Choose an application

• Reference Manager
• EndNote
• RefWorks (Direct export to RefWorks)

Networks have become nearly ubiquitous and increasingly complex, and their support of modern enterprise environments has become fundamental. Accordingly, robust network management techniques are essential to ensure optimal performance of these networks. This monograph treats the application of numerous graph-theoretic algorithms to a comprehensive analysis of dynamic enterprise networks. Network dynamics analysis yields valuable information about network performance, efficiency, fault prediction, cost optimization, indicators and warnings. The exposition is organized into four relatively independent parts: an introduction and overview of typical enterprise networks and the graph theoretical prerequisites for all algorithms introduced later; an in-depth treatise of usage of various graph distances for event detection; a detailed exploration of properties of underlying graphs with modeling applications; and a theoretical and applied treatment of network behavior inferencing and forecasting using sequences of graphs. Based on many years of applied research on generic network dynamics, this work covers a number of elegant applications (including many new and experimental results) of traditional graph theory algorithms and techniques to computationally tractable network dynamics analysis to motivate network analysts, practitioners and researchers alike. The material is also suitable for graduate courses addressing state-of-the-art applications of graph theory in analysis of dynamic communication networks, dynamic databasing, and knowledge management.

Local area networks (Computer networks) --- Business enterprises --- Graph theory --- Mathematical models. --- Communication systems. --- Data processing. --- LANs (Computer networks) --- Local area computer networks --- Computer networks --- Electronic data processing --- Distributed processing --- Computer network architectures. --- Computer system performance. --- Computer software. --- Mathematics. --- Computer science. --- Computer Systems Organization and Communication Networks. --- System Performance and Evaluation. --- Algorithm Analysis and Problem Complexity. --- Applications of Mathematics. --- Math Applications in Computer Science. --- Probability and Statistics in Computer Science. --- Informatics --- Science --- Math --- Software, Computer --- Computer systems --- Architectures, Computer network --- Network architectures, Computer --- Computer architecture --- Computer organization. --- Computer system failures. --- Algorithms. --- Applied mathematics. --- Engineering mathematics. --- Computer science—Mathematics. --- Mathematical statistics. --- Mathematics --- Statistical inference --- Statistics, Mathematical --- Statistics --- Probabilities --- Sampling (Statistics) --- Engineering --- Engineering analysis --- Mathematical analysis --- Algorism --- Algebra --- Arithmetic --- Computer failures --- Computer malfunctions --- Failure of computer systems --- System failures (Engineering) --- Fault-tolerant computing --- Organization, Computer --- Electronic digital computers --- Statistical methods --- Foundations --- Failures

Choose an application

• Reference Manager
• EndNote
• RefWorks (Direct export to RefWorks)

Choose an application

• Reference Manager
• EndNote
• RefWorks (Direct export to RefWorks)

This book is a collection of scientific papers published during the last five years, showing a broad spectrum of actual research topics and techniques used to solve challenging problems in the areas of computer vision and image analysis. The book will appeal to researchers, technicians and graduate students. Sample Chapter(s)
Chapter 1: An Appearance-Based Method for Parametric Video Registration (2,352 KB)

Contents:

• An Appearance-Based Method for Parametric Video Registration (X Orriols et al.)
• Relevance of Multifractal Textures in Static Imag

Computer vision. --- Image analysis.

Choose an application

• Reference Manager
• EndNote
• RefWorks (Direct export to RefWorks)

Choose an application

• Reference Manager
• EndNote
• RefWorks (Direct export to RefWorks)

Hidden Markov models (HMMs) originally emerged in the domain of speech recognition. In recent years, they have attracted growing interest in the area of computer vision as well. This book is a collection of articles on new developments in the theory of HMMs and their application in computer vision. It addresses topics such as handwriting recognition, shape recognition, face and gesture recognition, tracking, and image database retrieval.This book is also published as a special issue of the International Journal of Pattern Recognition and Artificial Intelligence (February 2001).

Computer vision --- Optical pattern recognition --- Markov processes. --- Analysis, Markov --- Chains, Markov --- Markoff processes --- Markov analysis --- Markov chains --- Markov models --- Models, Markov --- Processes, Markov --- Stochastic processes --- Optical data processing --- Pattern perception --- Perceptrons --- Visual discrimination --- Machine vision --- Vision, Computer --- Artificial intelligence --- Image processing --- Pattern recognition systems --- Mathematical models.