Context Understanding in Computer Vision: A Survey

Computer Vision and Image Understanding

Learning in Computer Vision and Image Understanding

Using learning in segmentation or recognition tasks has several advantages over classical model-based techniques. These include adaptivity to noise and changing environments, as well as in many cases, a simplified system generation procedure. Yet, learning from examples introduces a new challenge getting a representative data set of examples from which to learn. Applications of learning systems...

A Historical Survey of Geometric Computer Vision

This short paper accompanies an invited lecture on a historical survey of geometric computer vision problems. It presents some early works on image-based 3D modeling, multi-view geometry, and structurefrom-motion, from the last three centuries. Some of these are relatively well known to photogrammetrists and computer vision researchers whereas others seem to have been largely forgotten or overl...

Markov Random Field modeling, inference & learning in computer vision & image understanding: A survey

In this paper, we present a comprehensive survey of Markov Random Fields (MRFs) in computer vision and image understanding, with respect to the modeling, the inference and the learning. While MRFs were introduced into the computer vision field about two decades ago, they started to become a ubiquitous tool for solving visual perception problems around the turn of the millennium following the em...

A Survey of Distributed Computer Vision Algorithms

Recent years have seen great advances in computer vision research dealing with large numbers of cameras. However, many multi-camera computer vision algorithms assume that the information from all cameras is losslessly communicated to a central processor that solves the problem. This assumption is unrealistic for emerging wireless camera networks, which may contain processors with limited capabi...