UID:
almahu_9948621377202882
Format:
353 p.
,
online resource.
Edition:
1st ed. 1999.
ISBN:
9783642585500
Content:
This practical introduction focuses on how to build integrated solutions to industrial vision problems from individual algorithms. It gives a hands-on guide for setting up automated visual inspection systems using real-world examples and the NeuroCheck(Registered Trademark) software package. This software has actually been tested on a production line. Based on many years of experience in industries, the editors explain all the (mostly unpublished but essential) details encountered in the creation of real-world vision systems. With the original NeuroCheck (R) software package and all the example images included on CD-ROM, readers can work their way through the described inspection tasks and carry out their own experiments.
Note:
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.
In:
Springer Nature eBook
Additional Edition:
Printed edition: ISBN 9783540664109
Additional Edition:
Printed edition: ISBN 9783642585517
Additional Edition:
Printed edition: ISBN 9783642636424
Language:
English
DOI:
10.1007/978-3-642-58550-0
URL:
https://doi.org/10.1007/978-3-642-58550-0
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