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Medical imaging is an important topic and plays a key role in
robust diagnosis and patient care. It has experienced an explosive
growth over the last few years due to imaging modalities such as
X-rays, computed tomography (CT), magnetic resonance (MR) imaging,
and ultrasound. This book focuses primarily on model-based
segmentation techniques, which are applied to cardiac, brain,
breast and microscopic cancer cell imaging. It includes
contributions from authors working in industry and academia, and
presents new material.
International Conference on Advances in Pattern Recognition (ICAPR
98) at Plymouth represents an important meeting for advanced
research in pattern recognition. There is considerable interest in
the areas of image processing, medical imaging, speech recognition,
document analysis and character recognition, fuzzy data analysis
and neural networks. ICAPR 98 is aimed at providing an
international platform for invited research in this
multi-disciplinary area. It is expected that the conference will
grow in future years to include more research contributions that
detail state-of the-art research in pattern recognition. ICAPR 98
attracted contributions from different countries of the highest
quality. I should like to thank the programme and organising
committee for doing an excellent job in organising this conference.
The peer reviewed nature of the conference ensured high quality
publications in these proceedings. My personal thanks to Mrs.
Barbara Davies who served as conference secretary and worked
tirelessly in organising the conference. I thank the organising
chair for the local arrangements and our should also key-note,
plenary and tutorial speakers for their valuable contributions to
the conference. I also thank Springer-Verlag for publishing these
proceedings that will be a valuable source of research reference
for the readers. Finally, I thank all participants who made this
conference successful."
This book features a collection of articles presented at the
2007 Workshop on Advances in Pattern Recognition, which was
organized in conjunction with the 5th International Summer School
on Pattern Recognition. It provides readers with the
state-of-the-art algorithms in the area of pattern recognition as
well as a presentation of the cutting edge applications within the
field.
The field of pattern recognition has emerged as one of the most
challenging and important endeavours in the area of information
technology research. Research in the area of pattern recognition
has benefits for improving many areas of human endeavour, including
medicine, the economy, the environment, and security. This book
presents some of the latest advances in the area of pattern
recognition theory and applications. The first half of the book
discusses novel pattern classification and matching schemes, and
the second half describes the application of novel tools in
biometrics and digital multimedia. The applications included, such
as face/iris recognition, handwriting recognition, surveillance,
human dynamics, sensor fusion, etc., provide a detailed insight
into how to build real pattern recognition systems and how to
evaluate them. Given the dynamic nature of technology evolution in
this area, this book provides the latest algorithms and concepts
that can be used to build real systems. Features and topics:
Provides state-of-the art algorithms, as well as presents
cutting-edge applications within the field Introduces achievements
in theoretical pattern recognition, including statistical and
Bayesian pattern recognition, structural pattern recognition,
neural networks, classification and data mining, evolutionary
approaches to optimisation, and knowledge based systems Offers
insights and support to practitioners concerned with the
state-of-the art technology in the area Progress in Pattern
Recognition addresses the needs of postgraduate students,
researchers, and practitioners in the areas of computer science,
engineering and mathematicswhere pattern recognition techniques are
widely used. Professor Sameer Singh is Director of the Research
School of Informatics, Loughborough University, UK, and serves as
Editor-in-Chief of the Springer journal, Pattern Analysis and
Applications.
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Pattern Recognition and Data Mining - Third International Conference on Advances in Pattern Recognition, ICAR 2005, Bath, UK, August 22-25, 2005, Part I (Paperback, 2005 ed.)
Sameer Singh, Maneesha Singh, Chid Apte, Petra Perner
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R3,328
Discovery Miles 33 280
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Ships in 10 - 15 working days
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This LNCS volume contains the papers presented at the 3rd
International C- ferenceonAdvances in
PatternRecognition(ICAPR2005)organizedin August, 2005 in the
beautiful city of Bath, UK. The conference was ?rst organized in
November 1998 in Plymouth, UK and subsequently in March 2001 in Rio
de Janeiro, Brazil. The conference encouraged papers that made
signi?cant th- reticalandapplication-basedcontributions
inpatternrecognition. The emphasis was on an open exchange of ideas
and shared learning. The papers submitted to ICAPR 2005 were
thoroughly reviewed by up to three referees per paper and less than
40% of the submitted papers were accepted. The papers have been
?nally published as two volumes of LNCS and these are organized
under the themes of Pattern Recognition and Data Mining (which
included papers from the trackson PatternRecognition Methods,
Knowledgeand Learning, and Data Mining), and Pattern Recognition
and Image Analysis (which included papers from the Applications
track). From the conference technical programme point of view, the
?rst volume contains papers on pattern recognition, data mining,
signal processing and OCR/document analysis. The second volume
contains - pers from the Workshop on Pattern Recognition for Crime
Prevention, Security and Surveillance, Biometrics, Image Processing
and Medical Imaging. ICAPR 2005was run in parallel with the
InternationalWorkshop on Pattern Recognition for Crime Prevention,
Security and Surveillance that was organized
onthe22ndofAugust,2005. Thisworkshopbroughttogethera number ofexc-
lent papers that focussed on how pattern recognition techniques can
be used to developsystemsthathelpwithcrimepreventionanddetection.
