Inhaltsangabe1. 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 measur