Artificial Intelligence: A Modern Approach, 3rd Edition

Artificial Intelligence: A Modern Approach, 3rd Edition

English | 2009 | ISBN: 0136042597 | 1152 pages | PDF | 44.80 Mb

The long-anticipated revision of this #1 selling book offers the most comprehensive, state of the art introduction to the theory and practice of artificial intelligence for modern applications. Intelligent Agents. Solving Problems by Searching. Informed Search Methods. Game Playing. Agents that Reason Logically. First-order Logic. Building a Knowledge Base. Inference in First-Order Logic. Logical Reasoning Systems. Practical Planning. Planning and Acting. Uncertainty. Probabilistic Reasoning Systems. Making Simple Decisions. Making Complex Decisions. Learning from Observations. Learning with Neural Networks. Reinforcement Learning. Knowledge in Learning. Agents that Communicate. Practical Communication in English. Perception. Robotics. For computer professionals, linguists, and cognitive scientists interested in artificial intelligence.

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Advances in Artificial Intelligence for Privacy Protection and Security

Advances in Artificial Intelligence for Privacy Protection and Security Summary:

World Scientific Publishing Company | 2009 | ISBN: 9812790322 | 404 pages | PDF | 11,6 MB

In this book, we aim to collect the most recent advances in artificial intelligence techniques (i.e. neural networks, fuzzy systems, multi-agent systems, genetic algorithms, image analysis, clustering, etc), which are applied to the protection of privacy and security. The symbiosis between these fields leads to a pool of invigorating ideas, which are explored in this book. On the one hand, individual privacy protection is a hot topic and must be addressed in order to guarantee the proper evolution of a modern society. On the other, security can invade individual privacy, especially after the appearance of new forms of terrorism. In this book, we analyze these problems from a new point of view.

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Computer Vision 2nd Edition (Rapidshare)

Title : Computer Vision 2nd Ed

Pub Date : 1982

Author : Dana H. Ballard and Christopher M. Brown

Publisher: Prentice Hall

ISBN : 0-13-165316-4

Overview :

What information about scenes can be extracted from an image using only basic assumptions about physics and optics?

How are images segmented into meaningful parts?

At what stage must domain-dependent, prior knowledge about the world be incorporated into the understanding process?

How are world models and conceptual knowledge represented and used?

These and many other questions, inherent in this relatively new and fast-growing field, are explored and answered in Computer Vision. The authors assemble crucial material from many diciplines including artificial intelligence, psychology, computer graphics, and image processing to form a practical text and reference for anyone involved in builing vision systems.

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Computer Vision

Title : Computer Vision

Pub Date : March, 2000

Author : Linda Shapiro and George Stockman

Publisher: -

ISBN : -

Overview :

This book is intended as an introduction to computer vision for a broad audience. It provides necessary theory and examples for students and practicioners who will work in fields where significant information must be extracted automatically from images. The book should be a useful resource book for professionals, a text for both undergraduate and beginning graduate courses, and a resource for enrichment of college or even high school projects. Our goals were to provide a basic set of fundamental concepts and algorithms and also discuss some of the exciting evolving application areas. This book is unique in that it contains chapters on image databases and on virutal and augmented reality, two exciting evolving application areas. A final chapter gives a complete view of real world systems that use computer vision.

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Computer Vision A Modern Approach

Title : Computer Vision A Modern Approach
Pub Date : -
Author : Forsyth & Ponce
Publisher: -
ISBN : -

Overview :

Part I - Image Formations
Part II - Image Models
Part III - Early Vision: One Image
Part IV - Early Vision: Multiple views
Part V - Mid-Level Vision
Part VI - High-Level Vision
Part VII - Applications and Topics

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Computer Vision and Applications - A Guide for Students and Practitioners

Title : Computer Vision and Applications - A Guide for Students and Practitioners

Pub Date : 2000

Author : Bernd Jähne and Horst Haußecker (Editors)

Publisher: Academic Press

ISBN : 0–12–379777-2

Overview :

This book offers a fresh approach to computer vision. The whole vision process from image formation to measuring, recognition, or reacting is regarded as an integral process. Computer vision is understood as the host of techniques to acquire, process, analyze, and understand complex higher-dimensional data from our environment for scientific and technical exploration.

