Industrial Image Processing: Visual Quality Control in Manufacturing

by Christian Demant, Bernd Streicher-Abel, Carsten Garnica

Overview

This practical introduction focuses on how to design integrated solutions for industrial vision tasks from individual algorithms. The book is now available in a revised second edition that takes into account the current technological developments, including camera technology and color imaging processing. It gives a hands-on guide for setting up automated visual inspection systems using real-world examples and the NeuroCheck® standard software that has proven industrial strength integrated in thousands of applications in real-world production lines. Based on many years of experience in industry, the authors explain all the essential details encountered in the creation of vision system installations. With example material and a demo version of the software found on "extras.springer.com" readers can work their way through the described inspection tasks and carry out their own experiments.

Product Details

ISBN-13:	9783662495919
Publisher:	Springer Berlin Heidelberg
Publication date:	10/02/2013
Edition description:	2nd revised ed. 2013
Pages:	369
Product dimensions:	6.10(w) x 9.25(h) x (d)

About the Author

Christian Demant received his Engineering Diploma in Technical Cybernetics at the University of Stuttgart. He worked for two years at the Research and Development Center of Robert Bosch GmbH in the department of Applied Vision Technologies. In 1993 he co-founded NeuroCheck GmbH in Remseck/Stuttgart and is currently the General Manager of the company. He is in charge of the architecture and development of the core NeuroCheck software product. In addition he is one of the founders and current Directors of Industrial Vision Systems Ltd based in Oxford, United Kingdom.

Bernd Streicher-Abel received his Engineering Diploma in Electrical Engineering at the University of Stuttgart. He worked for six years at the development site of Robert Bosch GmbH in the department of Applied Vision Technologies. In 1993 he co-founded NeuroCheck GmbH in Remseck/Stuttgart and is currently the General Manager of the company. He is in charge of the development and integration of turnkey machine vision solutions.

  

Carsten Garnica received his Engineering Diploma in Geoinformatics at the University of Applied Sciences of Mainz. He worked for five years as a Research Associate at the Institute for Spatial Information and Surveying Technology in Mainz. Since 2001 he has been an employee of NeuroCheck GmbH in Stuttgart and contributes to the design of the NeuroCheck software and supports customers implementing machine vision applications.

