Image motion in computer vision: Variational processing and experimental verification

本研究的目的是提供一个共同的算法框架下的变分范式的基本主题的图像运动处理在计算机视觉。主题是运动估计，检测，跟踪和三维解释。运动估计从图像的时空变化来估计光学速度场。运动检测确定图像中被运动对象覆盖的区域，即，它将图像分割成运动对象的前景及其补充。跟踪是通过图像序列跟踪移动对象的过程，并且三维（3D）解释恢复产生图像运动的空间中的对象的形状和运动。变分方法从优化的角度来表述问题，即，作为目标函数的优化，该目标函数表示表征期望解的所有约束。因此，他们可以计算非常准确的解决方案，并有效地做到这一点。尽管其明显的优势，变分方法还没有被系统地研究在运动分析中，目前有许多重要的问题，已经解决了传统的启发式处理方案。这项研究将调查目前处理的问题，非变分方法，但也将解决重要的开放问题。调查将包括每个问题的变分分析，其中包括目标泛函的设计和有关其优化的有效手段的调查，如参数图切割和通过水平集的变分演算。将通过使用来自免费访问的数据库的图像序列进行详细的比较实验来验证所得到的配方，该数据库专用于验证图像运动处理算法。这项研究的预期成果是一个统一的机构的高效，易于使用，并有据可查的运动分析算法。

The purpose of this research is to provide a common algorithmic framework under the variational paradigm of the fundamental subjects of image motion processing in computer vision. The subjects are motion estimation, detection, tracking, and three-dimensional interpretation. Motion estimation evaluates the optical velocity field from the image spatiotemporal variations. Motion detection determines the region in an image covered by the moving objects, i.e., it segments the image into a foreground of moving objects and its complement. Tracking is the process of following moving objects through an image sequence, and three-dimensional (3D) interpretation recovers the shape and motion of the objects in space which produced the image motion. Variational methods formulate problems from an optimization viewpoint, i.e., as the optimization of an objective functional which expresses all of the constraints that characterize the desired solution. As a result, they can compute very accurate solutions and do so efficiently. In spite of their manifest advantage, variational methods have not been systematically investigated in motion analysis as there are currently many important problems that have been addressed by traditional heuristic processing schemes. This research will investigate problems currently treated by non-variational methods but will also address important open problems. The investigations will consist of a variational analysis of each problem, which includes the design of the objective functional and an inquiry about efficient means of its optimization, such as parametric graph cuts and the calculus of variations via level sets. The resulting formulations will be verified by detailed comparative experimentation using image sequences from freely accessible databases dedicated to the validation of image motion processing algorithms. The anticipated outcome of this research is a unified body of efficient, easily usable, and well documented algorithms for motion analysis.