MACHINE VISION

 

Ramesh Jain, Rangachar Kasturi, Brian G. Schunck

Published by McGraw-Hill, Inc., ISBN 0-07-032018-7, 1995

 

 

The field of machine vision, or computer vision, has been growing at a fast pace. As in most fast-developing fields, not all aspects of machine vision that are of interest to active researchers are useful to the designers and users of a vision system for a specific application.

This text is intended to provide a balanced introduction to machine vision. Basic concepts are introduced with only essential mathematical elements. The details to allow implementation and use of vision algorithm in practical application are provided, and engineering aspects of techniques are emphasized. This text intentionally omits theories of machine vision that do not have sufficient practical applications at the time.

This book is designed for people who want to apply machine vision to solve problems.

 

 

 

 

 

 

 

 

Chapter Index:

 

Front Matter

Chapter 1. Introduction (pp. 1-24)

1.1 Machine Vision

1.2 Relationships to Other Fields

1.3 Role of Knowledge

1.4 Image Geometry

14.l Perspective Projection

1.4.2 Coordinate Systems

1.5 Sam ling and Quantization

1.6 Image Definitions

1.7 Levels of Computation

1.7.1 Point Level

1.7.2 Local Level

1.7.3 Global Level

1.7.4 Object Level

1.8 Road Map

Chapter 2. Binary Image Processing (pp. 25-72)

2.1 Thresholding

2.2 Geometric Properties

2.2.1 Size

2.2.2 Position

2.2.3 Orientation

2.3 Projections

2.4 Run-Length Encoding

2.5 Binary Algorithms

2.5.1 Definitions

2.5.2 Component Labeling

2.5.3 Size Filter

2.5.4 Euler Number

2.5.5 Region Boundary

2.5.6 Area and Perimeter

2.5.7 Compactness

2.5.8 Distance Measures

2.5.9 Distance Transforms

2.5.10 Medial Axis

2.5.11 Thinning

2.5.12 Expanding and Shrinking

2.6 Morphological Operators

2.7 Optical Character Recognition

Chapter 3. Regions (pp. 73-111)

3.1 Regions and Edges

3.2 Region Segmentation

3.2.1 Automatic Thresholding

3.2.2 Limitations of Histogram Methods

3.3 Region Representation

3.3.1 Array Representation

3.3.2 Hierarchical Representations

3.3.3 Symbolic Representations

3.3.4 Data Structures for Segmentation

3.4 Split and Merge

3.4.1 Region Merging

3.4.2 Removing Weak Edges

3.4.3 Region Splitting

3.4.4 Split and Merge

3.5 Region Growing

Chapter 4. Image Filtering (pp. 112-139)

4.1 Image Filtering

4.2 Histogram Modification

4.3 Linear Systems

4.4 Linear Filters

4.5 Median Filter

4.6 Gaussian Smoothing

4.5.1 Rotational Symmetry

4.5.2 Fourier Transform Property

4.5.3 Gaussian Separability

4.5.4 Cascading Gaussians

4.5.5 Designing Gaussian Filters

4.5.6 Discrete Gaussian Filters

Chapter 5. Edge Detection (pp. 140-185)

5.1 Gradient

5.2 Steps in Edge Detection

5.2.1 Roberts Operator

5.2.2 Sobel Operator

5.2.3 Prewitt Operator

5.2.4 Comparison

5.3 Second Derivative Operators

5.3.1 Laplacian Operator

5.3.2 Second Directional Derivative

5.4 Laplacian of Gaussian

5.5 Image Approximation

5.6 Gaussian Edge Detection

5.6.1 Canny Edge Detector

5.7 Subpixel Location Estimation

5.8 Edge Detector Performance

5.8.1 Methods for Evaluating Performance

5.8.2 Figure of Merit

5.9 Sequential Methods

5.10 Line Detection

Chapter 6. Contours (pp. 186-233)

6.1 Geometry of Curves

6.2 Digital Curves

6.2.1 Chain Codes

6.2.2 Slope Representation

6.2.3 Slope Density Function

6.3 Curve Fitting

6.4 Polyline Representation

6.4.1 Polyline Splitting

6.4.2 Segment Merging

6.4.3 Split and Merge

6.4.4 Hop-Along Algorithm

6.5 Circular Arcs

6.6 Conic Sections

6.7 Spline Curves

6.8 Curve Approximation

6.8.1 Total Regression

6.8.2 Estimating Corners

6.8.3 Robust Regression

6.8.4 Hough Transform

6.9 Fourier Descriptors

Chapter 7. Texture (pp. 234-248)

7.1 Introduction

7.2 Statistical Methods of Texture Analysis

7.3 Structural Analysis of Ordered Texture

7.4 Model-Based Methods for Texture Analysis

7.5 Shape from Texture

Chapter 8. Optics (pp. 249-256)

