Image Deformation Estimation via Multiobjective Optimization

IEEE Access

Takumi Nakane¹  Chao Zhang¹  Haoran Xie² 

[1] Department of Engineering, University of Fukui, Fukui, Japan;Japan Advanced Institute of Science and Technology, Nomi, Japan;

关键词: Genetic algorithm;    image deformation estimation;    image registration;    multi-objective evolutionary algorithm;   

DOI  :  10.1109/ACCESS.2022.3174360

来源: DOAJ

【 摘 要 】

The free-form deformation model can represent a wide range of non-rigid deformations by manipulating a control point lattice over the image. However, due to a large number of parameters, it is challenging to fit the free-form deformation model directly to the deformed image for deformation estimation because of the complexity of the fitness landscape. In this paper, we cast the registration task as a multi-objective optimization problem (MOP) according to the fact that regions affected by each control point overlap with each other. Specifically, by partitioning the template image into several regions and measuring the similarity of each region independently, multiple objectives are built and deformation estimation can thus be realized by solving the MOP with off-the-shelf multi-objective evolutionary algorithms (MOEAs). In addition, a coarse-to-fine strategy is realized by image pyramid combined with control point mesh subdivision. Specifically, the optimized candidate solutions of the current image level are inherited by the next level, which increases the ability to deal with large deformation. Also, a post-processing procedure is proposed to generate a single output utilizing the Pareto optimal solutions. Comparative experiments on both synthetic and real-world images show the effectiveness and usefulness of our deformation estimation method.

【 授权许可】

Unknown