A Probabilistic Bitwise Genetic Algorithm for B-Spline based Image Deformation Estimation
This work addresses a classical trade-off in image deformation for computer vision applications, but it appears incremental as it modifies existing genetic algorithm operations.
The authors tackled the image deformation estimation problem by developing a probabilistic bitwise genetic algorithm to preserve genetic diversity, achieving better coverage of the solution space and demonstrating effectiveness on synthetic data.
We propose a novel genetic algorithm to solve the image deformation estimation problem by preserving the genetic diversity. As a classical problem, there is always a trade-off between the complexity of deformation models and the difficulty of parameters search in image deformation. 2D cubic B-spline surface is a highly free-form deformation model and is able to handle complex deformations such as fluid image distortions. However, it is challenging to estimate an apposite global solution. To tackle this problem, we develop a genetic operation named probabilistic bitwise operation (PBO) to replace the crossover and mutation operations, which can preserve the diversity during generation iteration and achieve better coverage ratio of the solution space. Furthermore, a selection strategy named annealing selection is proposed to control the convergence. Qualitative and quantitative results on synthetic data show the effectiveness of our method.