Computational Imaging and Vision

Theory and Applications

By: N. Sebe, Michael Lew

Write A Review

Hardcover | 30 April 2003

At a Glance

Hardcover
232 Pages

Dimensions(cm)
24.13 x 16.51 x 1.91

Hardcover

$169.00

or 4 interest-free payments of $42.25 with

Ships in 5 to 7 business days

From the foreword by Thomas Huang:
"During the past decade, researchers in computer vision have found that probabilistic machine learning methods are extremely powerful. This book describes some of these methods. In addition to the Maximum Likelihood framework, Bayesian Networks, and Hidden Markov models are also used. Three aspects are stressed: features, similarity metric, and models. Many interesting and important new results, based on research by the authors and their collaborators, are presented.

Although this book contains many new results, it is written in a style that suits both experts and novices in computer vision."

Shipping

	Standard Shipping	Express Shipping
Metro postcodes:	$9.99	$14.95
Regional postcodes:	$9.99	$14.95
Rural postcodes:	$9.99	$14.95

Orders over $89.00 qualify for free shipping.

How to return your order

At Booktopia, we offer hassle-free returns in accordance with our returns policy. If you wish to return an item, please get in touch with Booktopia Customer Care.

Additional postage charges may be applicable.

Defective items

If there is a problem with any of the items received for your order then the Booktopia Customer Care team is ready to assist you.

For more info please visit our Help Centre.

You Can Find This Book In

Non-Fiction Computing & I.T.Computer Science Mathematical Theory of Computation Artificial Intelligence Computer Vision Medicine Medicine in General Graphical & Digital Media Applications Databases Data Warehousing Computer Security

Data Encryption

Other Editions and Formats

Paperback

Published: 6th December 2010

View Product

Foreword	p. xi
Preface	p. xiii
Introduction	p. 1
Visual Similarity	p. 2
Color	p. 4
Texture	p. 7
Shape	p. 9
Stereo	p. 11
Motion	p. 13
Facial expression	p. 13
Summary	p. 15
Evaluation of Computer Vision Algorithms	p. 16
Overview of the Book	p. 19
Maximum Likelihood Framework	p. 25
Introduction	p. 25
Statistical Distributions	p. 26
Gaussian Distribution	p. 27
Exponential Distribution	p. 38
Cauchy Distribution	p. 41
Robust Statistics	p. 43
Outliers	p. 44
Maximum Likelihood Estimators	p. 45
Maximum Likelihood in Relation to Other Approaches	p. 47
Our Maximum Likelihood Approach	p. 50
Scale Parameter Estimation in a Cauchy Distribution	p. 54
Experimental Setup	p. 57
Concluding Remarks	p. 59
Color Based Retrieval	p. 61
Introduction	p. 61
Colorimetry	p. 64
Color Models	p. 64
RGB Color System	p. 65
HSV Color System	p. 66
l[subscript 1]l[subscript 2]l[subscript 3] Color System	p. 67
Color Based Retrieval	p. 68
Color Indexing	p. 69
Experiments with the Corel Database	p. 73
Early Experiments	p. 73
Usability Issues	p. 74
Printer-Scanner Noise Experiments	p. 75
Color Model	p. 76
Quantization	p. 76
Distribution Analysis	p. 77
Experiments with the Objects Database	p. 79
Concluding Remarks	p. 81
Robust Texture Analysis	p. 83
Introduction	p. 83
Human Perception of Texture	p. 86
Texture Features	p. 87
Texture Distribution Models	p. 88
Gray-level differences	p. 89
Laws' texture energy measures	p. 89
Center-symmetric covariance measures	p. 89
Local binary patterns and trigrams	p. 91
Complementary feature pairs	p. 91
Gabor and Wavelet Models	p. 92
Texture Classification Experiments	p. 95
Distribution Analysis	p. 97
Misdetection Rates	p. 99
Summary	p. 104
Texture Retrieval Experiments	p. 104
Texture Features	p. 105
Experiments Setup	p. 106
Similarity Noise for QMF-Wavelet Transform	p. 106
Similarity Noise for Gabor Wavelet Transform	p. 108
Concluding Remarks	p. 109
Shape Based Retrieval	p. 111
Introduction	p. 111
Human Perception of Visual Form	p. 113
Active Contours	p. 118
Behavior of Traditional Active Contours	p. 120
Generalized Force Balance Equations	p. 124
Gradient Vector Flow	p. 125
Invariant Moments	p. 130
Experiments	p. 131
Conclusions	p. 134
Robust Stereo Matching and Motion Tracking	p. 135
Introduction	p. 135
Stereoscopic Vision	p. 137
Stereo Matching	p. 138
Related Work	p. 142
Stereo Matching Algorithms	p. 144
Template Based Algorithm	p. 144
Multiple Windows Algorithm	p. 146
Cox' Maximum Likelihood Algorithm	p. 147
Stereo Matching Experiments	p. 150
Stereo Sets	p. 151
Stereo Matching Results	p. 151
Summary	p. 157
Motion Tracking Experiments	p. 157
Concluding Remarks	p. 160
Facial Expression Recognition	p. 163
Introduction	p. 164
Emotion Recognition	p. 166
Judgment Studies	p. 167
Review of Facial Expression Recognition	p. 167
Face Tracking and Feature Extraction	p. 171
The Static Approach: Bayesian Network Classifiers	p. 173
Continuous Naive-Bayes: Gaussian and Cauchy Naive Bayes Classifiers	p. 175
Beyond the Naive-Bayes Assumption: Finding Dependencies among Features Using a Gaussian TAN Classifier	p. 176
The Dynamic Approach: Expression Recognition Using Multi-level HMMs	p. 179
Hidden Markov Models	p. 182
Expression Recognition Using Emotion-Specific HMMs	p. 183
Automatic Segmentation and Recognition of Emotions Using Multi-level HMM	p. 184
Experiments	p. 187
Results Using the Chen Database	p. 191
Person-Dependent Tests	p. 191
Person-Independent Tests	p. 193
Results Using the Cohn-Kanade Database	p. 194
Summary and Discussion	p. 195
References	p. 199
Index	p. 210
Table of Contents provided by Ingram. All Rights Reserved.