8.1 Lens Equation

8.2 Image Resolution

8.3 Depth of Field

8.4 View Volume

8.5 Exposure

Chapter 9. Shading (pp. 257-275)

9.1 Image Irradiance

9.1.1 Illumination

9.1.2 Reflectance

9.2 Surface Orientation

9.3 The Reflectance Map

9.3.1 Diffuse Reflectance

9.3.2 Scanning Electron Microscopy

9.4 Shape from Shading

9.5 Photometric Stereo

Chapter 10. Color (pp. 276-288)

10.1 Color Physics

10.2 Color Terminology

10.3 Color Perception

10.4 Color Processing

10.5 Color Constancy

10.6 Discussion

Chapter 11. Depth (pp. 289-308)

11.1 Stereo Imaging

11.1.1 Cameras in Arbitrary Position and Orientation

11.2 Stereo Matching

11.2.1 Edge Matching

11.2.2 Region Correlation

11.3 Shape from X

11.4 Range Imaging

11.4.1 Structured Lighting

11.4.2 Imaging Radar

11.5 Active Vision

Chapter 12. Calibration (pp. 309-364)

12.1 Coordinate Systems

12.2 Rigid Body Transformations

12.2.1 Rotation Matrices

12.2.2 Axis of Rotation

12.2.3 Unit Quaternions

12.3 Absolute Orientation

12.4 Relative Orientation

12.5 Rectification

12.6 Depth from Binocular Stereo

12.7 Absolute Orientation with Scale

12.8 Exterior Orientation

12.8.1 Calibration Example

12.9 Interior Orientation

12.10 Camera Calibration

12.10.1 Simple Method for Camera Calibration

12.10.2 Affine Method for Camera Calibration

12.10.3 Nonlinear Method for Camera Calibration

12.11 Binocular Stereo Calibration

12.12 Active Triangulation

12.13 Robust Methods

12.14 Conclusions

Chapter 13. Curves and Surfaces (pp. 365-405)

13.1 Fields

13.2 Geometry of Curves

13.3 Geometry of Surfaces

13.3.1 Planes

13.3.2 Differential Geometry

13.4 Curve Representations

13.4.1 Cubic Spline Curves

13.5 Surface Representations

13.5.1 Polygonal Meshes

13.5.2 Surface Patches

13.5.3 Tensor-Product Surfaces

13.6 Surface Interpolation

13.6.1 Triangular Mesh Interpolation

13.6.2 Bilinear Interpolation

13.6.3 Robust Interpolation

13.7 Surface Approximation

13.7.1 Regression Splines

13.7.2 Variational Methods

13.7.3 Weighted Spline Approximation

13.8 Surface Segmentation

13.8.1 Initial Segmentation

13.8.2 Extending Surface Patches

13.9 Surface Registration

Chapter 14. Dynamic Vision (pp. 406-458)

14.1 Change Detection

14.1.1 Difference Pictures

14.1.2 Static Segmentation and Matching

14.2 Segmentation Using Motion

14.2.1 Time-Varying Edge Detection

14.2.2 Stationary Camera

14.3 Motion Correspondence

14.4 Image Flow

14.4.1 Computing Image Flow

14.4.2 Feature-Based Methods

14.4.3 Gradient-Based Methods

14.4.4 Variational Methods for Image Flow

14.4.5 Robust Computation of Image Flow

14.4.6 Information in Image Flow

14.5 Segmentation Using a Moving Camera

14.5.1 Ego-Motion Complex Log Mapping

14.5.2 Depth Determination

14.6 Tracking

14.6.1 Deviation Function for Path Coherence

14.6.2 Path Coherence Function

14.6.3 Path Coherence in the Presence of Occlusion

14.6.4 Modified Greedy Exchange Algorithm

14.7 Shape from Motion

Chapter 15. Object Recognition (pp. 459-491)

15.1 System Components

15.2 Complexity of Object Recognition

15.3 Object Representation

15.3.1 Observer-Centered Representations

15.3.2 Object-Centered Representations

15.4 Feature Detection

15.5 Recognition Strategies

15.5.1 Classification

15.5.2 Matching

15.5.3 Feature Indexing

15.6 Verification

15.6.1 Template Matching

15.6.2 Morphological Approach

15.6.3 Symbolic

15.6.4 Analogical Methods

Appendix A. Mathematical Concepts (pp. 492-501)

A.1 Analytic Geometry

A.2 Linear Algebra

A.3 Variational Calculus

A.4 Numerical Methods

Appendix B. Statistical Methods (pp. 502-510)

B.1 Measurement Errors

B.2 Error Distributions

B.3 Linear Regression

B.4 Nonlinear Regression

Appendix C. Programming Techniques (pp. 511-518)

C.1 Image Descriptors

C.2 Mapping operations

C.3 Image File Formats

Bibliography (pp. 519-541)

Index (pp. 542-549)