Front-end vision and multi-scale image analysis: multi-scale computer vision theory and applications, written in Mathematica

Front-End Vision and Multi-Scale Image A

📁 Front-End Vision and Multi-Scale Image Analysis. Multi-Scale Computer Vision Theory and Applications, written in Mathematica

✍ Ter Haar Romeny B.M. 📂 Library 🌐 English

Издательство Springer, 2003, -470 pp.<div class="bb-sep"></div>Scale is not an important parameter in computer vision research. It is an essential parameter. It is an immediate consequence of the process of observation, of measurements. This book is about scale, and its fundamental notion in compute

Front-End Vision and Multi-Scale Image A

📁 Front-End Vision and Multi-Scale Image Analysis: Multi-Scale Computer Vision Theory and Applications, written in Mathematics

✍ Prof. Dr. Bart M. ter Haar Romeny (auth.) 📂 Library 📅 2003 🏛 Springer Netherlands 🌐 English

<p>Many approaches have been proposed to solve the problem of finding the optic flow field of an image sequence. Three major classes of optic flow computation techniques can discriminated (see for a good overview Beauchemin and Barron IBeauchemin19951): gradient based (or differential) methods; phas

Front-End Vision and Multi-Scale Image A

📁 Front-End Vision and Multi-Scale Image Analysis: Multi-Scale Computer Vision Theory and Applications, written in Mathematics

✍ Prof. Dr. Bart M. ter Haar Romeny (auth.) 📂 Library 📅 2003 🏛 Springer Netherlands 🌐 English

<p>Many approaches have been proposed to solve the problem of finding the optic flow field of an image sequence. Three major classes of optic flow computation techniques can discriminated (see for a good overview Beauchemin and Barron IBeauchemin19951): gradient based (or differential) methods; phas

Robust Computer Vision: Theory and Appli

📁 Robust Computer Vision: Theory and Applications (Computational Imaging and Vision)

✍ N. Sebe, M.S. Lew 📂 Library 📅 2003 🏛 Springer 🌐 English

From the foreword by Thomas Huang: "During the past decade, researchers in computer vision have found that probabilistic machine learning methods are extremely powerful. This book describes some of these methods. In addition to the Maximum Likelihood framework, Bayesian Networks, and Hidden Mark

Inverse Problems, Multi-Scale Analysis,

📁 Inverse Problems, Multi-Scale Analysis, and Effective Medium Theory

✍ Ammari H., Kang H. (eds.) 📂 Library 📅 2006 🏛 AMS 🌐 English

Scale-Space Theory in Computer Vision

📁 Scale-Space Theory in Computer Vision

✍ Tony Lindeberg (auth.) 📂 Library 📅 1994 🏛 Springer US 🌐 English

<p>The problem of scale pervades both the natural sciences and the vi sual arts. The earliest scientific discussions concentrate on visual per ception (much like today!) and occur in Euclid's (c. 300 B. C. ) Optics and Lucretius' (c. 100-55 B. C. ) On the Nature of the Universe. A very clear accou