This tutorial presents a hands-on view of the field of multi-view stereo with a focus on
practical algorithms. Multi-view stereo algorithms are able to construct highly detailed 3D …

Optimization methods are at the core of many problems in signal/image processing,
computer vision, and machine learning. For a long time, it has been recognized that looking …

Combinatorial optimization problems are typically tackled by the branch-and-bound
paradigm. We propose a new graph convolutional neural network model for learning branch …

In recent years, airborne and spaceborne hyperspectral imaging systems have advanced in
terms of spectral and spatial resolution, which makes the data sets they produce a valuable …

Optical flow estimation is one of the oldest and still most active research domains in
computer vision. In 35 years, many methodological concepts have been introduced and …

We present a system to automatically generate natural language descriptions from images.
This system consists of two parts. The first part, content planning, smooths the output of …

A general framework for constructing and using probabilistic models of complex systems that
would enable a computer to use available information for making decisions. Most tasks …

Humans perceive the three-dimensional structure of the world with apparent ease. However,
despite all of the recent advances in computer vision research, the dream of having a …

Compressed sensing aims to undersample certain high-dimensional signals yet accurately
reconstruct them by exploiting signal characteristics. Accurate reconstruction is possible …

Many real-world applications produce networked data such as the world-wide web
(hypertext documents connected via hyperlinks), social networks (for example, people …