1.1 Human, Computer and Machine Vision
1.2 Vision System Hardware
1.3 Vision System Software
1.4 Machine Vision System Design
1.5 NeatVision: Where Java meets Machine Vision

2.1 The History of Java
2.2 Object Oriented Programming
2.3 Java Language Basics
2.4 Applications and Applets
2.5 Java and Image Processing
2.6 AdditionalClasses
2.7 Double Bu ering
2.8 Recent Additions toJava for Imaging
2.9 Additional Information on Java
2.10 Conclusion

3.1 Elementary Image Processing Functions
3.2 LocalOperators
3.3 Binary Images
3.4 Global Image Transforms
3.5 Conclusion

4.1 Binary Mathematical Morphology
4.2 Grey Scale Mathematical Morphology
4.3 Morphological Reconstruction
4.4 Morphological Segmentation
4.5 Case Study: Geometric Packing
4.6 Morphological System Implementation
4.7 Conclusion

5.1 Texture and Images
5.2 Edge Density
5.3 Monte-Carlo Method
5.4 Auto-Correlation Function (ACF)
5.5 Fourier Spectral Analysis
5.6 Histogram Features
5.7 Grey Level Run Length Method (GLRLM)
5.8 Grey Level Di erence Method (GLDM)
5.9 Co-occurrence Analysis
5.10 Morphological Texture Analysis
5.11 Fractal Analysis
5.12 Textural Energy
5.13 Texture Spectrum Method
5.14 Local Binary Patterns (LBP)
5.15 Random Field Models
5.16 Spatial / Frequency Methods
5.17 Autoregressive Model
5.18 Structural Approaches to Texture Analysis
5.19 Conclusion

6.1 ColourCameras
6.2 Red-Green-Blue (RGB) Colour Representation
6.3 Hue-Saturation-Intensity (HSI) Colour Representation
6.4 Opponent Process Representation
6.5 YIQ Colour Representation
6.6 YUV Colour Representation
6.7 CIE Chromaticity Diagram
6.8 CIEXYZ Colour Representation
6.9 CIELUV Colour Representation
6.10 CIELAB Colour Representation
6.11 Spatial CIELAB Colour Representation
6.12 Programmable Colour Filter (PCF)
6.13 Conclusion

7.1 Visual Programming in NeatVision
7.2 Java Programming in NeatVision
7.3 The NeatVision Application
7.4 Sample Applications
7.5 Conclusion

References
Index