflections and translations,non-rigid alignment is not easily parameterized.Majority of the methods addressing this problem boil down to a minimization of a certain distortion measure.The complexity of a matching process is exponential by nature,but it can be heuristically reduced to a quadratic or even linear for shapes which are smooth two-manifolds.Here we model the shapes using both local and global structures,employ these to construct a quadratic dissimilarity measure,and provide a hierarchical framework for minimizing it to obtain sparse set of corresponding points.These correspondences may serve as an initialization for dense linear correspondence search.
