Beschreibung
InhaltsangabeOne: Introduction.- 1. Intelligent manufacturing.- 1.1 Introduction.- 1.2 Definition of intelligence.- 1.3 Role of vision in intelligence.- 1.4 Modem manufacturing systems.- 1.5 Considerations in intelligent manufacturing systems design.- 2. Intelligent visual inspection.- 2.1 Vision systems for intelligent manufacturing.- 2.2 Definition of visual inspection.- 2.3 Objectives of visual inspection.- 2.4 Inspection errors.- 2.5 Categories of inspection.- 2.6 Areas of application for visual inspection.- 2.7 Benefits of automated inspection.- 2.8 Commercially applied inspection systems.- 2.9 Limitations of automated visual inspection.- 2.10 Research goals.- Two: Fundamentals of Artificial Vision Systems.- 3. Biological vision systems.- 3.1 Introduction.- 3.2 Basic physiology of vision systems.- 3.3 Human visual cognition.- 3.4 Object recognition.- 3.5 Summary.- 4. Artificial neural networks for pattern recognition.- 4.1 Introduction.- 4.2 Early artificial neural networks.- 4.3 Backpropagation neural networks.- 4.4 Self-organizing maps.- 4.5 Adaptive resonance theory and its derivatives.- 4.6 Neocognitron neural network.- 4.7 HAVNET neural network.- 4.8 Summary.- Three: Artificial Vision Systems Design.- 5. Image acquisition and storage.- 5.1 Introduction.- 5.2 Cameras.- 5.3 Object lighting and presentation.- 5.4 Image acquisition cards.- 5.5 Image processing hardware.- 5.6 Image formats.- 6.6 Low-level image processing.- 6.1 Introduction.- 6.2 Image size reduction.- 6.3 Noise removal and filtering.- 6.4 Thresholding and histograms.- 6.5 Region growing and hole filling.- 6.6 Edge detection.- 6.7 Primitive image features.- 6.8 Summary.- 7. Intermediate image processing.- 7.1 Introduction.- 7.2 Color representation and processing.- 7.3 Shape from shading.- 7.4 Stereo vision.- 7.5 Analysis of visual motion.- 7.6 Grouping primitive features into complex features.- 7.7 Summary.- 8. Computational approach to artificial vision.- 8.1 Introduction.- 8.2 The work of David Marr.- 8.3 ACRONYM vision system.- 8.4 SCERPO vision system.- 8.5 Recognition by components theory and the PARVO vision system.- 8.6 Summary.- 9. Connectionist approach to artificial vision.- 9.1 Introduction.- 9.2 Grossberg vision model.- 9.3 Seibert-Waxman vision model.- 9.4 CAMERA vision model.- 10. Experimental evaluation of the CAMERA vision model.- 10.1 Introduction.- 10.2 Experimental apparatus and conditions.- 10.3 Recognition of simple two-dimensional objects.- 10.4 Recognition of complex two-dimensional objects.- 10.5 Recognition of three-dimensional objects.- 10.6 Recognition accuracy.- 10.7 Recognition time.- 10.8 Summary of CAMERA evaluation.- Four: Case Studies.- 11. Automated visual inspection systems.- 11.1 Introduction.- 11.2 Inspection of polished silicon wafers.- 11.3 Inspection of pharmaceutical blister packages.- 11.4 Summary.- 12. Future of automated visual inspection.- 12.1 Introduction.- 12.2 Proposed flexible manufacturing system.- 12.3 Challenging visual inspection problem.- 12.4 Summary.- Appendix A.- Appendix B.- References.
