Classification, Parameter Estimation and State Estimation
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.
Professor Bangjun Lei, Dr. Guangzhu Xu, Dr. Ming Feng, Dr. Yaobin Zou, Dr. F. van der Heijden, Professor Dick de Ridder, Dr. David M. J. Tax
Classification, Parameter Estimation and State Estimation
Engineering disciplines are those fields of research and development that attempt to create products and systems operating in, and dealing with, the real world. The number of disciplines is large, as is the range of scales that they typically operate in: from the very small scale of nanotechnology up to very large scales that span whole regions, for example water management systems, electric power distribution systems or even global systems (e.g. the global positioning system, GPS). The level of advancement in the fields also varies wildly, from emerging techniques (again, nanotechnology) to trusted techniques that have been applied for centuries (architecture, hydraulic works). Nonetheless, the disciplines share one important aspect: engineering aims at designing and manufacturing systems that interface with the world around them.
Systems designed by engineers are often meant to influence their environment: to manipulate it, to move it, to stabilize it, to please it, and so on. To enable such actuation, these systems need information, for example values of physical quantities describing their environments and possibly also describing themselves. Two types of information sources are available: prior knowledge and empirical knowledge. The latter is knowledge obtained by sensorial observation. Prior knowledge is the knowledge that was already there before a given observation became available (this does not imply that prior knowledge is obtained without any observation). The combination of prior knowledge and empirical knowledge leads to posterior knowledge.
The sensory subsystem of a system produces measurement signals. These signals carry the empirical knowledge. Often, the direct usage of these signals is not possible, or is inefficient. This can have several causes:
The information in the signals is not represented in an explicit way. It is often hidden and only available in an indirect, encoded, form.
Measurement signals always come with noise and other hard-to-predict disturbances.
The information brought forth by posterior knowledge is more accurate and more complete than information brought forth by empirical knowledge alone. Hence, measurement signals should be used in combination with prior knowledge.
Measurement signals need processing in order to suppress the noise and to disclose the information required for the task at hand.
1.1 The Scope of the Book
In a sense, classification and estimation deal with the same problem: given the measurement signals from the environment, how can the information that is needed for a system to operate in the real world be inferred? In other words, how should the measurements from a sensory system be processed in order to bring maximal information in an explicit and usable form? This is the main topic of this book.
Good processing of the measurement signals is possible only if some knowledge and understanding of the environment and the sensory system is present. Modelling certain aspects of that environment - like objects, physical processes or events - is a necessary task for the engineer. However, straightforward modelling is not always possible. Although the physical sciences provide ever deeper insight into nature, some systems are still only partially understood; just think of the weather. Even if systems are well understood, modelling them exhaustively may be beyond our current capabilities (i.e. computer power) or beyond the scope of the application. In such cases, approximate general models, but adapted to the system at hand, can be applied. The development of such models is also a topic of this book.
The title of the book already indicates the three main subtopics it will cover: classification, parameter estimation and state estimation. In classification, o