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.
翻译:自由式变形模型可以通过对图像的控制点板状进行操纵,代表一系列广泛的非硬化变形。然而,由于参数众多,由于健身环境的复杂性,将自由式变形模型直接与变形图像相适应以进行变形估计具有挑战性。在本文中,根据受每个控制点影响的区域相互重叠这一事实,我们将登记任务作为一个多目标优化问题(MOP),具体来说,将模板图像分割到几个区域,独立测量每个区域的相似性,从而建立多种目标,通过用现成的多目标进化算法(MOEAs)解决缔约方会议问题,从而可以实现变形估计。此外,通过图像金字塔结合控制点网状次配置,实现了粗化至软化战略。具体地说,当前图像水平的最佳候选解决方案由下一个级别继承,这提高了应对大规模变形的能力。此外,还提议了后处理程序,以便利用Pareto最佳用途模型来生成单一产出。此外,还进行了合成和合成方法的对比,以合成方式展示了我们最佳的变形方法。