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Image Recognition
and Classification
Algorithms, Systems, and Applications
edited by
Bahram Javidi
University of Connecticut
Storrs, Connecticut
Marcel Dekker, Inc.
TM
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Copyright © 2002 by Marcel Decker, Inc. All Rights Reserved
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ISBN: 0-8247-0783-4
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For my Aunt Matin
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Preface
Image recognition and classification is one of the most actively pursued
areas in the broad field of imaging sciences and engineering. The reason is
evident: the ability to replace human visual capabilities with a machine is
very important and there are diverse applications. The main idea is to
inspect an image scene by processing data obtained from sensors. Such
machines can substantially reduce the workload and improve accuracy of
making decisions by human operators in diverse fields including the military
and defense, biomedical engineering systems, health monitoring, surgery,
intelligent transportation systems, manufacturing, robotics, entertainment,
and security systems.
Image recognition and classification is a multidisciplinary field. It
requires contributions from diverse technologies and expertise in sensors,
imaging systems, signal/image processing algorithms, VLSI, hardware and
software, and packaging/integration systems.
In the military, substantial efforts and resources have been placed in this
area. The main applications are in autonomous or aided target detection
and recognition, also known as automatic target recognition (ATR). In
addition, a variety of sensors have been developed, including high-speed
video, low-light-level TV, forward-looking infrared (FLIR), synthetic aper-
ture radar (SAR), inverse synthetic aperture radar (ISAR), laser radar
(LADAR), multispectral and hyperspectral sensors, and three-dimensional
sensors. Image recognition and classification is considered an extremely
useful and important resource available to military personnel and opera-
tions in the areas of surveillance and targeting.
In the past, most image recognition and classification applications have
been for military hardware because of high cost and performance demands.
With recent advances in optoelectronic devices, sensors, electronic hard-
ware, computers, and software, image recognition and classification systems
have become available with many commercial applications.
Copyright © 2002 by Marcel Decker, Inc. All Rights Reserved
Dekker,
v
vi
Preface
While there have been significant advances in image recognition and
classification technologies, major technical problems and challenges face
this field. These include large variations in the inspected object signature
due to environmental conditions, geometric variations, aging, and target/
sensor behavior (e.g., IR thermal signature fluctuations, reflection angles,
etc.). In addition, in many applications the target or object of interest is a
small part of a very complex scene under inspection; that is, the distorted
target signature is embedded in background noise such as clutter, sensor
noise, environmental degradations, occlusion, foliage masking, and camou-
flage. Sometimes the algorithms are developed with a limited available train-
ing data set, which may not accurately represent the actual fluctuations of
the objects or the actual scene representation, and other distortions are
encountered in realistic applications. Under these adverse conditions, a reli-
able system must perform recognition and classification in real time and
with high detection probability and low false alarm rates. Therefore, pro-
gress is needed in the advancement of sensors and algorithms and compact
systems that integrate sensors, hardware, and software algorithms to pro-
vide new and improved capabilities for high-speed accurate image recogni-
tion and classification.
This book presents important recent advances in sensors, image proces-
sing algorithms, and systems for image recognition and classification with
diverse applications in military, aerospace, security, image tracking, radar,
biomedical, and intelligent transportation. The book includes contributions
by some of the leading researchers in the field to present an overview of
advances in image recognition and classification over the past decade. It
provides both theoretical and practical information on advances in the field.
The book illustrates some of the state-of-the-art approaches to the field of
image recognition using image processing, nonlinear image filtering, statis-
tical theory, Bayesian detection theory, neural networks, and 3D imaging.
Currently, there is no single winning technique that can solve all classes of
recognition and classification problems. In most cases, the solutions appear
to be application-dependent and may combine a number of these
approaches to acquire the desired results.
Image Recognition and Classification
provides examples, tests, and experi-
ments on real world applications to clarify theoretical concepts. A bibliog-
raphy for each topic is also included to aid the reader. It is a practical
book, in which the systems and algorithms have commercial applications
and can be implemented with commercially available computers, sensors,
and processors. The book assumes some elementary background in signal/
image processing. It is intended for electrical or computer engineers with
interests in signal/image processing, optical engineers, computer scientists,
imaging scientists, biomedical engineers, applied physicists, applied mathe-
Copyright © 2002 by Marcel Decker, Inc. All Rights Reserved
Dekker,

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