Onthesameday, a number of tutorials were also organized
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Pattern Recognition and Image Analysis - Third International Conference on Advances in Pattern Recognition, ICAPR 2005, Bath, UK, August 22-25, 2005, Part II (Paperback, 2005 ed.)
Sameer Singh, Maneesha Singh, Chid Apte, Petra Perner
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R3,125
Discovery Miles 31 250
|
Ships in 10 - 15 working days
|
This LNCS volume contains the papers presented at the 3rd
International C- ferenceonAdvances in
PatternRecognition(ICAPR2005)organizedin August, 2005 in the
beautiful city of Bath, UK. The conference was ?rst organized in
November 1998 in Plymouth, UK and subsequently in March 2001 in Rio
de Janeiro, Brazil. The conference encouraged papers that made
signi?cant th- reticalandapplication-basedcontributions
inpatternrecognition. The emphasis was on an open exchange of ideas
and shared learning. The papers submitted to ICAPR 2005 were
thoroughly reviewed by up to three referees per paper and less than
40% of the submitted papers were accepted. The papers have been
?nally published as two volumes of LNCS and these are organized
under the themes of Pattern Recognition and Data Mining (which
included papers from the trackson PatternRecognition Methods,
Knowledgeand Learning, and Data Mining), and Pattern Recognition
and Image Analysis (which included papers from the Applications
track). From the conference technical programme point of view, the
?rst volume contains papers on pattern recognition, data mining,
signal processing and OCR/document analysis. The second volume
contains - pers from the Workshop on Pattern Recognition for Crime
Prevention, Security and Surveillance, Biometrics, Image Processing
and Medical Imaging. ICAPR 2005was run in parallel with the
InternationalWorkshop on Pattern Recognition for Crime Prevention,
Security and Surveillance that was organized
onthe22ndofAugust,2005. Thisworkshopbroughttogethera number ofexc-
lent papers that focussed on how pattern recognition techniques can
be used to developsystemsthathelpwithcrimepreventionanddetection.
Onthesameday, a number of tutorials were also organized
Medical imaging is an important topic which is generally recognised as key to better diagnosis and patient care. It has experienced an explosive growth over the last few years due to imaging modalities such as X-rays, computed tomography (CT), magnetic resonance (MR) imaging, and ultrasound.This book focuses primarily on state-of-the-art model-based segmentation techniques which are applied to cardiac, brain, breast and microscopic cancer cell imaging. It includes contributions from authors based in both industry and academia and presents a host of new material including algorithms for:- brain segmentation applied to MR;- neuro-application using MR; - parametric and geometric deformable models for brain segmentation;- left ventricle segmentation and analysis using least squares and constrained least squares models for cardiac X-rays; - left ventricle analysis in echocardioangiograms;- breast lesion detection in digital mammograms;detection of cells in cell images.As an overview of the latest techniques, this book will be of particular interest to students and researchers in medical engineering, image processing, computer graphics, mathematical modelling and data analysis. It will also be of interest to researchers in the fields of mammography, cardiology, pathology and neurology.
This book constitutes the refereed proceedings of the Second International Conference on Advances in Pattern Recognition, ICAPR 2001, held in Rio de Janeiro, Brazil in March 2001.The 40 revised full papers presented together with three invited papers and two tutorial presentations were carefully reviewed and selected for inclusion in the proceedings. The book is organized in topical sections on neural networks and computational intelligence, character recognition and document analysis, feature selection and analysis, pattern recognition and classification, image and signal processing applications, and image feature analysis and retrieval.
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