Computer Vision: Algorithms And Applications

Computer Vision: Algorithms and Applications (Repost)  eBooks & eLearning

Posted by step778 at May 23, 2017
Computer Vision: Algorithms and Applications (Repost)

Richard Szeliski, "Computer Vision: Algorithms and Applications"
2008 | pages: 655 | ISBN: 1848829345 | PDF | 14,1 mb

Computer Vision: Algorithms and Applications (repost)  eBooks & eLearning

Posted by tot167 at Dec. 7, 2010
Computer Vision: Algorithms and Applications (repost)

Richard Szeliski, "Computer Vision: Algorithms and Applications"
S.,er | 2010 | ISBN: 1848829345 | 812 pages | PDF | 43,6 MB

Computer Vision: Algorithms and Applications (draft)  eBooks & eLearning

Posted by tot167 at March 2, 2010
Computer Vision: Algorithms and Applications (draft)

Richard Szeliski, "Computer Vision: Algorithms and Applications (draft)"
Springer | 2010 | ISBN: 1848829345 | 630 pages | PDF | 14 MB

Ellipse Fitting for Computer Vision: Implementation and Applications  eBooks & eLearning

Posted by Underaglassmoon at June 4, 2017
Ellipse Fitting for Computer Vision: Implementation and Applications

Ellipse Fitting for Computer Vision: Implementation and Applications
Morgan & Claypool | English | 2016 | ISBN-10: 1627054588 | 141 pages | PDF | 1.86 mb

by Professor Department of Computer Science Kenichi Kanatani (Author)

Biologically Inspired Computer Vision: Fundamentals and Applications  eBooks & eLearning

Posted by AlenMiler at Dec. 26, 2015
Biologically Inspired Computer Vision: Fundamentals and Applications

Biologically Inspired Computer Vision: Fundamentals and Applications by Gabriel Cristobal
English | Nov. 16, 2015 | ISBN: 3527412646 | 480 Pages | AZW3/EPUB/PDF (conv) | 47.24 MB

As the state-of-the-art imaging technologies became more and more advanced, yielding scientific data at unprecedented detail and volume, the need to process and interpret all the data has made image processing and computer vision increasingly important.

Color in Computer Vision: Fundamentals and Applications (repost)  eBooks & eLearning

Posted by arundhati at Oct. 22, 2015
Color in Computer Vision: Fundamentals and Applications (repost)

Theo Gevers, Arjan Gijsenij, "Color in Computer Vision: Fundamentals and Applications"
English | 2012 | ISBN: 0470890843 | 384 pages | PDF | 6,4 MB

Color in Computer Vision: Fundamentals and Applications  eBooks & eLearning

Posted by interes at Aug. 2, 2013
Color in Computer Vision: Fundamentals and Applications

Color in Computer Vision: Fundamentals and Applications by Theo Gevers, Arjan Gijsenij, Joost van de Weijer and Jan-Mark Geusebroek
English | 2012 | ISBN: 0470890843 | 384 pages | PDF | 6,4 MB

While the field of computer vision drives many of today’s digital technologies and communication networks, the topic of color has emerged only recently in most computer vision applications. One of the most extensive works to date on color in computer vision, this book provides a complete set of tools for working with color in the field of image understanding.

Biologically Inspired Computer Vision: Fundamentals and Applications  eBooks & eLearning

Posted by ChrisRedfield at Jan. 21, 2017
Biologically Inspired Computer Vision: Fundamentals and Applications

Gabriel Cristobal, Laurent Perrinet, Matthias S. Keil - Biologically Inspired Computer Vision: Fundamentals and Applications
Published: 2015-11-16 | ISBN: 3527412646 | True PDF | 480 pages | 14.85 MB

Robust Computer Vision: Theory and Applications (Repost)  eBooks & eLearning

Posted by DZ123 at Nov. 23, 2014
Robust Computer Vision: Theory and Applications (Repost)

N. Sebe, Michael Lew, "Robust Computer Vision: Theory and Applications"
English | 2003 | ISBN: 1402012934 | PDF | pages: 234 | 3,1 mb

Robust Computer Vision: Theory and Applications (Repost)  eBooks & eLearning

Posted by step778 at Dec. 5, 2010
Robust Computer Vision: Theory and Applications (Repost)

Robust Computer Vision: Theory and Applications
Publisher: Springer | pages: 230 | 2003 | ISBN: 1402012934 | PDF | 3,1 mb

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 Markov models are also used. Three aspects are stressed: features, similarity metric, and models. Many interesting and important new results, based on research by the authors and their collaborators, are presented. Although this book contains many new results, it is written in a style that suits both experts and novices in computer vision.