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Affine image registration guided by particle filter

机译：仿射图像配准的粒子滤波

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摘要

Image registration is a central task to different applications, such as medical image analysis, biomedical systems, stereo computer vision and optical flow estimation. There are many methods described in the literature for resolving this task, but they are mainly based on the minimisation of some cost function. These methods, depending on the complexity of the function to optimise, use different strategies for localising a minimum which explain the alignment between images or volumes, such as linearising the cost function or using multiscale spaces. In this work, a particle filter method, also known as sequential Monte Carlo strategy, is proposed to settle these difficulties by estimating the probability distribution function (PDF) of the parameters of affine transformations. Using the reconstructed PDF, it is possible to obtain an accurate estimation of the transformation parameters in order to register unimodal and multimodal data. The proposed method proved to be robust to noise, partial data and initialising parameters. A set of evaluation experiments also showed that the method is easy to implement, and competitive to estimate affine parameters in two-dimensional (2D) and 3D.
机译：图像配准是不同应用程序的中心任务，例如医学图像分析，生物医学系统，立体计算机视觉和光流估计。文献中描述了许多解决此任务的方法，但是它们主要基于最小化某些成本函数。这些方法取决于优化函数的复杂性，使用不同的策略来定位最小值，这说明了图像或体积之间的对齐方式，例如线性化成本函数或使用多尺度空间。在这项工作中，提出了一种粒子滤波方法，也称为顺序蒙特卡洛策略，通过估计仿射变换参数的概率分布函数（PDF）来解决这些难题。使用重构的PDF，可以获取转换参数的准确估计，以便注册单峰和多峰数据。所提出的方法被证明对噪声，部分数据和初始化参数具有鲁棒性。一组评估实验还表明，该方法易于实现，并且在估计二维（2D）和3D仿射参数方面具有竞争力。

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