Classification, Parameter Estimation and State Estimation: An Engineering Approach Using MATLAB

A practical introduction to intelligent computer vision theory, design, implementation, and technology

The past decade has witnessed epic growth in image processing and intelligent computer vision technology. Advancements in machine learning methods especially among adaboost varieties and particle filtering methods have made machine learning in intelligent computer vision more accurate and reliable than ever before. The need for expert coverage of the state of the art in this burgeoning field has never been greater, and this book satisfies that need. Fully updated and extensively revised, this 2nd Edition of the popular guide provides designers, data analysts, researchers and advanced post–graduates with a fundamental yet wholly practical introduction to intelligent computer vision. The authors walk you through the basics of computer vision, past and present, and they explore the more subtle intricacies of intelligent computer vision, with an emphasis on intelligent measurement systems. Using many timely, real–world examples, they explain and vividly demonstrate the latest developments in image and video processing techniques and technologies for machine learning in computer vision systems, including:

PRTools5 software for MATLAB especially the latest representation and generalization software toolbox for PRTools5 Machine learning applications for computer vision, with detailed discussions of contemporary state estimation techniques vs older content of particle filter methods The latest techniques for classification and supervised learning, with an emphasis on Neural Network, Genetic State Estimation and other particle filter and AI state estimation methods All new coverage of the Adaboost and its implementation in PRTools5.

A valuable working resource for professionals and an excellent introduction for advanced–level students, this 2nd Edition features a wealth of illustrative examples, ranging from basic techniques to advanced intelligent computer vision system implementations. Additional examples and tutorials, as well as a question and solution forum, can be found on a companion website.

Ferdinand van der Heijden, Ph.D., is on the faculty of theDepartment of Signals and Systems, University of Twente, Netherlands. Robert P.W. Duin, is on the faculty of Electrical Engineering, Mathematics and Computer Science, Pattern Recognition Laboratory, Delft University of Technology, Netherlands.  Dr. Dick de Ridder is Professor at the Bioinformatics lab at Wageningen University, Netherlands. David M. J. Tax, is a researcher with the Pattern Recognition laboratory, Delft University of Technology. Drs. Bangjun Lei, Guangzhu Xu,  and Ming Feng are with The Institute of Intelligent Vision and Image Information, China Three Gorges University, China.