Pattern Recognition and Machine Learning (Information Science and Statistics)
by Christopher M. Bishop
from Springer
The dramatic growth in practical applications for machine learning over the last ten years has been accompanied by many important developments in the underlying algorithms and techniques. For example, Bayesian methods have grown from a specialist niche to become mainstream, while graphical models have emerged as a general framework for describing and applying probabilistic techniques. The practical applicability of Bayesian methods has been greatly enhanced by the development of a range of approximate inference algorithms such as variational Bayes and expectation propagation, while new models based on kernels have had a significant impact on both algorithms and applications.
This completely new textbook reflects these recent developments while providing a comprehensive introduction to the fields of pattern recognition and machine learning. It is aimed at advanced undergraduates or first-year PhD students, as well as researchers and practitioners. No previous knowledge of pattern recognition or machine learning concepts is assumed. Familiarity with multivariate calculus and basic linear algebra is required, and some experience in the use of probabilities would be helpful though not essential as the book includes a self-contained introduction to basic probability theory.
The book is suitable for courses on machine learning, statistics, computer science, signal processing, computer vision, data mining, and bioinformatics. Extensive support is provided for course instructors, including more than 400 exercises, graded according to difficulty. Example solutions for a subset of the exercises are available from the book web site, while solutions for the remainder can be obtained by instructors from the publisher. The book is supported by a great deal of additional material, and the reader is encouraged to visit the book web site for the latest information.
Coming soon:
*For students, worked solutions to a subset of exercises available on a public web site (for exercises marked "www" in the text)
*For instructors, worked solutions to remaining exercises from the Springer web site
*Lecture slides to accompany each chapter
*Data sets available for download
Speech and Language Processing (2nd Edition) (Prentice Hall Series in Artificial Intelligence)
by Daniel Jurafsky
from Prentice Hall
An explosion of Web-based language techniques, merging of distinct fields, availability of phone-based dialogue systems, and much more make this an exciting time in speech and language processing. The first of its kind to thoroughly cover language technology – at all levels and with all modern technologies – this book takes an empirical approach to the subject, based on applying statistical and other machine-learning algorithms to large corporations. Builds each chapter around one or more worked examples demonstrating the main idea of the chapter, usingthe examples to illustrate the relative strengths and weaknesses of various approaches. Adds coverage of statistical sequence labeling, information extraction, question answering and summarization, advanced topics in speech recognition, speech synthesis. Revises coverage of language modeling, formal grammars, statistical parsing, machine translation, and dialog processing. A useful reference for professionals in any of the areas of speech and language processing.
Multiple View Geometry in Computer Vision
by Richard Hartley
from Cambridge University Press
A basic problem in computer vision is to understand the structure of a real world scene. This book covers relevant geometric principles and how to represent objects algebraically so they can be computed and applied. Recent major developments in the theory and practice of scene reconstruction are described in detail in a unified framework. Richard Hartley and Andrew Zisserman provide comprehensive background material and explain how to apply the methods and implement the algorithms. First Edition HB (2000): 0-521-62304-9
A basic problem in computer vision is to understand the structure of a real world scene given several images of it. Recent major developments in the theory and practice of scene reconstruction are described in detail in a unified framework. The book covers the geometric principles and how to represent objects algebraically so they can be computed and applied. The authors provide comprehensive background material and explain how to apply the methods and implement the algorithms directly.
Algorithms for Image Processing and Computer Vision
by J. R. Parker
from Wiley
A cookbook of the hottest new algorithms and cutting-edge techniques in image processing and computer vision
This amazing book/CD package puts the power of all the hottest new image processing techniques and algorithms in your hands. Based on J. R. Parker's exhaustive survey of Internet newsgroups worldwide, Algorithms for Image Processing and Computer Vision answers the most frequently asked questions with practical solutions.
Parker uses dozens of real-life examples taken from fields such as robotics, space exploration, forensic analysis, cartography, and medical diagnostics, to clearly describe the latest techniques for morphing, advanced edge detection, wavelets, texture classification, image restoration, symbol recognition, and genetic algorithms, to name just a few. And, best of all, he implements each method covered in C and provides all the source code on the CD.
For the first time, you're rescued from the hours of mind-numbing mathematical calculations it would ordinarily take to program these state-of-the-art image processing capabilities into software. At last, nonmathematicians get all the shortcuts they need for sophisticated image recognition and processing applications.
On the CD-ROM you'll find:
* Complete code for examples in the book
* A gallery of images illustrating the results of advanced techniques
* A free GNU compiler that lets you run source code on any platform
* A system for restoring damaged or blurred images
* A genetic algorithms package
Computer Vision
by Linda G. Shapiro
from Prentice Hall
For upper level courses in Computer Vision and Image Analysis. Provides necessary theory and examples for students and practitioners who will work in fields where significant information must be extracted automatically from images. Appropriate for those interested in multimedia, art and design, geographic information systems, and image databases, in addition to the traditional areas of automation, image science, medical imaging, remote sensing and computer cartography. The text provides a basic set of fundamental concepts and algorithms for analyzing images, and discusses some of the exciting evolving application areas of computer vision.
Numerical Geometry of Non-Rigid Shapes (Monographs in Computer Science)
by Alexander Bronstein
from Springer
Deformable objects are ubiquitous in the world surrounding us, on all levels from micro to macro. The need to study such shapes and model their behavior arises in a wide spectrum of applications, ranging from medicine to security. In recent years, non-rigid shapes have attracted growing interest, which has led to rapid development of the field, where state-of-the-art results from very different sciences - theoretical and numerical geometry, optimization, linear algebra, graph theory, machine learning and computer graphics, to mention several - are applied to find solutions.
