Showing papers by "Shai Avidan published in 2002"
11 Aug 2002
TL;DR: A simple and effective technique for obtaining invariance to image-plane transformations within a linear dimensionality reduction approach is demonstrated.
Abstract: Manifold pursuit extends principal component analysis to be invariant to a desired group of image-plane transformations of an ensemble of un-aligned images. We derive a simple technique for projecting a misaligned target image onto the linear subspace defined by the superpositions of a collection of model images. We show that it is possible to generate a fixed projection matrix which would separate the projected image into the aligned projected target and a residual image which accounts for the mis-alignment. An iterative procedure is then introduced for eliminating the residual image and leaving the correct aligned projected target image. Taken together, we demonstrate a simple and effective technique for obtaining invariance to image-plane transformations within a linear dimensionality reduction approach.
72 citations
28 May 2002
TL;DR: Eigensegments combine image segmentation and Principal Component Analysis (PCA) to obtain a spatio-temporal decomposition of an ensemble of images that gives better classification results, gives smaller reconstruction errors, can handle local changes in appearance and is faster to compute.
Abstract: Eigensegments combine image segmentation and Principal Component Analysis (PCA) to obtain a spatio-temporal decomposition of an ensemble of images. The image plane is spatially decomposed into temporally correlated regions. Each region is independently decomposed temporally using PCA. Thus, each image is modeled by several low-dimensional segment-spaces, instead of a single high-dimensional image-space. Experiments show the proposed method gives better classification results, gives smaller reconstruction errors, can handle local changes in appearance and is faster to compute. Results for faces and vehicles are shown.
7 citations