Providing all the necessary theoretical tools, this comprehensive introduction to machine vision shows how these tools are applied in actual image processing and machine vision systems. A key feature is the inclusion of many programming exercises giving insights into the development of practical image processing algorithms. A CD-ROM containing software and data used in these exercises is also included. Aimed at graduate students in electrical engineering, computer science, and mathematics, the book will be a useful reference for professionals as well.
1. Introduction; 2. Review of mathematical principles; 3. Writing programs to process images; 4. Images: description and characterization; 5. Linear operators and kernels; 6. Image relaxation: restoration and feature extraction; 7. Mathematical morphology; 8. Segmentation; 9. Shape; 10. Consistent labeling; 11. Parametric transform; 12. Graphs and graph-theoretic concepts; 13. Image matching; 14. Statistical pattern recognition; 15. Clustering; 16. Syntactic pattern recognition; 17. Applications; 18. Automatic target recognition.