# Feature Matching and Pose Estimation Using Newton Iteration

• Hongdong Li
• Richard Hartley
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3617)

## Abstract

Feature matching and pose estimation are two crucial tasks in computer vision. The widely adopted scheme is first find the correct matches then estimate the transformation parameters. Unfortunately, such simple scheme does not work well sometimes, because these two tasks of matching and estimation are mutually interlocked. This paper proposes a new method that is able to estimate the transformation and find the correct matches simultaneously. The above interlock is disentangled by an alternating Newton iteration method. We formulate the problem as a nearest-matrix problem, and provide a different numerical technique. Experiments on both synthetic and real images gave good results. Fast global convergence was obtained without the need of good initial guess.

## Keywords

Feature Point Real Image Feature Match Newton Iteration Permutation Matrix
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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