Implementation of Efficient Computer Vision Based on a Robust Subspace Method

从计算机视觉的角度研究了一种新的子空间方法框架。在这个框架中，特征空间是由一个归一化特征空间的齐次表示来表示的。，其中归一化表示11归一化。一个给定的图像的部分投影到一个给定的归一化特征空间，然后有效地由一个图像的线性投影到一个齐次特征空间，通过考虑所有有效像素。当将异常点检测模块与简单部分投影相结合时，可以将该简单公式扩展到鲁棒部分投影。有效的局部投影可以促进鲁棒人脸识别、实时人脸/目标跟踪和其他应用。在人脸识别中，为了实现自然环境下人脸识别的鲁棒性机制，引入了平行部分投影的概念。在实时跟踪中，为了实现模板与输入图像的有效匹配，引入了稀疏模板匹配的概念。在粒子滤波框架中利用稀疏模板匹配，实现了一种高效的跟踪算法。当特征模板事先给定时，稀疏模板匹配也可以推广到稀疏特征模板匹配，特征模板匹配也可以用于粒子滤波。研究中还探讨了其他一些扩展。

A novel framework of the subspace method is investigated from a viewpoint of computer vision. In this framework, an eigenspace is formulated by a homogeneous representation of a normalized eigenspace., where the normalization means 11-normalization. A partial projection of a given image onto a given normalized eigenspace is then efficiently made by a linear projection of the image onto a homogeneous eigenspace by taking all the effective pixels into account. This simple formulation can be extended to robust partial projection when an outlier detection module is combined with the simple partial projection.The efficient partial projection can facilitate robust face recognition, real-time face/object tracking, and other applications. In the face recognition, a concept of parallel partial projections is introduced for implementation of robust mechanism for the face recognition in natural environments. In the real-time tracking, a concept of sparse template matching is introduced for accomplishing an efficient matching of a template and an input image. When the sparse template matching is utilized in a framework of particle filter, a very efficient tracker is implemented. The sparse template matching can also be generalized to sparse eigen-template matching when the eigen-template is given in prior, and the eigentemplate matching can also be utilized in the particle filter. Some other extensions are also investigated in the research.