Computational Imaging and Vision

Theory and Applications

At a Glance

Hardcover

Shipping

How to return your order

Defective items

You Can Find This Book In

Other Editions and Formats

Paperback

More in Data Warehousing

Designing Data-Intensive Applications

The Big Ideas Behind Reliable, Scalable, and Maintainable Systems

Data-Centric Artificial Intelligence for Multidisciplinary Applications

Architecting an Apache Iceberg Lakehouse

A Scalable, Open-Source Data Platform

Building a Scalable Data Warehouse with Data Vault 2.0

Data Strategy

How to Use Data and Artificial Intelligence to Transform Your Business

Apache Iceberg: The Definitive Guide

Data Lakehouse Functionality, Performance, and Scalability on the Data Lake

SQL Cookbook

Query Solutions and Techniques for All SQL Users 2nd Edition

Data Governance: The Definitive Guide

People, Processes, and Tools to Operationalize Data Trustworthiness

Data Science on AWS

Implementing End-To-End, Continuous AI and Machine Learning Pipelines

Database Internals

A Deep Dive Into How Distributed Data Systems Work

SQL for Data Analysis

Advanced Techniques for Transforming Data Into Insights

Infonomics

How to Monetize, Manage, and Measure Information as an Asset for Competitive Advantage

In a Nutshell (O'Reilly)

In a Nutshell (O'Reilly)

Fundamentals of Data Observability

Implement Trustworthy End-To-End Data Solutions

Amazon Redshift: The Definitive Guide

Jump-Start Analytics Using Cloud Data Warehousing

Intro to Python for Computer Science and Data Science

Learning to Program with AI, Big Data and The Cloud, Global Edition

Data Science for Sustainable Development Goals

Indian Case Studies

Data Quality Techniques

Fix Your Data and Transform Your Business

Data Quality Techniques

Fix Your Data and Transform Your Business

Vector Databases

A Practical Introduction

Health Information Processing

11th China Health Information Processing Conference, CHIP 2025, Dongguan, China, November 22-24, 2025, Proceedings

ML and Generative AI in the Data Lakehouse

Building and Deploying AI Applications at Scale

Databricks Certified Data Engineer Associate Study Guide

In-Depth Guidance and Practice

Delta Lake: The Definitive Guide

Modern Data Lakehouse Architectures with Data Lakes

This product is categorised by