Image Processing of Edge and Surface Defects: Theoretical Basis of Adaptive Algorithms with Numerous Practical Applications / Edition 1

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Overview

The edge and surface inspection is one of the most important and most Challenging tasks in quality assessment in industrial production. Typical defects are cracks, inclusions, pores, surface, flakings, partial or complete tears of material surface and s.o. These defects can occur through defective source material or through extreme strain during machining process.

Detection of defects on a material surface can be complicated due to extremely varying degrees of material brightness or due to shadow areas, caused by the folding of the surface. Furthermore, impurities or surface discolourations can lead to artefacts that can be detected as pseudo-defects. The brightness conditions on the edge of material defects are interpreted as a Gauss distribution of a radiation and used for a physical model. Basing on this model, an essentially new set of adaptive edge-based algorithms was developed. Using these methods, different types of defects can be detected, without the measurements being dependent on local or global brightness conditions of the image taken. The new adaptive edge-based algorithms allow a defect detection on different materials, like metal, ceramics, plastics and stone.

These methods make it possible to explicitly detect all kinds of different defects independently of their size, form and position and of the surface to be inspected. The adaptive edge-based methods provide a very wide spectrum of applications.

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Editorial Reviews

From the Publisher
Aus den Rezensionen:

“... Die HeIligkeitsverhältnisse an der Kante einer Materialbeschädigung werden als Gauß'sche Verteilung einer Strahlung interpretiert und in einem physikalischen Modell erfasst. Basierend auf diesem Modell wurden neue Methoden entwickelt, mit denen unterschiedliche Fehlertypen unabhängig von den Helligkeitsbedingungen eines aufgenommenen Bildes ermittelt werden können. ... Zahlreiche Anwendungsbeispiele veranschaulichen die theoretischen Ausführungen.“ (in: QZ Qualität und Zuverlässigkeit, March/2010, Issue 3, S. 13)

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Product Details

  • ISBN-13: 9783642006821
  • Publisher: Springer Berlin Heidelberg
  • Publication date: 9/15/2009
  • Series: Springer Series in Materials Science , #123
  • Edition description: 2009
  • Edition number: 1
  • Pages: 168
  • Product dimensions: 6.30 (w) x 9.40 (h) x 0.70 (d)

Meet the Author

Born on February 26, 1954 in Kiev, Ukraine.

1971 – 1976 Study at the State University of Woronezh, Russia. Graduation: Qualified physicist with award.

1977 – 1992 Scientific research at the Institute of Material Problems of the Science Academy, Kiev, Ukraine. Area of studies: plasma physics, composed materials.

1987 Promotion with the grade: Doctor of Engineering Sciences

1992 – 2006 hema electronic GmbH, Aalen, Department: machine vision, Development engineer, Project director. Main topic: Algorithmics for defect recognition on edges and surfaces

Since April 2006 Thermosensorik GmbH, Erlangen, Manager Software Engineering, IP Expert

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Table of Contents

1 Introduction 1

1.1 What Does an Image Processing Task Look Like? 1

1.2 Conventional Methods of Defect Recognition 3

1.2.1 Structural Analysis 3

1.2.2 Edge-Based Segmentation with Pre-defined Thresholds 5

1.3 Adaptive Edge-Based Object Detection 6

2 Edge Detection 9

2.1 Detection of an Edge 9

2.1.1 Single Edge 10

2.1.2 Double Edge 21

2.1.3 Multiple Edges 24

2.2 Non-Linear Approximation as Edge Compensation 27

3 Defect Detection on an Edge 31

3.1 Defect Recognition on a Regular Contour 32

3.2 Defect Detection on a Dented Wheel Contour 33

3.3 Recognition of a Defect on a Free-Form Contour 34

3.3.1 Fundamentals on Morphological Enveloping Filtering 37

3.3.2 Defect Recognition on a Linear Edge Using an Envelope Filter 43

3.3.3 Defect Recognition on a Free-Form Edge Using an Envelope Filter 44

4 Defect Detection on an Inhomogeneous High-Contrast Surface 47

4.1 Defect Edge 47

4.2 Defect Recognition 50

4.2.1 Detection of Potential Defect Positions 51

4.2.2 100% Defect Positions 56

4.2.3 How Many 100% Defect Positions Must a Real Defect Have? 57

4.2.4 Evaluation of Detected Defects 60

4.3 Setup of Adaptivity Parameters of the SDD Algorithm 60

4.4 Industrial Applications 64

4.4.1 Surface Inspection of a Massive Metallic Part 64

4.4.2 Surface Inspection of a Deep-Drawn Metallic Part 65

4.4.3 Inspection of Non-Metallic Surfaces 65

4.4.4 Position Determination of a Welded Joint 66

4.4.5 Robot-Assisted Surface Inspection 68

5 Defect Detection on an Inhomogeneous Structured Surface 71

5.1 How to Search for a Blob? 71

5.2 Adaptive Blob Detection 73

5.2.1 Adaptivity Level 1 74

5.2.2 Further Adaptivity Levels 79

5.3 Setup ofAdaptivity Parameters of the ABD Algorithm 81

5.4 Industrial Applications 83

5.4.1 Cell Inspection using Microscopy 84

5.4.2 Inspection of a Cold-Rolled Strip Surface 85

5.4.3 Inspection of a Wooden Surface 86

6 Defect Detection in Turbo Mode 93

6.1 What is the Quickest Way to Inspect a Surface? 93

6.2 How to Optimize the Turbo Technique? 95

7 Adaptive Edge and Defect Detection as a basis for Automated Lumber Classification and Optimisation 99

7.1 How to Grade a Wood Cutting? 99

7.1.1 Boundary Conditions100

7.1.2 Most Important Lumber Terms 100

7.2 Traditional Grading Methods 101

7.2.1 Defect-Related Grading 101

7.2.2 Grading by Sound Wood Cuttings 102

7.3 Flexible Lumber Grading 103

7.3.1 Adaptive Edge and Defect Detection 104

7.3.2 Defect-Free Areas: From "Spaghetti" to "Cutting" 104

7.3.3 Simple Lumber Classification Using only Four Parameters 106

7.3.4 The 3-Metres Principle 116

7.3.5 Grading of Lumber with Red Heart 119

7.4 The System for Automatic Classification and Sorting of Hardwood Lumber 123

7.4.1 Structure of the Vision system 123

7.4.2 User Interface 124

8 Object Detection on Images Captured Using a Special Equipment 129

8.1 Evaluation of HDR Images 129

8.2 Evaluation of X-ray Images 131

9 Before an Image Processing System is Used 135

9.1 Calibration 135

9.1.1 Evaluation Parameters 136

9.1.2 Industrial Applications 141

9.2 Geometrical Calibration 142

9.2.1 h-Calibration 144

9.2.2 l-Calibration 149

9.3 Smallest Detectable Objects 158

9.3.1 Technical Pre-Condition for Minimal Object Size 158

9.3.2 Minimum Detectable Objects in Human Perception 159

References 161

Index 165

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