Thumbnail
Access Restriction
Open

Author Lian, Wei ♦ Zhang, Lei
Source CiteSeerX
Content type Text
File Format PDF
Subject Domain (in DDC) Computer science, information & general works ♦ Data processing & computer science
Subject Keyword Robust Point Matching ♦ Concave Optimization Approach ♦ Energy Function ♦ Concave Function ♦ Deformation Need ♦ Optimal Solution ♦ State-of-the-art Method ♦ Linear Transformation ♦ Global Optimality ♦ Real Data ♦ Concave Optimization Technique ♦ Point Set ♦ Problem Size ♦ Non-rigid Term ♦ Simple Transformation ♦ Transformation Variable ♦ Well-known Robust Point Matching ♦ Similarity Transform ♦ Undesirable Result
Abstract Abstract. The well-known robust point matching (RPM) method uses deterministic annealing for optimization, and it has two problems. First, it cannot guarantee the global optimality of the solution and tends to align the centers of two point sets. Second, deformation needs to be regularized to avoid the generation of undesirable results. To address these problems, in this paper we first show that the energy function of RPM can be reduced to a concave function with very few non-rigid terms after eliminating the transformation variables and applying linear transformation; we then propose to use concave optimization technique to minimize the resulting energy function. The proposed method scales well with problem size, achieves the globally optimal solution, and does not need regularization for simple transformations such as similarity transform. Experiments on synthetic and real data validate the advantages of our method in comparison with state-of-the-art methods. 1
Educational Role Student ♦ Teacher
Age Range above 22 year
Educational Use Research
Education Level UG and PG ♦ Career/Technical Study
Learning Resource Type Article