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Handbook of pattern recognition and computer vision (5th ed.)
Chen C., World Scientific Publishing Co, Inc., River Edge, NJ, 2016. 560 pp. Type: Book
Date Reviewed: Mar 10 2016

Pattern recognition and computer vision have very broad applications, and technologies in the field are evolving rapidly. It is great that the editor brought this book up to date in the 5th edition with the latest developments in the field.

The book has three parts: “Pattern Recognition Methods and Applications,” “Computer Vision and Image Processing,” and “System, Architecture, and Technology.” Part 1 is divided into ten chapters. In chapter 1.1, syntactic pattern recognition issues are presented, which slow down the development of syntactic pattern recognition. Chapter 1.2 introduces “deep discriminative and generative models for speech pattern recognition.” Chapter 1.3 presents “a general issue concerning the comparison of performances of a classifier that has been trained in different kinds of ways.” Chapter 1.4 explores the k-nearest neighbors (k-nn) approach in the framework of information theoretic clustering. Chapter 1.5 talks about pruning trees in random forests, making the classification of medical images more reliable. In chapter 1.6, the optimum-path forest (OPF) classifier is presented. Chapter 1.7 focuses on the curvelet-based texture features. Chapter 1.8 discusses the coin recognition problem, and chapter 1.9 is dedicated to underwater live fish recognition. Model adaptation for personalized music emotion recognition is discussed in chapter 1.10.

Part 2 has ten chapters. Chapter 2.1 describes unified context-assisted methods from the perspective of two-person identification tasks. Chapter 2.2 gives a review on “statistical shape spaces for 3D data.” Chapter 2.3 presents multiple target tracking without appearance descriptors. Chapter 2.4 “proposes to augment the current data-driven visual learning methods with knowledge of different types from different sources to improve different computer vision tasks.” Chapter 2.5 proposes a new approach on graph distance to speed up graph matching. Chapter 2.6 focuses on the long short-term memory (LSTM) neural network for document image analysis. Chapter 2.7 covers “the use of multilevel filtering based on hierarchical representations of the image for land cover classification.” Chapter 2.8 introduces “manifold-based sparse representation [algorithms] for hyperspectral image classification.” Chapter 2.9 gives “a review of texture classification methods and ... applications in medical image analysis of the brain.” Chapter 2.10 discusses 3D tomosynthesis to detect breast cancer.

Part 3 includes nine chapters. In chapter 3.1, several representations are combined to improve sketch recognition. Chapter 3.2 achieves image retrieval with a multiple kernel learning algorithm. Chapter 3.3 presents the face identification problem in video streams. Chapter 3.4 describes the development of pattern recognition methodologies for fuel cell applications. Chapter 3.5 talks about “outdoor shadow modeling and its applications.” Chapter 3.6 focuses on model-free tracking. Chapter 3.7 presents “using 3D vision for automated industrial inspection.” Chapter 3.8 discusses the challenges in imaged-based barcode reading, and chapter 3.9 is dedicated to “parallel pattern matching using the automata processor.”

The chapters are organized to cover the theory, algorithms, and applications of pattern recognition and computer vision. The book offers cutting-edge techniques that are presented very clearly. Furthermore, each chapter (paper) provides a rich number of references for readers. Although this is not a book for undergraduate students, it’s a great book for graduate students and researchers. Readers do not have to read the chapters one by one; they could just directly jump into the ones they are interested in based on their research interests. For graduate students pursuing PhDs and professionals doing research and development in the pattern recognition and computer vision field, this is a book you shouldn’t miss.

Reviewer:  Zhaoqiang Lai Review #: CR144230 (1605-0302)
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Vision And Scene Understanding (I.2.10 )
 
 
General (I.5.0 )
 
 
Reference (A.2 )
 
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