Image registration using genetic algorithms | Zendy

Flávio Luiz Seixas | Zendy; Luiz Satoru Ochi | Zendy; Aura Conci | Zendy; Débora Muchaluat Saade | Zendy

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Image registration using genetic algorithms

Author(s) -

Flávio Luiz Seixas,

Luiz Satoru Ochi,

Aura Conci,

Débora Muchaluat Saade

Publication year - 2008

Publication title -

citeseer x (the pennsylvania state university)

Language(s) - English

Resource type - Conference proceedings

DOI - 10.1145/1389095.1389320

Subject(s) - affine transformation , image registration , point set registration , euclidean distance , similarity (geometry) , computer science , artificial intelligence , point (geometry) , matching (statistics) , k nearest neighbors algorithm , affine shape adaptation , harris affine region detector , image (mathematics) , pattern recognition (psychology) , set (abstract data type) , noise (video) , algorithm , genetic algorithm , euclidean geometry , computer vision , mathematics , affine combination , machine learning , statistics , geometry , pure mathematics , programming language

This paper addresses the image registration problem applying genetic algorithms. The image registration's objective is the definition of a mapping that best match two set of points or images. In this work the point matching problem was addressed employing a method based on nearest-neighbor. The mapping was handled by affine transformations. Experiments were conducted using three 2D synthetic point-sets with different affine transformations and noise. The results were compared against other optimization techniques. The similarity of two point-sets is measured using the Euclidean distance between matched points.

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