RI:Small:Robust Image Matching with Deformations and Lighting Variation

该项目是开发新的，有效的距离度量比较两个图像。这些指标解释了两种影响。首先，像素可以改变它们的位置，从一个图像变形到另一个图像。其次，像素可能会改变其强度。在许多视觉问题中，强度变化主要是由于光线变化造成的。研究小组首先研究了光照变化的影响，这使得能够开发出一种新的、强大的、稳健的距离来测量图像中光照变化的影响。研究小组将这一点与现有和新的方法相结合，开发出一种稳健的距离，同时考虑到图像变形和强度变化。计算此距离可将这两种效果分开，从而提供图像之间的对应关系。这可以用来跟踪相对于光线移动的对象，匹配在一天中不同时间拍摄的图像，或者识别在不同照明下从不同角度看到的具有不同形状的对象。这一新的度量提供了一种计算变形和光照的理论，它编码了我们对图像相似性的概念。然而，寻找有效计算这种图像度量的方法仍然是一个相当大的挑战。因此，研究小组还基于这一新指标开发了计算效率高的算法。这些算法提高了人脸识别、自主导航、光流和跟踪等众多应用的性能，在这些应用中，光照和形状的变化对现有方法造成了重大挑战。

This project is to develop new, effective distance metrics for comparing two images. These metrics account for two effects. First, pixels can change their position, deforming from one image to another. Second, pixels may change their intensity. In many vision problems, intensity changes are primarily due to lighting variation. The research team first addresses the effect of illumination changes, which enables to develop a new, powerful, robust distance for measuring the effects of lighting variation in an image. The research team combines this with both existing and new methods to develop a robust distance that accounts simultaneously for image deformations and intensity variations. Computing this distance separates these two effects, providing a correspondence between images. This can be used to track objects moving relative to a light, to match images taken at different times of day, or to recognize objects seen under different lighting, from different viewpoints, with variations in their shape.This new metric provides a theory of computation for deformation and lighting that encodes our notion of image similarity. However, it is still a considerable challenge to find ways to effectively compute with such an image metric. Therefore, the research team also develops computationally effective algorithms based on this new metric. These algorithms improve performance in numerous applications such as face recognition, autonomous navigation, and optical flow and tracking, in which variations in lighting and shape cause significant challenges for existing methods.