Open Access
A Robust Point Matching Algorithm Based on Particle Swarm Optimization
Zhang Ming-ju
- 01 Jan 2004
10
TL;DR: This paper proposes an accurate and robust algorithm for solving the point matching problem using particle swarm optimization, and is able to combine the estimation of both spatial mapping parameters and matching matrix between the two point-sets.
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Abstract: The matching of two point-sets plays an important role in computer vision, pattern recognition and medicine diagnose. In this paper, we propose an accurate and robust algorithm for solving the point matching problem using particle swarm optimization. At first, an energy function describing the problem is defined. Secondly, PSO is used to minimize the above energy function, and then we are able to combine the estimation of both spatial mapping parameters and matching matrix between the two point-sets. The experimental results demonstrate the algorithm is simple and reliable, and avoids local extrema.
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Citations
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Block matching algorithm based on Harmony Search optimization for motion estimation
TL;DR: In this article, a new BM algorithm that combines Harmony Search (HS) with a fitness approximation model is proposed, which uses motion vectors belonging to the search window as potential solutions, and evaluates the matching quality of each motion vector candidate.
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Image matching based on improved particle swarm optimization
YongFang Guo,YiCai Sun +1 more
- 03 Nov 2011
TL;DR: The paper proposed a new image matching algorithm based on improved particle swarm optimization, which can overcome the shortcoming of traditional matching for computering the fitness for every pixel in the searching space and results showed that the method could get high matching precision and not sensitive to noise while it can drastically reduce the computation time.
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Object Tracking Using Point Matching Based on MCMC
Yang Hongbo,Hou Xia +1 more
- 31 Mar 2009
TL;DR: A new object tracking base on MCMC point matching method which applies MCMC algorithm to solve the posterior probability distribution problem and obtain the optimal matching parameters include position, rotation, scale information.
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Application of Particle Swarm Optimization in High-precision Fundamental Matrix Estimation
TL;DR: The fundamental matrix that is recovered by the use of the characteristic points of the images collected by camera will be solved with corresponding matching points best matching points are gained.
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