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Feature Extraction and Image Processing

E-book Title : Feature Extraction and Image Processing

Pub Date : 2002

Author : Mark S. Nixon & Alberto S. Aguado

Publisher: Newnes

ISBN : 0 7506 5078 8

Overview :

We will no doubt be asked many times: why on earth write a new book on computer vision? Fair question: there are already many good books on computer vision already out in the bookshops, as you will find referenced later, so why add to them? Part of the answer is that any textbook is a snapshot of material that exists prior to it. Computer vision, the art of processing images stored within a computer, has seen a considerable amount of research by highly qualified people and the volume of research would appear to have increased in recent years. That means a lot of new techniques have been developed, and many of the more recent approaches have yet to migrate to textbooks.

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Fundamentals of Computer Vision

E-book Title : Fundamentals of Computer Vision
Pub Date : 7 December 1997
Author : Mubarak Shah

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HANDBOOK OF Computer Vision Algorithms in Image Algebra second edition

Title : HANDBOOK OF Computer Vision Algorithms in Image Algebra second edition

Pub Date : 2001

Author : Gerhard X. Ritter & Joseph N. Wilson

Publisher: CRC Press

ISBN : 0-8493-0075-4

Overview :

Chapter 1 provides a short introduction to the field of image algebra. Chapters 2–12 are devoted to particular techniques commonly used in computer vision algorithm development, ranging from early processing techniques to such higher level topics as image descriptors and artificial neural networks. Although the chapters on techniques are most naturally studied in succession, they are not tightly interdependent and can be studied according to the reader’s particular interest. In the Appendix we present iac++ computer programs of some of the techniques surveyed in this book. These programs reflect the image algebra pseudocode presented in the chapters and serve as examples of how image algebra pseudocode can be converted into efficient computer programs.

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Handbook of Computer Vision and Applications- Volume 1: Sensors and Imaging

Ebook Title : Handbook of Computer Vision and Applications- Volume 1: Sensors and Imaging

Pub Date : 1999

Author : Bernd Jähne, Horst Hauecker & Peter Geiler (Editors)

Publisher: ACADEMIC PRESS

ISBN : 0–12–379771-3

Overview :

This handbook oers a fresh approach to computer vision. The whole vision process from image formation to measuring, recognition, or reacting is regarded as an integral process. Computer vision is understood as the host of techniques to acquire, process, analyze, and understand complex higher-dimensional data from our environment for scientific and technical exploration.

In this sense the handbook takes into account the interdisciplinary nature of computer vision with its links to virtually all natural sciences and attempts to bridge two important gaps. The first is between modern physical sciences and the many novel techniques to acquire images. The second is between basic research and applications. When a reader with a background in one of the fields related to computer vision feels he has learned something from one of the many other facets of computer vision, the handbook will have fulfilled its purpose.

The handbook comprises three volumes. The first volume, Sensors and Imaging, covers image formation and acquisition. The second volume, Signal Processing and Pattern Recognition, focuses on processing of the spatial and spatiotemporal signal acquired by imaging sensors. The third volume, Systems and Applications, describes how computer vision is integrated into systems and applications.

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Handbook of Computer Vision and Applications - Volume 2: Signal Processing and Pattern Recognition

Ebook Title : Handbook of Computer Vision and Applications - Volume 2: Signal Processing and Pattern Recognition

Pub Date : 1999

Author : Bernd Jähne, Horst Hauecker & Peter Geiler (Editors)

Publisher: ACADEMIC PRESS

ISBN : 0–12–379772–1

Overview :

This handbook covers a fresh approach to computer vision. The whole vision process from image formation to measuring, recognition, or reacting is regarded as an integral process. Computer vision is understood as the host of techniques to acquire, process, analyze, and understand complex higher-dimensional data from our environment for scientific and technical exploration.

In this sense the handbook takes into account the interdisciplinary nature of computer vision with its links to virtually all natural sciences and attempts to bridge two important gaps. The first is between modern physical sciences and the many novel techniques to acquire images. The second is between basic research and applications. When a reader with a background in one of the fields related to computer vision feels he has learned something from one of the many other facets of computer vision, the handbook will have fulfilled its purpose.

The handbook comprises three volumes. The first volume, Sensors and Imaging, covers image formation and acquisition. The second volume, Signal Processing and Pattern Recognition, focuses on processing of the spatial and spatiotemporal signal acquired by imaging sensors. The third volume, Systems and Applications, describes how computer vision is integrated into systems and applications.

