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Lectures on Concurrency and Petri Nets: Advances in Petri Nets

Posted: 01 Feb 2009 07:18 PM CST

Lectures on Concurrency and Petri Nets: Advances in Petri Nets

Product Description

This tutorial volume originates from the 4th Advanced Course on Petri Nets, ACPN 2003, held in Eichstätt, Germany in September 2003.

In addition to lectures given at ACPN 2003, additional chapters have been commissioned to give a well-balanced presentation of the state of the art in the area. This book will be useful as both a reference for those working in the area as well as a study book for the reader who is interested in an up-to-date overview of research and development in concurrent and distributed systems; of course, readers specifically interested in theoretical or applicational aspects of Petri nets will appreciate the book as well.

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IT Essentials: PC Hardware and Software Labs and Study Guide

Posted: 01 Feb 2009 07:17 PM CST

Natural Computing in Computational Finance: Volume 2

Posted: 01 Feb 2009 07:15 PM CST

Algorithms and Models for the Web-Graph: 6th International Workshop

Posted: 01 Feb 2009 07:14 PM CST

Algorithms and Models for the Web-Graph: 6th International Workshop

Product Description

This book constitutes the refereed proceedings of the 6th International Workshop on Algorithms and Models for the Web-Graph, WAW 2009, held in Barcelona, Spain, in February 2009 - co-located with WSDM 2009, the Second ACM International Conference on Web Search and Data Mining.

The 14 revised full papers presented were carefully reviewed and selected from numerous submissions for inclusion in the book. The papers address a wide variety of topics related to the study of the Web-graph such as theoretical and empirical analysis of the Web graph and Web 2.0 graphs, random walks on the Web and Web 2.0 graphs and their applications, and design and performance evaluation of the algorithms for social networks. The workshop papers have been naturally clustered in three topical sections on graph models for complex networks, pagerank and Web graph, and social networks and search.

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Metaheuristic Clustering

Posted: 01 Feb 2009 07:13 PM CST

Metaheuristic Clustering

Product Description

Cluster analysis means the organization of an unlabeled collection of objects or patterns into separate groups based on their similarity. The task of computerized data clustering has been approached from diverse domains of knowledge like graph theory, multivariate analysis, neural networks, fuzzy set theory, and so on. Clustering is often described as an unsupervised learning method but most of the traditional algorithms require a prior specification of the number of clusters in the data for guiding the partitioning process, thus making it not completely unsupervised. Modern data mining tools that predict future trends and behaviors for allowing businesses to make proactive and knowledge-driven decisions, demand fast and fully automatic clustering of very large datasets with minimal or no user intervention.

In this volume, we formulate clustering as an optimization problem, where the best partitioning of a given dataset is achieved by minimizing/maximizing one (single-objective clustering) or more (multi-objective clustering) objective functions. Using several real world applications, we illustrate the performance of several metaheuristics, particularly the Differential Evolution algorithm when applied to both single and multi-objective clustering problems, where the number of clusters is not known beforehand and must be determined on the run. This volume comprises of 7 chapters including an introductory chapter giving the fundamental definitions and the last Chapter provides some important research challenges.

Academics, scientists as well as engineers engaged in research, development and application of optimization techniques and data mining will find the comprehensive coverage of this book invaluable.

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An Introduction to 3D Computer Vision Techniques and Algorithms

Posted: 01 Feb 2009 07:03 PM CST

An Introduction to 3D Computer Vision Techniques and Algorithms

Product Description
Computer vision encompasses the construction of integrated vision systems and the application of vision to problems of real-world importance. The process of creating 3D models is still rather difficult, requiring mechanical measurement of the camera positions or manual alignment of partial 3D views of a scene. However using algorithms, it is possible to take a collection of stereo-pair images of a scene and then automatically produce a photo-realistic, geometrically accurate digital 3D model.

This book provides a comprehensive introduction to the methods, theories and algorithms of 3D computer vision. Almost every theoretical issue is underpinned with practical implementation or a working algorithm using pseudo-code and complete code written in C++ and MatLab®. There is the additional clarification of an accompanying website with downloadable software, case studies and exercises. Organised in three parts, Cyganek and Siebert give a brief history of vision research, and subsequently:

present basic low-level image processing operations for image matching, including a separate chapter on image matching algorithms;
explain scale-space vision, as well as space reconstruction and multiview integration;
demonstrate a variety of practical applications for 3D surface imaging and analysis;
provide concise appendices on topics such as the basics of projective geometry and tensor calculus for image processing, distortion and noise in images plus image warping procedures.

An Introduction to 3D Computer Vision Algorithms and Techniques is a valuable reference for practitioners and programmers working in 3D computer vision, image processing and analysis as well as computer visualisation. It would also be of interest to advanced students and researchers in the fields of engineering, computer science, clinical photography, robotics, graphics and mathematics.

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