1.1 Preface.

1.2 Design and Structure of the Eye.

1.3 Optical Aberrations and Consequences for Visual Performance.

1.4 Chromatic Aberration.

1.5 Neural Adaptation to Monochromatic Aberrations.

1.6 Optimizing Retinal Processing with Limited Cell Numbers, Space and Energy.

1.7 Adaptation to Different Light Levels.

1.8 Rod and Cone Responses.

1.9 Spiking and Coding.

1.10 Temporal and Spatial Performance.

1.11 ON/OFF Structure, Division of theWhole Illuminance Amplitude in Two Segments.

1.12 Consequences of the Rod and Cone Diversity on Retinal Wiring.

1.13 Motion Sensitivity in the Retina.

1.14 Visual Information Processing in Higher Centers.

1.15 Effects of Attention.

1.16 Color Vision, Color Constancy, and Color Contrast.

1.17 Depth Perception.

1.18 Adaptation in the Visual System to Color, Spatial, and Temporal Contrast.

1.19 Conclusions.

References.

2 Introduction to Building a Machine Vision Inspection (Axel Telljohann, Consulting Team Machine Vision (CTMV)).

2.1 Preface.

2.2 Specifying a Machine Vision System.

2.3 Designing a Machine Vision System.

2.4 Costs.

2.5 Words on Project Realization.

2.6 Examples.

3 Lighting in Machine Vision (I. Jahr, Vision & Control GmbH).

3.1 Introduction.

3.2 Demands on Machine Vision lighting.

3.3 Light used in Machine Vision.

3.4 Interaction of Test Object and Light.

3.5 Basic Rules and Laws of Light Distribution.

3.6 Light Filters.

3.7 Lighting Techniques and Their Use.

3.8 Lighting Control.

3.9 Lighting Perspectives for the Future.

References.

4 Optical Systems in Machine Vision (Dr. Karl Lenhardt, Jos. Schneider OptischeWerke GmbH).

4.1 A Look on the Foundations of Geometrical Optics.

4.2 Gaussian Optics.

4.3 The Wave Nature of Light.

4.4 Information Theoretical Treatment of Image Transfer and Storage.

4.5 Criteria for Image Quality.

4.6 Practical Aspects.

References.

5 Camera Calibration (R. Godding, AICON 3D Systems GmbH).

5.1 Introduction.

5.2 Terminology.

5.3 Physical Effects.

5.4 Mathematical Calibration Model.

5.5 Calibration and Orientation Techniques.

5.6 Verification of Calibration Results.

5.7 Applications.

References.

6 Camera Systems in Machine Vision (Horst Mattfeldt, Allied Vision Technologies GmbH).

6.1 Camera Technology.

6.2 Sensor Technologies.

6.3 CCD Image Artifacts.

6.4 CMOS Image Sensor.

6.5 Block Diagrams and their Description.

6.6 Digital Cameras.

6.7 Controlling Image Capture.

6.8 Configuration of the Camera.

6.9 Camera Noise1.

6.10 Digital Interfaces.

References.

7 Camera Computer Interfaces (Tony Iglesias, Anita Salmon, Johann Scholtz, Robert Hedegore, Julianna Borgendale, Brent Runnels, Nathan McKimpson, National Instruments).

7.1 Overview.

7.2 Analog Camera Buses.

7.3 Parallel Digital Camera Buses.

7.4 Standard PC Buses.

7.5 Choosing a Camera Bus.

7.6 Computer Buses.

7.7 Choosing a Computer Bus.

7.8 Driver Software.

7.9 Features of a Machine Vision System.

8 Machine Vision Algorithms (Dr. Carsten Steger, MVTec Software GmbH).

8.1 Fundamental Data Structures.

8.2 Image Enhancement.

8.3 Geometric Transformations.

8.4 Image Segmentation.

8.5 Feature Extraction.

8.6 Morphology.

8.7 Edge Extraction.

8.8 Segmentation and Fitting of Geometric Primitives.

8.9 Template Matching.

8.10 Stereo Reconstruction.

8.11 Optical Character Recognition.

References.

9 Machine Vision in Manufacturing (Dr.-Ing. Peter Waszkewitz, Robert Bosch GmbH).

9.1 Introduction.

9.2 Application Categories.

9.3 System Categories.

9.4 Integration and Interfaces.

9.5 Mechanical Interfaces.

9.6 Electrical Interfaces.

9.7 Information Interfaces.

9.8 Temporal Interfaces.

9.9 Human–Machine Interfaces.

9.10 Industrial Case Studies.

9.11 Constraints and Conditions.

References.

Index.