This book is a companion book to the comprehensive text entitled Image Processing, Analysis, and Machine Vision by M. Sonka, V. Hlavac, and R. Boyle. This workbook provides additional material for readers of Sonka and is similarly structured. Written for students, teachers and practitioners to acquire practical understanding in a hands on fashion, this book provides the reader with short-answer questions, problems and selected algorithms from the main text using MATLAB in levels of varying difficulty. These resources can be used as extra practice for students to reinforce the material studied within the main text or can be useful as test materials for teachers.
Product Identifiers
Publisher
Course Technology
ISBN-10
0495295957
ISBN-13
9780495295952
eBay Product ID (ePID)
102956175
Product Key Features
Author
Jan Kybic, Tomas Svoboda, Vaclav Hlavac
Publication Name
Image Processing, Analysis and and Machine Vision-A Matlab Companion
Format
Perfect
Language
English
Features
New Edition
Publication Year
2007
Type
Textbook
Number of Pages
256 Pages
Dimensions
Item Length
9.2in
Item Height
0.6in
Item Width
7.9in
Item Weight
15.2 Oz
Additional Product Features
Edition Description
New Edition
Table of Content
Preface 0.1 References Chapter 1 - Introduction 1.1 References Chapter 2 - The Image, its Representation and Properties 2.1 Chapter Introduction 2.2 Short-answer Questions 2.3 Problems 2.4 References Chapter 3 - The Image, its Mathematical and Physical Background 3.1 Chapter Introduction 3.2 Short-answer Questions 3.3 Problems 3.4 References Chapter 4 - Data Structures for Image Analysis 4.1 Chapter Introduction 4.2 Short-answer Questions 4.3 Problems 4.4 Co-occurrence matrix 4.5 References Chapter 5 - Image Pre-Processing 5.1 Chapter Introduction 5.2 Short-answer Questions 5.3 Problems 5.4 Gray-scale Transformation, Histogram Equalization 5.5 Geometric Transformation 5.6 Smoothing Using a Rotation Mask 5.7 Image Sharpening by Using Laplacian 5.8 Harris Corner Detector 5.9 References Chapter 6 - Segmentation I 6.1 Chapter Introduction 6.2 Short-answer Questions 6.3 Problems 6.4 References 6.5 Iterative Threshold Selection 6.6 Line Detection Using Hough Transform 6.7 Dynamic Programming Boundary Tracing 6.8 Region Merging via Boundary Melting 6.9 Removal of Small Regions Chapter 7 - Segmentation II 7.1 Chapter Introduction 7.2 Short-answer Questions 7.3 Problems 7.4 Mean Shift Segmentation 7.5 Active Contours (snakes) 7.6 Gradient Vector Flow Snakes 7.7 Level Sets 7.8 Graph Cut Segmentation 7.9 References Chapter 8 - Shape Representation and Description 8.1 Chapter Introduction 8.2 Short-answer Questions 8.3 Problems 8.4 References Chapter 9 - Object Recognition 9.1 Chapter Introduction 9.2 Short-answer Questions 9.3 Problems 9.4 References Chapter 10 - Image Understanding 10.1 Chapter Introduction 10.2 Short-answer Questions 10.3 Problems 10.4 References Chapter 11 - 3D Vision, Geometry 11.1 Chapter Introduction 11.2 Short-answer Questions 11.3 Problems 11.4 Mathematical Description of a Camera 11.5 Visualize a Camera in a 3D Plot 11.6 Conversion of Rotation Parameters 11.7 RQ Matrix Decomposition 11.8 Homography Estimation From Point Correspondences -DLT Method 11.8.1 Example of Homography Mapping 11.9 Isotropic Point Normalization 11.10 3D Point Reconstruction - Linear Method 11.11 References Chapter 12 - Use of 3D Vision 12.1 Chapter Introduction 12.2 Short-answer Questions 12.3 Problems 12.4 References Chapter 13 - Mathematical Morphology 13.1 Chapter Introduction 13.2 Short-answer Questions 13.3 Problems 13.4 References Chapter 14 - Image Data Compression 14.1 Chapter Introduction 14.2 Short-answer Questions 14.3 Problems 14.4 References Chapter 15 - Texture 15.1 Chapter Introduction 15.2 Short-answer Questions 15.3 Problems 15.4 References Chapter 16 - Motion Analysis 16.1 Chapter Introduction 16.2 Short-answer Questions 16.3 Problems 16.4 Particle Filtering 16.5 Kernel-Based Tracking 16.6 References