A general framework for low level vision

A general framework for low level vision

We introduce a new geometrical framework based on which natural flows for image scale space and enhancement are presented. We consider intensity images as surfaces in the (x, I) space. The image is, thereby, a two-dimensional (2-D) surface in three-dimensional (3-D) space for gray-level images, and 2-D surfaces in five dimensions for color images. The new formulation unifies many classical sche...

A General Framework for Low Level

| We introduce a new geometrical framework based on which natural ows for image scale space and enhancement are presented. We consider intensity images as surfaces in the (x; I) space. The image is thereby a 2D surface in 3D space for gray level images, and 2D surfaces in 5D for color images. The new formulation uniies many classical schemes and algorithms via a simple scaling of the intensity ...

Modular Integration for Low-level and High-level Vision Problems in a Multiresolution Framework

A computational framework and SIMD algorithms for low-level support of intermediate level vision processing

Computation on and among data sets mapped to irregular, non-uniform, aggregates of processing elements (PEs) is a very important, but largely ignored, problem in parallel vision processing. Associative processing 11] is an eeective means of applying parallel processing to these computations 33], but is often restricted to operating on one data set at a time. What we propose is an additional lev...

Learning Low-level Vision Learning Low-level Vision

We show a learning-based method for low-level vision problems{estimating scenes from images. We generate a synthetic world of scenes and their corresponding rendered images. We model that world with a Markov network, learning the network parameters from the examples. Bayesian belief propagation allows us to e ciently nd a local maximum of the posterior probability for the scene, given the image...