Industrial Image Processing: Visual Quality Control in Manufacturing

Digital image processing has become a key technology in the area of manu- facturing and quality control. Increasing quality demands require inspection of every single part, which in turn will lead to a much more widespread use of automatic visual inspection systems in the near future. Furthermore, the documentation requirements of ISO 9000 and similar quality control standards can only be met by fully automated, networked inspection systems. On the other hand, despite a multitude of successful applications, digital image processing has not yet established itself as an accepted element of man- ufacturing technology. This holds true for the industrial practice as well as for the training of engineers. Digital image processing is still widely regarded as some kind of secret lore, mastered only by a small number of expensive -- experts. This impression of incomprehensibility frequently leads to the accusation of unreliability. The manufacturers of digital image processing systems in the industry are not least responsible for this state of affairs, due to their policy of giving the customer as little information as possible about the methods and technology used to inspect his products.

Table of Contents:
1. Introduction.- 1.1 Why write another book about image processing?.- 1.2 Possibilities and limitations.- 1.3 Types of inspection tasks.- 1.4 Structure of image processing systems.- 1.5 Solution approach.- 1.6 Introductory example.- 1.7 From here.- 2. Overview: Image Preprocessing.- 2.1 Gray scale transformations.- 2.2 Image arithmetic.- 2.3 Linear filters.- 2.4 Median filter.- 2.5 Morphological filters.- 2.6 Other non-linear filters.- 2.7 Global operations.- 2.8 Key terms.- 3. Positioning.- 3.1 Position of an individual object.- 3.2 Orientation of an individual object.- 3.3 Robot positioning.- 3.4 Key terms.- 4. Overview: Segmentation.- 4.1 Regions of interest.- 4.2 Thresholding.- 4.3 Contour tracing.- 4.4 Edge based methods.- 4.5 Template matching.- 4.6 Key terms.- 5. Mark Identification.- 5.1 Bar code identification.- 5.2 Character recognition.- 5.3 Recognition of pin-marked digits on metal.- 5.4 Block codes on rolls of film.- 5.5 Print quality inspection.- 5.6 Key terms.- 6. Overview: Classification.- 6.1 What is classification?.- 6.2 Classification as function approximation.- 6.3 Instance-based classifiers.- 6.4 Function-based classifiers.- 6.5 Remarks on the application of neural networks.- 6.6 Key terms.- 7. Dimensional Checking.- 7.1 Gauging tasks.- 7.2 Simple gauging.- 7.3 Shape checking on a punched part.- 7.4 Angle gauging on toothed belt.- 7.5 Shape checking on injection-molded part.- 7.6 High accuracy gauging on thread flange.- 7.7 Calibration.- 7.8 Key terms.- 8. Overview: Image Acquisition and Illumination.- 8.1 Solid-state sensors.- 8.2 Standard video cameras.- 8.3 Other camera types.- 8.4 Transmission to the computer.- 8.5 Optical foundations.- 8.6 Illumination technology.- 8.7 Key terms.- 9. Presence Verification.- 9.1 Simple presence verification.- 9.2 Simple gauging for assembly verification.- 9.3 Presence verification using classifiers.- 9.4 Contrast-free presence verification.- 9.5 Key terms.- 10. Overview: Object Features.- 10.1 Basic geometrical features.- 10.2 Shape-descriptors.- 10.3 Gray level features.- 10.4 Key terms.- 11. Outlook: Visual Inspection Projects.- A. Mathematical Notes.- A.1 Backpropagation training.- A.1.1 Neural networks — concept and history.- A.1.2 Fundamentals.- A.1.3 Backpropagation.- A.2 Computation of the depth of field.- A.2.1 Limit distances.- A.2.2 Depth of field at infinite distance.- A.2.3 Dependence of the depth of field on the focal length.- B. The Companion CD.- References.