Machine vision / Theory, algorithms, practicalities

Enhanced descriptions from Syndetics:

Machine vision may be defined as the automatic analysis of images by computer with the aim of controlling machines and monitoring real-world processes. This edition contains material on artificial neural networks, mathematical morphology, motion, invariance, texture analysis, x-ray inspection and foreign object detection. Intermediate level vision is examined in depth (especially Hough transforms), and automated visual inspection is discussed. The author considers theoretical aspects as well as practical applications, including perspective invariants and robust statistics.

Table of contents provided by Syndetics

• Vision, the Challenge
• I Low-Level Processing: Images and Imaging Operations
• Basic Image Filtering Operations
• Thresholding Techniques
• Locating Objects via Their Edges
• Binary Shape Analysis
• Boundary Pattern Analysis
• II Intermediate-Level Processing: Line Detection
• Circle Detection
• The Hough Transform and Its Nature
• Ellipse Detection
• Hole Detection
• Polygon and Corner Detection
• III Application Level Processing: Abstract Pattern Matching Techniques
• The Three-Dimensional World
• Tackling the Perspective n-Point Problem
• Motion
• Invariants and their Applications
• Automated Visual Inspection
• Statistical Pattern Recognition
• Biologically Inspired Recognition Schemes
• Texture
• Image Acquisition
• The Need for Speed: Real-Time Electronic Hardware Systems
• IV Perspectives on Vision: Machine Vision, Art or Science?
• Appendices
• References
• Subject Index
• Author Index
• Vision, the Challenge: Introduction
• Man and his Senses
• The Nature of Vision
• Automated Visual Inspection
• What This Book is About
• The Following Chapters
• I Low-Level Processing: Images and Imaging Operations: Image Processing Operations
• Convolutions and Point Spread Functions
• Sequential Versus Parallel Operations
• Basic Image Filtering Operations: Noise Suppression by Gaussian Smoothing
• Median Filtering
• Mode Filtering
• Bias Generated by Noise Suppression Filters
• Reducing Computational Load
• The Role of Filters in Industrial Applications of Vision
• Sharp-Unsharp Masking
• Thresholding Techniques: Region-Growing Methods
• Thresholding
• Adaptive Thresholding
• Locating Objects via Their Edges: Basic Theory of Edge Detection
• The Template Matching Approach
• Theory of 3 x 3 Template Operators
• Summary
• Design Constraints and Conclusions
• The Design of Differential Gradient Operators
• The Concept of a Circular Operator
• Detailed Implementation of Circular Operators
• Structured Bands of Pixels in Neighbourhoods of Various Sizes
• The Systematic Design of Differential Edge Operators
• Problems with the Above ApproachSome Alternative Schemes
• Binary Shape Analysis: Connectedness in Binary Images
• ObjectLabelling and Counting
• Metric Properties in Digital Images
• Size Filtering
• The Convex Hull and Its Computation
• Distance Functions and Their Uses
• Skeletons and Thinning
• Some Simple Measures for Shape Recognition
• Shape Description by Moments
• BoundaryTracking Procedures
• Boundary Pattern Analysis: Boundary Tracking Procedures
• Template Matchinga Reminder
• Centroidal Profiles
• Problems with the Centroidal Profile Approach
• The (s, () Plot
• Tackling the Problems of Occlusion
• Chain Code
• The (r, s) Plot
• Accuracy of Boundary Length Measures
• Concluding Remarks
• Bibliographical and Historical
• Notes
• II Intermediate-Level Processing: Line Detection: Application of the Hough Transform to Line Detection
• The Foot-of-Normal Method
• Longitudinal Line Localization
• Final Line Fitting
• Circle Detection: Hough-Based Schemes for Circular Object Detection
• The Problem of Unknown Circle Radius
• The Problem of Accurate Centre Location
• Overcoming the Speed Problem
• The Hough Transform and Its Nature: The Generalized Hough Transform
• Setting Up the Generalized Hough TransformSome Relevant Questions
• Spatial Matched Filtering in Images
• From Spatial Matched Filters to Generalized Hough Transforms
• Gradient Weighting Versus Uniform Weighting
• Summary
• Applying the Generalized Hough Transform to Line Detection
• An Instructive Example
• Tradeoffs to Reduce Computational Load
• The Effects of Occlusions for Objects with Straight Edges
• Fast Implementations of the HoughTransform
• The Approach of Gerig and Klein
• Ellipse Detection: The Diameter Bisection Method
• The Chord Tangent Method
• Finding the Remaining Ellipse Parameters
• Reducing Computational Load for the Generalized Hough Transform Method
• Comparing the Various Methods
• Hole Detection: The Template Matching Approach
• The Lateral Histogram Technique
• The Removal of Ambiguities in the Lateral Histrogram