Table Of ContentHANDBOOK OF
PATTERN RECOGNITION
AND COMPUTER VISION
6th Edition
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HANDBOOK OF
PATTERN RECOGNITION
AND COMPUTER VISION
6th Edition
editor
C H Chen
University of Massachusetts Dartmouth, USA
World Scientific
NEW JERSEY • LONDON • SINGAPORE • BEIJING • SHANGHAI • HONG KONG • TAIPEI • CHENNAI • TOKYO
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HANDBOOK OF PATTERN RECOGNITION AND COMPUTER VISION
Sixth Edition
Copyright © 2020 by World Scientific Publishing Co. Pte. Ltd.
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ISBN 978-981-121-106-5 (hardcover)
ISBN 978-981-121-107-2 (ebook for institutions)
ISBN 978-981-121-108-9 (ebook for individuals)
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The book is dedicated to the memory of the following pioneers
of pattern Recognition and computer vision
Prof. K.S. Fu,
Dr. Pierre A. Devijver,
Prof. Azriel Rosenfeld,
Prof. Thomas M. Cover,
Dr. C.K. Chow,
Prof. Roger Mohr, and
Prof. Jack Sklansky
v
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PREFACE TO THE 6TH EDITION
Motivated by the re-emergence of artificial intelligence, big data, and machine
learning in the last six years that have impacted many areas of pattern recognition
and computer vision, this new edition of Handbook is intended to cover both new
developments involving deep learning and more traditional approaches. The book
is divided into two parts: part 1 on theory and part 2 on applications.
Statistical pattern recognition is of fundamental importance to the
development of pattern recognition. The book starts with Chapter 1.1, Optimal
Statistical Classification, by Profs. Dougherty and Dalton, that reviews the
optimal Bayes classifier in a broader context than an optimal classifier with
unknown feature-label distribution designed from sample data, an optimal
classifier that possess minimal expected error relative to the posterior, etc.
Though optimality includes a degree of subjectivity, it always incorporates the
aim and knowledge of the designer. The chapter also deals with the topic of
optimal Bayesian transfer learning, where the training data are augmented with
data from a different source. From my observation of the last half century, I
must say that it is amazing that the Bayesian theory of inferences has such a long
lasting value. Chapter 1.2 by Drs. Shi and Gong on Deep Discrimitive Feature
Learning Methods for Object Recognition, presents the entropy-orthogonality
loss and Min-Max loss to improve the within-class compactness and between-
class separability of the convolutional neural network classifier for better object
recognition. Chapter 1.3 by Prof. Bouwmans et al. on Deep Learning Based
Background Subtraction: A Systematic Survey, provides a full review of recent
advances on the use of deep neural networks applied to background subtraction
for detection of moving objects in video taken by a static camera. The readers
may be interested to read a related chapter on Statistical Background Modeling
for Foreground Detection: A Survey, also by Prof. Bouwmans, et al. in the 4th
edition of the handbook series. Chapter 1.4 by Prof. Ozer, on Similarity Domains
Network for Modeling Shapes and Extracting Skeleton Without Large Datasets,
introduces a novel shape modeling algorithm, Similarity Domain Network
(SDN), based on Radial Basis Networks which are a particular type of neural
networks that utilize radial basis function as an activation function in the hidden
layer. The algorithm effectively computes similarity domains for shape modeling
and skeleton extraction using only one image sample as data.
As a tribute to Prof. C.C. Li who recently retired from University of
Pittsburgh after over 50 years of dedicated research and teaching in pattern
recognition and computer vision, his chapter in the 5th edition of the handbook
vii
viii Preface
series is revised as Chapter 1.5 entitled, On Curvelet-Based Texture Features for
Pattern Classification. The chapter provides a concise introduction to the
curvelet transform which is still a relatively new method for sparse representation
of images with rich edge structure. The curvelet-based texture features are very
useful for the analysis of medical MRI organ tissue images, classification of
critical Gleason grading of prostate cancer histological images and other medical
as well as non-medical images. Chapter 1.6 by Dr. Wang is entitled, An Overview
of Efficient Deep Learning on Embedded Systems. It is now evident that the
superior accuracy of deep learning neural networks comes from the cost of high
computational complexity. Implementing deep learning on embedded systems
with limited hardware resources is a critical and difficult problem. The chapter
reviews some of the methods that can be used to improve energy efficiency
without sacrificing accuracy within cost-effective hardware. The quantization,
pruning, and network structure optimization issues are also considered. As
pattern recognition needs to deal with complex data such as data from different
sources, as for autonomous vehicles for example, or from different feature
extractors, learning from these types of data is called multi-view learning and
each modality/set of features is called a view. Chapter 1.7, Random Forest for
Dissimilarity-Based Multi-View Learning, by Dr. Bernard, et al. employs random
forest (RF) classifiers for measuring dissimilarities. RF embed a (dis)similarity
measure that takes the class membership into account in such a way that
instances from the same class are similar. A Dynamic View Selection method is
proposed to better combine the view-specific dissimilarity representations.
