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Informationen zum Autor Boguslaw Cyganek received his M.Sc. degree in electronics in 1993, then in computer science in 1996 from the AGH University of Science and Technology, Krakow, Poland. He obtained his Ph.D. degree cum laude in 2001 with a thesis on correlation of stereo images, and D.Sc. degree in 2011 with a thesis on methods and algorithms of object recognition in digital images. During the recent years, Dr. Boguslaw Cyganek has been cooperating with many scientific centers in development of computer vision systems. He has also gained several years of practical experience working as a Software Development Manager and a Senior Software Engineer both in the USA and Poland. He is currently a researcher and lecturer at the Department of Electronics, AGH University of Science and Technology. His research interests include computer vision, pattern recognition, as well as development of programmable devices and embedded systems. He is an author or a co-author of over eighty conference and journal papers and four books including An Introduction to 3D Computer Vision Techniques and Algorithms published by Wiley. Dr. Cyganek is a member of the IEEE, IAPR and SIAM. Klappentext Object detection, tracking and recognition in images are key problems in computer vision. This book provides the reader with a balanced treatment between the theory and practice of selected methods in these areas to make the book accessible to a range of researchers, engineers, developers and postgraduate students working in computer vision and related fields.Key features:* Explains the main theoretical ideas behind each method (which are augmented with a rigorous mathematical derivation of the formulas), their implementation (in C++) and demonstrated working in real applications.* Places an emphasis on tensor and statistical based approaches within object detection and recognition.* Provides an overview of image clustering and classification methods which includes subspace and kernel based processing, mean shift and Kalman filter, neural networks, and k-means methods.* Contains numerous case study examples of mainly automotive applications.* Includes a companion website hosting full C++ implementation, of topics presented in the book as a software library, and an accompanying manual to the software platform. Zusammenfassung This book addresses key problems of computer vision (CV), focusing on the significant issues of object detection, tracking, and recognition in images, which are not found in other CV books. Throughout, the book balances theory, implementation, and case studies in order to provide a complete and accessible treatment of the topic. Inhaltsverzeichnis Preface xiii Acknowledgements xv Notations and Abbreviations xvii 1 Introduction 1 1.1 A Sample of Computer Vision 3 1.2 Overview of Book Contents 6 References 8 2 Tensor Methods in Computer Vision 9 2.1 Abstract 9 2.2 Tensor - A Mathematical Object 10 2.2.1 Main Properties of Linear Spaces 10 2.2.2 Concept of a Tensor 11 2.3 Tensor - A Data Object 13 2.4 Basic Properties of Tensors 15 2.4.1 Notation of Tensor Indices and Components 16 2.4.2 Tensor Products 18 2.5 Tensor Distance Measures 20 2.5.1 Overview of Tensor Distances 22 2.5.1.1 Computation of Matrix Exponent and Logarithm Functions 24 2.5.2 Euclidean Image Distance and Standardizing Transform 29 2.6 Filtering of Tensor Fields 33 2.6.1 Order Statistic Filtering of Tensor Data 33 2.6.2 Anisotropic Diffusion Filtering 36 2.6.3 IMPLEMENTATION of Diffusion Processes 40 2.7 Looking into Images with the Structural Tensor 44 2.7.1 Structural Tensor in Two-Dimensional Image Space 47 2.7.2 Spatio-Temporal Structural Tensor 50 2.7.3 Multichannel and Scale-Space Struct...