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.- 1.4.1 Hardware setup.- 1.4.2 Signal flow in the process environment.- 1.4.3 Signal flow within an image processing system.- 1.5 Solution approach.- 1.6 Introductory example.- 1.6.1 Character recognition.- 1.6.2 Thread depth.- 1.6.3 Presence verification.- 1.7 From here.- 2. Overview: Image Preprocessing.- 2.1 Gray scale transformations.- 2.1.1 Look-up tables.- 2.1.2 Linear gray level scaling.- 2.1.3 Contrast enhancement.- 2.1.4 Histogram equalization.- 2.1.5 Local contrast enhancement.- 2.2 Image arithmetic.- 2.2.1 Image addition and averaging.- 2.2.2 Image subtraction.- 2.2.3 Minimum and maximum of two images.- 2.2.4 Shading correction.- 2.3 Linear filters.- 2.3.1 Local operations and neighborhoods.- 2.3.2 Principle of linear filters.- 2.3.3 Smoothing filter.- 2.3.4 Edge 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.- 3.1.1 Positioning using the entire object.- 3.1.2 Positioning using an edge.- 3.2 Orientation of an individual object.- 3.2.1 Orientation computation using principal axis.- 3.2.2 Distance-versus-angle signature.- 3.3 Robot positioning.- 3.3.1 Setting of tasks.- 3.3.2 Image processing components.- 3.3.3 Position determination on one object.- 3.3.4 Orientation of an object configuration.- 3.3.5 Comments concerning position adjustment.- 3.4 Key terms.- 4. Overview: Segmentation.- 4.1 Regions of interest.- 4.1.1 Regions and objects.- 4.2 Thresholding.- 4.2.1 Thresholds.- 4.2.2 Threshold determination from histogram analysis.- 4.2.3 Gray level histograms.- 4.2.4 Generalizations of thresholding.- 4.3 Contour tracing.- 4.3.1 Pixel connectedness.- 4.3.2 Generating object contours.- 4.3.3 Contour representation.- 4.4 Edge based methods.- 4.4.1 Edge probing in industrial image scenes.- 4.4.2 Edge detection with subpixel accuracy.- 4.5 Template matching.- 4.5.1 Basic operation.- 4.5.2 Optimizing template matching.- 4.5.3 Comments on template matching.- 4.6 Key terms.- 5. Mark Identification.- 5.1 Bar code identification.- 5.1.1 Principle of gray-level-based bar code identification.- 5.1.2 Bar code symbologies.- 5.1.3 Examples of industrial bar code identification.- 5.1.4 Further information.- 5.2 Character recognition.- 5.2.1 Laser-etched characters on an IC.- 5.2.2 Basic configuration of the character recognition.- 5.2.3 Fundamental structure of a classifier application.- 5.2.4 Position adjustment on the IC.- 5.2.5 Improving character quality.- 5.2.6 Optimization in operation.- 5.3 Recognition of pin-marked digits on metal.- 5.3.1 Illumination.- 5.3.2 Preprocessing.- 5.3.3 Segmentation and classification.- 5.4 Block codes on rolls of film.- 5.5 Print quality inspection.- 5.5.1 Procedure.- 5.5.2 Print quality inspection in individual regions.- 5.5.3 Print quality inspection with automatic subdivision.- 5.6 Key terms.- 6. Overview: Classification.- 6.1 What is classification?.- 6.2 Classification as function approximation.- 6.2.1 Machine learning.- 6.2.2 Statistical foundations.- 6.2.3 Constructing classifiers.- 6.3 Instance-based classifiers.- 6.3.1 Nearest neighbor classifier.- 6.3.2 RCE networks.- 6.3.3 Radial basis functions.- 6.3.4 Vector quantization.- 6.3.5 Template matching.- 6.3.6 Remarks on instance-based classifiers.- 6.4 Function-based classifiers.- 6.4.1 Polynomial classifier.- 6.4.2 Multilayer perceptron-type neural networks.- 6.4.3 Representation of other classifiers as neural networks.- 6.5 Remarks on the application of neural networks.- 6.5.1 Composition of the training set.- 6.5.2 Feature scaling.- 6.5.3 Rejection.- 6.5.4 Arbitrariness.- 6.6 Key terms.- 7. Dimensional Checking.- 7.1 Gauging tasks.- 7.2 Simple gauging.- 7.2.1 Center point distance.- 7.2.2 Contour distances.- 7.2.3 Angle measurements.- 7.3 Shape checking on a punched part.- 7.3.1 Inspection task.- 7.3.2 Modeling contours by lines.- 7.3.3 Measuring the contour angle.- 7.4 Angle gauging on toothed belt.- 7.4.1 Illumination setup.- 7.4.2 Edge creation.- 7.5 Shape checking on injection-molded part.- 7.5.1 Computing radii.- 7.5.2 Remarks on model circle computation.- 7.6 High accuracy gauging on thread flange.- 7.6.1 Illumination and image capture.- 7.6.2 Subpixel-accurate gauging of the thread depth.- 7.7 Calibration.- 7.7.1 Calibration mode.- 7.7.2 Inspection-related calibration.- 7.8 Key terms.- 8. Overview: Image Acquisition and Illumination.- 8.1 Solid-state sensors.- 8.1.1 CCD sensor operation.- 8.1.2 Properties of CCD sensors.- 8.1.3 Image degradation.- 8.2 Standard video cameras.- 8.2.1 Basic structure.- 8.2.2 The video standard.- 8.2.3 Sampling of the line signal.- 8.2.4 Extensions of the video standard.- 8.2.5 Image quality.- 8.3 Other camera types.- 8.3.1 Progressive scan cameras.- 8.3.2 Asynchronous cameras.- 8.3.3 Digital cameras.- 8.3.4 Line-scan cameras.- 8.3.5 Additional camera properties.- 8.4 Transmission to the computer.- 8.4.1 Basic operation of a frame grabber.- 8.4.2 Frame grabbers for standard video cameras.- 8.4.3 Frame grabbers for other camera types.- 8.4.4 Direct digital transmission.- 8.5 Optical foundations.- 8.5.1 F-number.- 8.5.2 Thin lens imaging equation.- 8.5.3 Depth of field.- 8.5.4 Typical imaging situations.- 8.5.5 Aberrations.- 8.5.6 Lens selection.- 8.5.7 Special optical devices.- 8.6 Illumination technology.- 8.6.1 Light sources.- 8.6.2 Front lighting.- 8.6.3 Back Lighting.- 8.7 Key terms.- 9. Presence Verification.- 9.1 Simple presence verification.- 9.1.1 Part geometry.- 9.1.2 Illumination.- 9.1.3 Position adjustment.- 9.1.4 Segmentation.- 9.1.5 Evaluation.- 9.1.6 Segmentation with template matching.- 9.2 Simple gauging for assembly verification.- 9.2.1 Illumination.- 9.2.2 Inspection criteria.- 9.2.3 Object creation and measurement computation.- 9.2.4 Position adjustment.- 9.3 Presence verification using classifiers.- 9.3.1 Illumination.- 9.3.2 Inspection of the caulking.- 9.3.3 Type verification of the flange.- 9.4 Contrast-free presence verification.- 9.5 Key terms.- 10. Overview: Object Features.- 10.1 Basic geometrical features.- 10.1.1 Enclosing rectangle.- 10.1.2 Area and perimeter.- 10.1.3 Center of gravity.- 10.1.4 Axes and radii.- 10.2 Shape-descriptors.- 10.2.1 Curvature.- 10.2.2 Fiber features.- 10.2.3 Euler’s number.- 10.2.4 Moments and Fourier descriptors.- 10.3 Gray level features.- 10.3.1 First-order statistics.- 10.3.2 Texture 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.