Chapter 1.8, A Review of Image Colorisation, by Dr. Rosin, et al. brings us
to a different theoretical but practical problem of adding color to a given
grayscale image. Three classes of colourization, including colourization by deep
learning, are reviewed in the chapter. Chapter 1.9 on speech recognition is
presented by Drs. Li and Yu, Recent Progresses on Deep Learning for Speech
Recognition. The authors noted that recent advances in automatic speech
recognition (ASP) have been mostly due to the advent of using deep learning
algorithms to build hybrid ASR systems with deep acoustic models like feed-
forward deep neural networks, convolution neural networks, and recurrent neural
networks. The summary of progresses is presented in the two areas where
significant efforts have been taken for ASR, namely, E2E (end to end) modeling
and robust modeling.
Part 2 begins with Chapter 2.1, Machine Learning in Remote Sensing by Dr.
Ronny Haenesch, providing an overview of remote sensing problems and
sensors. It then focuses on two machine learning approaches, one based on
random forest theory and the other on convolutional neural networks with
examples based on synthetic aperture radar image data. While much progress has
Preface ix
been made on information processing for hyperspectral images in remote
sensing, spectral unmixing problem presents a challenge. Chapter 2.2 by Kizel
and Benediktsson is on Hyperspectral and Spatially Adaptive Unmixing for
Analytical Reconstruction of Fraction Surfaces from Data with Corrupted Pixels.
Analysis of the spectral mixture is important for a reliable interpretation of
spectral image data. The information provided by spectral images allows for
distinguishing between different land cover types. However, due to the typical
low spatial resolution in remotely sensed data, many pixels in the image
represent a mixture of several materials within the area of the pixels, Therefore,
subpixel information is needed in different applications, which is extracted by
estimating fractional abundance that corresponds to pure signatures, known as
endmembers. The unmixing problem has been typically solved by using spectral
information only. In this chapter, a new methodology is presented based on a
modification of the spectral unmixing method called Gaussican-based spatially
adaptive unmixing (GBSAU). The problem of spatially adaptive unmixing is
similar to the fitting of a certain function using grid data. An advantage of the
GBSAU framework is to provide a novel solution for unmixing images with both
low SNR and a non-continuity due to the presence of corrupted pixels. Remote
sensing readers may also be interested in the excellent chapter in the second
edition of the handbook series, Statistical and Neural Network Pattern
Recognition Methods for Remote Sensing Application also by Prof. Benediktsson.
Chapter 2.3, Image Processing for Sea Ice Parameter Identification from Visual
Images, by Dr. Zhang introduces novel sea ice image processing algorithms to
automatically extract useful ice information such as ice concentration, ice types,
and ice flow size distribution, which are important in various fields of ice
engineering. It is noted that gradient vector flow snake algorithm is particularly
useful in ice boundary-based segmentation. More details on the chapter is
available in the author’s recent book, Sea Ice Image Processing with Matlab
(CRC Press 2018).
The next chapter (2.4) by Drs. Evan Fletcheris and Alexandeer Knaack is,
Applications of Deep Learning to Brain Segmentation and Labeling of MRI
Brain Structures. The authors successfully demonstrated deep learning
convolution neural networks (CNNs) applications in two areas of brain structural
image processing. One application focused on improving production and
robustness in brain segmentation. The other aimed at improving edge
recognition, leading to greater biological accuracy and statistical power for
computing longitudinal atrophy rates. The authors have also carefully presented a
detailed experimental set-up for the complex brain medical image processing
using deep learning and a large archive of MRIs for training and testing. While
there have been much increased interest in brain research and brain image
