Synopsis
List of Contributors 1. Like Two Peas in a Pod B.G. Batchelor Editorial Introduction 1.1 Advantages of Being Able to See 1.2 Machine Vision 1.2.1 Model for Machine Vision Systems 1.2.2 Applications Classified by Task 1.2.3 Other Applications of Machine Vision 1.2.4 Machine Vision Is Not Natural 1.3 Product Variability 1.3.1 Linear Dimensions 1.3.2 Shape 1.3.3 Why Physical Tolerances Matter 1.3.4 Flexible and Articulated Objects 1.3.5 Soft and Semi-fluid Objects 1.3.6 Colour Variations 1.3.7 Transient Phenomena 1.3.8 Very Complex Objects 1.3.9 Uncooperative Objects 1.3.10 Texture 1.4 Systems Issues 1.5 References 2. Basic Machine Vision Techniques B.G. Batchelor and P.F. Whelan Editorial Introduction 2.1 Representation of Images 2.2 Elementary Image Processing Functions 2.2.1 Monadic Point-by-point Operators 2.2.2 Dyadic Point-by-point Operators 2.2.3 Local Operators 2.2.4 Linear Local Operators 2.2.5 Non-linear Local Operators 2.2.6 N-tuple Operators 2.2.7 Edge Effects 2.2.8 Intensity Histogram [hpi, hgi, he, hgc} 2.3 Binary Images 2.3.1 Measurements on Binary Images 2.3.2 Shape Descriptors 2.4 Binary Mathematical Morphology 2.4.1 Opening and Closing Operations 2.4.2 Structuring Element Decomposition 2.5 Grey-scale Morphology 2.6 Global Image Transforms 2.6.1 Hough Transform 2.6.2 Two-dimensional Discrete Fourier Transform 2.7 Texture Analysis 2.7.1 Statistical Approaches 2.7.2 Co-occurrence Matrix Approach 2.7.3 Structural Approaches 2.7.4 Morphological Texture Analysis 2.8 Implementation Considerations 2.8.1 Morphological System Implementation 2.9 Commercial Devices 2.9.1 Plug-in Boards: Frame-grabbers 2.9.2 Plug-in Boards: Dedicated Function 2.9.3 Self-contained Systems 2.9.4 Turn-key Systems 2.9.5 Software 2.10 Further Remarks 2.11References 3. Intelligent Image Processing B.G. Batchelor Editorial Introduction 3.1 Why We Need Intelligence 3.2 Pattern Recognition 3.2.1 Similarity and Distance 3.2.2 Compactness Hypothesis 3.2.3 Pattern Recognition Models 3.3 Rule-based Systems 3.3.1 How Rules are Used 3.3.2 Combining Rules and Image Processing 3.4 Colour Recognition 3.4.1 RGB Representation 3.4.2 Pattern Recognition 3.4.3 Programmable Colour Filter 3.4.4 Colour Triangle 3.5 Methods and Applications 3.5.1 Human Artifacts 3.5.2 Plants 3.5.3 Semi-processed Natural Products 3.5.4 Food Products 3.6 Concluding Remarks 3.7 References 4. Using Natural Phenomena to Aid Food Produce Inspection G. Long Editorial Introduction 4.1 Introduction 4.2 Techniques to Exploit Natural Phenomena 4.3 Potato Sizing and Inspection 4.4 Stone Detection in Soft Fruit Using Auto-fluorescence 4.5 Brazil Nut Inspection 4.6 Intact Egg Inspection 4.7 Wafer Sizing 4.8 Enrobed Chocolates 4.9 Conclusion 4.10 References 5. Colour Sorting in the Food Industry S.C. Bee and M.J. Honeywood Editorial Introduction 5.1 Introduction 5.2 The Optical Sorting Machine 5.2.1 The Feed System 5.2.2 The Optical System 5.2.3 The Ejection System 5.2.4 The Image Processing Algorithms 5.3 Assessment of Objects for Colour Sorting 5.3.1 Spectrophotometry 5.3.2 Monochromatic Sorting 5.3.3 Bichromatic Sorting 5.3.4 Dual Monochromatic Sorting 5.3.5 Trichromatic Sorting 5.3.6 Fluorescence Techniques 5.3.7 Infrared Techniques 5.3.8 Optical Sorting with Lasers 5.4 The Optical Inspection System 5.4.1 Illumination 5.4.2 Background and Aperture 5.4.3 Optical Filters 5.4.4 Detectors 5.5 The Sorting System 5.5.1 Feed 5.5.2 Ejection 5.5.3 Cleaning and Dust Extraction 5.5.4 The Electronic Processing System 5.6 The Lim
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