This book gives an overview of the current state of science in analysis and synthesis of non-rigid shapes. Everyday examples are used to explain concepts and to illustrate different techniques. The presentation unfolds systematically and numerous figures enrich the engaging exposition. Practice problems follow at the end of each chapter, with detailed solutions to selected problems in the appendix. A gallery of colored images enhances the text.
This book will be of interest to graduate students, researchers and professionals in different fields of mathematics, computer science and engineering. It may be used for courses in computer vision, numerical geometry and geometric modeling and computer graphics or for self-study.
Introduction to Computer Graphics: Using Java 2D and 3D (Undergraduate Topics in Computer Science)
by Frank Klawonn
from Springer
Computer graphics comprises the creation and representation of simple graphical elements and images, as well as modern techniques for rendering a virtual reality. To apply these techniques correctly, one requires a basic understanding of the fundamental concepts in graphics.
This book introduces the most important basic concepts of computer graphics, coupling the technical background and theory with practical examples and applications throughout. Its user-friendly approach enables the reader to gain understanding through the theory at work, via the many example programs provided. With only elementary knowledge of the programming language Java, the reader will be able to create their own images and animations immediately, using Java 2D and/or Java 3D.
Features:
• Presents computer graphics theory and practice in integrated combination
• Focuses on the increasingly used Java 3D (and 2D in the first section of the book)
• Uses many pedagogical tools, including numerous easy-to-understand example programs and end-of-chapter exercises
• Offers Internet support for students and instructors (found at http://public.rz.fh-wolfenbuettel.de/~klawonn/computergraphics), such as additional exercises, solutions, program examples, slides for lecturers and links to useful websites
• Provides an ideal, self-contained introduction to computer graphics
Written for second year undergraduates and above, this reader-friendly, clear and concise textbook will be an essential tool for those studying Computer Science and Computer Engineering.
Frank Klawonn has many years of experience teaching computer graphics and coordinating application projects with companies.
Machine Vision : Theory, Algorithms, Practicalities (Signal Processing and its Applications)
by E. R. Davies
from Morgan Kaufmann
In the last 40 years, machine vision has evolved into a mature field embracing a wide range of applications including surveillance, automated inspection, robot assembly, vehicle guidance, traffic monitoring and control, signature verification, biometric measurement, and analysis of remotely sensed images. While researchers and industry specialists continue to document their work in this area, it has become increasingly difficult for professionals and graduate students to understand the essential theory and practicalities well enough to design their own algorithms and systems. This book directly addresses this need.
As in earlier editions, E.R. Davies clearly and systematically presents the basic concepts of the field in highly accessible prose and images, covering essential elements of the theory while emphasizing algorithmic and practical design constraints. In this thoroughly updated edition, he divides the material into horizontal levels of a complete machine vision system. Application case studies demonstrate specific techniques and illustrate key constraints for designing real-world machine vision systems.
· Includes solid, accessible coverage of 2-D and 3-D scene analysis.
· Offers thorough treatment of the Hough Transform-a key technique for inspection and surveillance.
· Brings vital topics and techniques together in an integrated system design approach.
· Takes full account of the requirement for real-time processing in real applications.
Digital Image Processing: An Algorithmic Introduction using Java
by Wilhelm Burger
from Springer
"This will be one of my continuing reference books for some time to come."
Steve Cunningham, PhD, Past President of SIGGRAPH
"An excellent resource for the users of ImageJ."
Wayne Rasband, author of ImageJ
This modern, self-contained, textbook explains the fundamental algorithms of digital image processing through practical examples and complete Java implementations. Available for the first time in English, Digital Image Processing is the definitive textbook for students, researchers, and professionals in search of critical analysis and modern implementations of the most important algorithms in the field.
• Practical examples and carefully constructed chapter-ending exercises drawn from the authors' years of experience teaching this material
• Real implementations, concise mathematical notation, and precise algorithmic descriptions designed for programmers and practitioners
• Easily adaptable Java code and completely worked out examples for easy inclusion in existing, and rapid prototyping of new, applications
• Self-contained chapters and additional online material suitable for a flexible one- or two- semester course
• Uses ImageJ, the image processing system developed, maintained, and freely distributed by the U.S. National Institutes of Health (NIH)
• A comprehensive Website (www.imagingbook.com) with complete Java source code, test images, and additional instructor materials
This comprehensive, reader-friendly introduction is ideal for foundation courses as well as eminently suitable for self-study.
Wilhelm Burger is the director of the Digital Media degree programs at the Upper Austria University of Applied Sciences at Hagenberg.
Mark J. Burge is a program director at the National Science Foundation (NSF) and a principal at Noblis (Mitretek) in Washington, D.C.
Computer Vision: A Modern Approach
by David A. Forsyth
from Prentice Hall
Appropriate for upper-division undergraduate- and graduate-level courses in computer vision found in departments of Computer Science, Computer Engineering and Electrical Engineering. This long anticipated book is the most complete treatment of modern computer vision methods by two of the leading authorities in the field. This accessible presentation gives both a general view of the entire computer vision enterprise and also offers sufficient detail for students to be able to build useful applications. Students will learn techniques that have proven to be useful by first-hand experience and a wide range of mathematical methods.
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