Implementation of Efficient Computer Vision Based on a Robust Subspace Method

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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.