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Handbook of Computer Vision and Applications - Volume 3: Systems and Applications

E-book Title : Handbook of Computer Vision and Applications - Volume 3: Systems and Applications

Pub Date : 1999

Author : Bernd Jähne, Horst Hauecker & Peter Geiler (Editors)

Publisher: ACADEMIC PRESS

ISBN : 0–12–379773-X

Overview :

This handbook covers a fresh approach to computer vision. The whole vision process from image formation to measuring, recognition, or reacting is regarded as an integral process. Computer vision is understood as the host of techniques to acquire, process, analyze, and understand complex higher-dimensional data from our environment for scientific and technical exploration.

In this sense the handbook takes into account the interdisciplinary nature of computer vision with its links to virtually all natural sciences and attempts to bridge two important gaps. The first is between modern physical sciences and the many novel techniques to acquire images. The second is between basic research and applications. When a reader with a background in one of the fields related to computer vision feels he has learned something from one of the many other facets of computer vision, the handbook will have fulfilled its purpose.

The handbook comprises three volumes. The first volume, Sensors and Imaging, covers image formation and acquisition. The second volume, Signal Processing and Pattern Recognition, focuses on processing of the spatial and spatiotemporal signal acquired by imaging sensors. The third volume, Systems and Applications, describes how computer vision is integrated into systems and applications.

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Dynamic Bayesian Networks: Representation, Inference and Learning

Title : Dynamic Bayesian Networks: Representation, Inference and Learning

Pub Date : 2002

Author : Kevin Patrick Murphy

Publisher: UNIVERSITY OF CALIFORNIA, BERKELEY

ISBN : -

Overview :

Abstract:

Modelling sequential data is important in many areas of science and engineering. Hidden Markov models (HMMs) and Kalman filter models (KFMs) are popular for this because they are simple and flexible. For example, HMMs have been used for speech recognition and bio-sequence analysis, and KFMs have been used for problems ranging from tracking planes and missiles to predicting the economy. However, HMMs and KFMs are limited in their “expressive power”. Dynamic Bayesian Networks (DBNs) generalize HMMs by allowing the state space to be represented in factored form, instead of as a single discrete random variable. DBNs generalize KFMs by allowing arbitrary probability distributions, not just (unimodal) linear-Gaussian. In this thesis, I will discuss how to represent many different kinds of models as DBNs, how to perform exact and approximate inference in DBNs, and how to learn DBN models from sequential data.

In particular, the main novel technical contributions of this thesis are as follows: a way of representing Hierarchical HMMs as DBNs, which enables inference to be done in O(T) time instead of O(T 3), where T is the length of the sequence; an exact smoothing algorithm that takes O(log T) space instead of O(T); a simple way of using the junction tree algorithm for online inference in DBNs; new complexity bounds on exact online inference in DBNs; a new deterministic approximate inference algorithm called factored frontier; an analysis of the relationship between the BK algorithm and loopy belief propagation; a way of applying Rao-Blackwellised particle filtering to DBNs in general, and the SLAM (simultaneous localization and mapping) problem in particular; a way of extending the structural EM algorithm to DBNs; and a variety of different applications of DBNs. However, perhaps the main value of the thesis is its catholic presentation of the field of sequential data modelling.

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Learning Bayesian Networks

Title : Learning Bayesian Networks

Pub Date : -

Author : Richard E. Neapolitan

Publisher: Northeastern Illinois University

ISBN : -

Overview :

Bayesian networks are graphical structures for representing the probabilistic relationships among a large number of variables and doing probabilistic inference with those variables. During the 1980’s, a good deal of related research was done on developing Bayesian networks (belief networks, causal networks, influence diagrams), algorithms for performing inference with them, and applications that used them. However, the work was scattered throughout research articles. My purpose in writing the 1990 text Probabilistic Reasoning in Expert Systems was to unify this research and establish a textbook and reference for the field which has come to be known as ‘Bayesian networks.’ The 1990’s saw the emergence of excellent algorithms for learning Bayesian networks from data. However,by 2000 there still seemed to be no accessible source for ‘learning Bayesian networks.’ Similar to my purpose a decade ago, the goal of this text is to provide such a source.

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