A Quadratic Programming based Cluster Correspondence Projection Algorithm for Fast Point Matching

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Description

In this project , a gradient descent based point matching algorithm, where the possible optimal correspondence are searched via gradient descent and the constraints on the correspondence are satisfied by constrained projection. The problem of projection was solved by using quadratic programming, and to reduce the computational cost, a cluster projection technique was introduced. Compared with the POCS based method, the proposed algorithm has advantages in fast convergence, high accuracy and low computational cost. The experimental results show that the proposed algorithm is comparable in accuracy with state-of-the art algorithms but needs much less computational cost.

 

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