Empirical Evolution Of Computer Vision Algorithms
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Empirical Evaluation Methods in Computer Vision
Author | : Henrik I. Christensen |
Publisher | : World Scientific |
Total Pages | : 170 |
Release | : 2002 |
Genre | : Science |
ISBN | : 9810249535 |
This book provides comprehensive coverage of methods for the empirical evaluation of computer vision techniques. The practical use of computer vision requires empirical evaluation to ensure that the overall system has a guaranteed performance. The book contains articles that cover the design of experiments for evaluation, range image segmentation, the evaluation of face recognition and diffusion methods, image matching using correlation methods, and the performance of medical image processing algorithms.
Empirical Evaluation of Computer Vision Algorithms
Author | : Patrick J. Flynn |
Publisher | : |
Total Pages | : 199 |
Release | : 2001 |
Genre | : Computer algorithms |
ISBN | : |
Empirical Evaluation Techniques in Computer Vision
Author | : Kevin W. Bowyer |
Publisher | : Wiley-IEEE Computer Society Press |
Total Pages | : 272 |
Release | : 1998-07-11 |
Genre | : Computers |
ISBN | : |
Empirical Evaluation Techniques in Computer Vision presents methods that allow comparative assessment of algorithms and the accompanying benefits: places computer vision on solid experimental and scientific grounds, assists the development of engineering solutions to practical problems, allows accurate assessments of computer vision research, provides convincing evidence that computer vision research results in practical solutions. The chapters in this volume cover the three main paradigms for evaluating computer vision algorithms. The paradigms are: (1) evaluations that are independently administered, (2) evaluation of a set of algorithms by one research group, and (3) evaluation methods that feature ground truthing procedures as a major component.
Evolutionary Computer Vision
Author | : Gustavo Olague |
Publisher | : Springer |
Total Pages | : 432 |
Release | : 2016-09-28 |
Genre | : Computers |
ISBN | : 3662436930 |
This book explains the theory and application of evolutionary computer vision, a new paradigm where challenging vision problems can be approached using the techniques of evolutionary computing. This methodology achieves excellent results for defining fitness functions and representations for problems by merging evolutionary computation with mathematical optimization to produce automatic creation of emerging visual behaviors. In the first part of the book the author surveys the literature in concise form, defines the relevant terminology, and offers historical and philosophical motivations for the key research problems in the field. For researchers from the computer vision community, he offers a simple introduction to the evolutionary computing paradigm. The second part of the book focuses on implementing evolutionary algorithms that solve given problems using working programs in the major fields of low-, intermediate- and high-level computer vision. This book will be of value to researchers, engineers, and students in the fields of computer vision, evolutionary computing, robotics, biologically inspired mechatronics, electronics engineering, control, and artificial intelligence.
Machine Learning in Computer Vision
Author | : Nicu Sebe |
Publisher | : Springer Science & Business Media |
Total Pages | : 253 |
Release | : 2005-10-04 |
Genre | : Computers |
ISBN | : 1402032757 |
The goal of this book is to address the use of several important machine learning techniques into computer vision applications. An innovative combination of computer vision and machine learning techniques has the promise of advancing the field of computer vision, which contributes to better understanding of complex real-world applications. The effective usage of machine learning technology in real-world computer vision problems requires understanding the domain of application, abstraction of a learning problem from a given computer vision task, and the selection of appropriate representations for the learnable (input) and learned (internal) entities of the system. In this book, we address all these important aspects from a new perspective: that the key element in the current computer revolution is the use of machine learning to capture the variations in visual appearance, rather than having the designer of the model accomplish this. As a bonus, models learned from large datasets are likely to be more robust and more realistic than the brittle all-design models.