A commonly used representation of a visual pattern is the set of marginal probability distributions of the output of a bank of filters (Gaussian, Laplacian, Gabor etc...). This re...
We investigate the use of linear and nonlinear principal manifolds for learning low-dimensional representations for visual recognition. Three techniques: Principal Component Analy...
The need to examine and manipulate large surface models is commonly found in many science, engineering, and medical applications. On a desktop monitor, however, seeing the whole mo...
We develop a statistical framework for the simultaneous, unsupervised segmentation and discovery of visual object categories from image databases. Examining a large set of manuall...
Visual forms come in countless varieties, from the simplicity of a sphere, to the geometric complexity of a face, to the fractal complexity of a rugged coast. These varieties have ...