3 minute read
Pattern Recognition/Image Analysis/ Computer Vision
HANDS-ON COMPUTER VISION
by Marc Pomplun (University of Massachusetts Boston, USA)
The proposed book will not only teach readers the fundamental methods and concepts in computer vision, but it will also show them in detail how to implement the most important algorithms efficiently.
Readership: Undergraduates, graduates, and professionals studying/ dealing with machine perception/computer vision, pattern recognition/ image analysis, software engineering/programming, neural networks, robotics & automated systems.
650pp Jun 2022 978-981-4571-97-5(pbk) US$78 £65 978-981-4571-96-8 US$129 £107
Series in Mathematical Biology and Medicine - Vol 10
OPTIMAL TRANSPORT NETWORKS IN NATURE
by Natalya Kizilova (Kharkov National University, Ukraine)
Key Features
• Provides a comprehensive measurement data and statistical relationships on construction rules of the transportation networks in nature • Discusses the evolutionary ways of plants and animals towards the optimal liquid transport systems • Gives a novel insight into the evolutionary optimization problem
Readership: Academics, researchers, and graduate students in mathematical biology, mathematical modeling, fluid mechanics, biomedical engineering, pattern recognition/image analysis.
200pp 978-981-283-873-5 Jun 2022 US$106 £88
Series in Machine Perception and Artificial Intelligence - Vol 88
THE LOGNORMALITY PRINCIPLE AND ITS APPLICATIONS IN E-SECURITY, E-LEARNING AND E-HEALTH
edited by Réjean Plamondon (Polytechnique Montréal, Canada), Angelo Marcelli (Universitàdi Salerno, Italy) & Miguel Ángel Ferrer (Universidad de Las Palmas de Gran Canaria, Spain)
This compendium provides a detailed account of the lognormality principle characterizing the human motor behavior by summarizing a sound theoretical framework for modeling such a behavior, introducing the most recent algorithms for extracting the lognormal components of complex movements in 2, 2.5 and 3 dimensions. It also vividly reports the most advanced applications to handwriting analysis and recognition, signature and writer verification, gesture recognition and calligraphy generation, evaluation of motor skills, improvement/degradation with aging, handwriting learning, education and developmental deficits, prescreening of children with ADHD (Attention Development and Hyperactivity Disorder), monitoring of concussion recovery, diagnosis and monitoring of Alzheimer’s and Parkinson’s diseases and aging effects in speech and handwriting.
Readership: Professionals, academics, researchers, and graduate students in pattern recognition, artificial intelligence, biomedical engineering and mathematical modeling.
QUANTUM MECHANICS AND BAYESIAN MACHINES
by George Chapline (Lawrence Livermore National Laboratory, USA)
This compendium brings together the fields of Quantum Computing, Machine Learning, and Neuromorphic Computing. It provides an elementary introduction for students and researchers interested in quantum or neuromorphic computing to the basics of machine learning and the possibilities for using quantum devices for pattern recognition and Bayesian decision tree problems.
Readership: Researchers, academics, professionals and graduate students in pattern recognition/image analysis, machine learning, quantum mechanics and general applied maths.
100pp 978-981-3232-46-4 Jun 2022 US$68 £60
DEEP LEARNING FOR IMAGE RECONSTRUCTION
by Markus Haltmeier, Stephan Antholzer & Johannes Schwab (University of Innsbruck, Austria)
Many problems in science, engineering and medicine follow an inverse approach to problem by observations the output data to calculate or predict the inputs should be to generated the responses: for example, calculating an image in X-ray computed tomography, source reconstruction in acoustics, or calculating the density of the Earth from measurements of its gravity field. Recent advance in deep learning-based algorithms has emerged as a novel paradigm for image processing.
This book compiles the state-of-the-art approaches for solving inverse problems by deep learning; from basic concepts to deep learning and algorithms in image processing. It serves as an introduction to researchers working in image processing, and pattern recognition as well as students undertaking research in signal processing and AI.
Readership: Advanced undergraduate and graduate students; researchers in the field of Image Processing and AI.
250pp 978-981-120-367-1 May 2022 US$98 £85
Series on Language Processing, Pattern Recognition, and Intelligent Systems - Vol 6
edited by Nicola Nobile), Marleah Blom & Ching Y Suen (Concordia University, Canada)
This book includes reviewed papers by international scholars from the 2020 International Conference on Pattern Recognition and Artificial Intelligence (held online). The papers have been expanded to provide more details specifically for the book. It is geared to promote ongoing interest and understanding about pattern recognition and artificial intelligence.
Chapters include works centred on medical and financial applications as well as topics related to handwriting analysis and text processing, internet security, image analysis, database creation, neural networks and deep learning. While the book is geared to promote interest from the general public, it may also be of interest to graduate students and researchers in the field.
