Vernon's Machine Vision: Table of Contents
- Preface & Table of Contents
- 1. An introduction to computer vision (Page 1)
- 1.1 Computer vision: image processing or artificial intelligence? (Page 1)
- 1.2 Industrial machine vision vs. image understanding (Page )
- 1.3 Sensory feedback for manufacturing systems: why vision? (Page 3)
- 1.4 Examples of industrial machine vision problems and solutions (Page 4)
- 1.4.1 Measurement of steel bars (Page 4)
- 1.4.2 Inspection of computer screens (Page 5)
- 1.5 A typical system architecture (Page 5)
- 2. Illumination and sensors (Page 1)
- 2.1 Illumination (Page 1)
- 2.2 Sensors (Page 2)
- 2.2.1 Image formation: elementary optics (Page 2)
- 2.2.2 Camera sensors (Page 3)
- 2.2.3 Camera interfaces and video standards (Page 5)
- 2.2.4 Characteristics of camera sensors (Page 5)
- 2.2.5 Commercially available cameras (Page 7)
- 3. Image acquisition and representation (Page 1)
- 3.1 Sampling and quantization (Page 1)
- 3.1.1 Spatial frequency and the effects of sampling (Page 1)
- 3.2 Inter-pixel distances (Page 4)
- 3.3 Adjacency conventions (Page 4)
- 3.4 Image acquisition hardware (Page 5)
- 3.5 Speed considerations (Page 7)
- 4. Fundamentals of digital image processing (Page 1)
- 4.1 Point operations (Page 1)
- 4.1.1 Contrast stretching (Page 2)
- 4.1.2 Thresholding (Page 3)
- 4.1.3 Noise suppression by image addition (Page 4)
- 4.1.4 Background subtraction (Page 5)
- 4.2 Neighbourhood operations (Page 5)
- 4.2.1 Convolution (Page 5)
- 4.2.2 Noise suppression (Page 7)
- 4.2.3 Thinning, erosion and dilation (Page 9)
- 4.3 Geometric operations (Page 12)
- 4.3.1 Spatial warping (Page 12)
- 4.3.1.1 The spatial transformation (Page 13)
- 4.3.1.2 Grey-level interpolation (Page 14)
- 4.3.2 Registration and geometric decalibration (Page 16)
- 4.4 Mathematical morphology (Page 16)
- 4.4.1 Basic set theory (Page 16)
- 4.4.2 Structuring elements and hit or miss transformations (Page 16)
- 4.4.3 Erosion and dilation (Page 17)
- 4.4.4 Opening and closing (Page 18)
- 4.4.5 Thinning and the extraction of endpoints (Page 18)
- 4.4.6 Application: identification of endpoints of electrical wires (Page 19)
- 4.4.7 A brief introduction to grey-scale mathematical morphology (Page 19)
- 5. The segmentation problem (Page 1)
- 5.1 Introduction: region- and boundary-based approaches (Page 1)
- 5.2 Thresholding (Page 2)
- 5.2.1 Global, local, and dynamic approaches (Page 2)
- 5.2.2 Threshold selection (Page 2)
- 5.3 An overview of edge detection techniques (Page 4)
- 5.3.1 Gradient- and difference-based operators (Page 5)
- 5.3.2 Template matching (Page 8)
- 5.3.3 Edge fitting (Page 10)
- 5.3.4 Statistical techniques (Page 11)
- 5.3.5 Assessment of edge detection (Page 12)
- 5.4 Region growing (Page 12)
- 5.4.1 The split and merge procedure using quad-trees (Page 12)
- 5.5 Boundary detection (Page 13)
- 5.5.1 Boundary refining (Page 13)
- 5.5.2 Graph-theoretic techniques (Page 13)
- 5.5.3 Dynamic programming (Page 14)
- 5.5.4 Contour following (Page 14)
- 6. Image Analysis (Page 1)
- 6.1 Introduction: inspection, location, and identification (Page 1)
- 6.2 Template matching (Page 1)
- 6.2.1 Measures of similarity (Page 1)
- 6.2.2 Local template matching (Page 2)
- 6.3 Decision-theoretic approaches (Page 3)
- 6.3.1 Components of statistical pattern recognition process (Page 3)
- 6.3.2 Simple feature extraction (Page 3)
- 6.3.3 Classification (Page 4)
- 6.3.3.1 A synopsis of classification using Bayes' rule (Page 5)
- 6.4 The Hough transform (Page 7)
- 6.4.1 Hough transform for line detection and circle detection (Page 7)
- 6.4.2 The generalized Hough transform (Page 9)
- 6.5 Histogram analysis (Page 10)
- 7. An overview of techniques for shape description (Page 1)
- 7.1 A taxonomy of shape detectors (Page 1)
- 7.2 External scalar transform descriptors: features of the boundary (Page 1)
- 7.3 Internal scalar transform descriptors: features of the region (Page 2)
- 7.4 External space domain descriptors: spatial organization of the boundary (Page 3)
- 7.4.1 An algorithm for resampling the boundary chain codes (Page 5)
- 7.5 Internal space domain descriptors: spatial organization of the region (Page 6)
- 8. Robot programming and robot vision (Page 1)
- 8.1 A brief review of robot programming methodologies (Page 1)
- 8.2 Description of object pose with homogeneous transformations (Page 2)
- 8.3 Robot programming: a wire crimping task specification (Page 5)
- 8.4 A simple robot-programming language (Page 13)
- 8.5 Two vision algorithms for identifying ends of wires (Page 17)
- 8.5.1 A binary vision algorithm (Page 17)
- 8.5.2 A grey-scale vision algorithm (Page 19)
- 8.5.3 The vision/manipulator interface (Page 20)
- 8.6 The camera model and the inverse perspective transformation (Page 21)
- 8.6.1 The camera model (Page 21)
- 8.6.2 The inverse perspective transformation (Page 23)
- 8.6.3 Recovery of the third dimension (Page 24)
- 8.7 Three-dimensional vision using structured light (Page 24)
- 9. An introduction to image understanding (Page 1)
- 9.1 Representations and information processing: from images to object models (Page 1)
- 9.2 Organization of visual processes (Page 2)
- 9.3 Visual representations (Page 3)
- 9.3.1 The raw primal sketch (Page 3)
- 9.3.2 The full primal sketch (Page 3)
- 9.3.3 The two-and-a-half dimensional sketch (Page 6)
- 9.3.4 Three-dimensional models (Page 8)
- 9.3.4.1 Volumetric representations (Page 8)
- 9.3.4.2 Skeletal representations (Page 8)
- 9.3.4.3 Surface representations (Page 9)
- 9.3.5 The extended Gaussian image (Page 10)
- 9.4 Visual processes (Page 11)
- 9.4.1 Stereopsis (Page 11)
- 9.4.2 Camera motion (Page 11)
- 9.4.3 Shading (Page 17)
- 9.5 Concluding remarks (Page 20)
- Appendix: Separability of the Laplacian of Gaussian Operator (Page 22)
- Index (Page 23)
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