Title
Self-Adaptive Evolution Toward New Parameter Free Image Registration Methods
Abstract
Image registration (IR) is a challenging topic in both the computer vision and pattern recognition fields; its main aim is to find the optimal transformation to provide the best overlay or fitting between two or more images. Usually, the success of well-known algorithms, such as iterative closest point, highly depends on several assumptions, e.g., the user should provide an initial near-optimal pose of the images to be registered. In the last decade, a new family of registration algorithms based on evolutionary principles has been contributed in order to overcome the latter drawbacks. However, their performance highly depends on carefully tuning (usually by hand) the control parameters of the algorithm, which is an error-prone and a time-consuming task. In this paper, we propose a new self-adaptive evolution model to deal with IR problems. To our knowledge, this is the first time a self-adaptive approach has been used for tuning the control parameters of evolutionary algorithms tackling computer vision tasks. Specifically, we introduce a novel design of the proposed self-adaptive approach facing pair-wise range IR problem instances, which is a challenging real-world optimization problem. In addition, several classical approaches, as well as state-of-the-art evolutionary IR methods, have been considered for numerical comparison.
Year
DOI
Venue
2013
10.1109/TEVC.2012.2209890
IEEE Trans. Evolutionary Computation
Keywords
Field
DocType
parameter estimation,evolutionary computation,computer vision,image registration
Mathematical optimization,Evolutionary algorithm,Computer science,Evolutionary computation,Self adaptive,Artificial intelligence,Estimation theory,Overlay,Optimization problem,Machine learning,Image registration,Iterative closest point
Journal
Volume
Issue
ISSN
17
4
1089-778X
Citations 
PageRank 
References 
10
0.47
33
Authors
4
Name
Order
Citations
PageRank
Jose Santamaría113810.84
Sergio Damas236328.95
Oscar Cordón31572100.75
Agustín Escámez4111.20