Machine Vision for the Inspection of Natural Products

List of Contributors 1. Like Two Peas in a PodB.G. Batchelor Editorial Introduction 1.1 Advantages of Being Able to See1.2 Machine Vision 1.2.1 Model for Machine Vision Systems1.2.2 Applications Classified by Task1.2.3 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 Shape1.3.3 Why Physical Tolerances Matter 1.3.4 Flexible and Articulated Objects1.3.5 Soft and Semi-fluid Objects 1.3.6 Colour Variations1.3.7 Transient Phenomena1.3.8 Very Complex Objects1.3.9 Uncooperative Objects1.3.10 Texture 1.4 Systems Issues 1.5 References 2. Basic Machine Vision TechniquesB.G. Batchelor and P.F. Whelan Editorial Introduction 2.1 Representation of Images 2.2 Elementary Image Processing Functions2.2.1 Monadic Point-by-point Operators 2.2.2 Dyadic Point-by-point Operators 2.2.3 Local Operators2.2.4 Linear Local Operators 2.2.5 Non-linear Local Operators2.2.6 N-tuple Operators2.2.7 Edge Effects 2.2.8 Intensity Histogram [hpi, hgi, he, hgc} 2.3 Binary Images2.3.1 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 Morphology2.6 Global Image Transforms2.6.1 Hough Transform2.6.2 Two-dimensional Discrete Fourier Transform2.7 Texture Analysis2.7.1 Statistical Approaches2.7.2 Co-occurrence Matrix Approach2.7.3 Structural Approaches2.7.4 Morphological Texture Analysis2.8 Implementation Considerations2.8.1 Morphological System Implementation 2.9 Commercial Devices2.9.1 Plug-in Boards: Frame-grabbers 2.9.2 Plug-in Boards: Dedicated Function 2.9.3 Self-contained Systems 2.9.4 Turn-key Systems2.9.5 Software 2.10 Further Remarks 2.11References 3. Intelligent Image ProcessingB.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 Models3.3 Rule-based Systems3.3.1 How Rules are Used3.3.2 Combining Rules and Image Processing3.4 Colour Recognition 3.4.1 RGB Representation3.4.2 Pattern Recognition3.4.3 Programmable Colour Filter3.4.4 Colour Triangle 3.5 Methods and Applications3.5.1 Human Artifacts 3.5.2 Plants3.5.3 Semi-processed Natural Products 3.5.4 Food Products 3.6 Concluding Remarks 3.7 References 4. Using Natural Phenomena to Aid Food Produce InspectionG. LongEditorial 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 Inspection4.6 Intact Egg Inspection4.7 Wafer Sizing4.8 Enrobed Chocolates4.9 Conclusion4.10 References 5. Colour Sorting in the Food IndustryS.C. Bee and M.J. Honeywood Editorial Introduction 5.1 Introduction 5.2 The Optical Sorting Machine 5.2.1 The Feed System5.2.2 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 Sorting5.3.5 Trichromatic Sorting 5.3.6 Fluorescence Techniques 5.3.7 Infrared Techniques 5.3.8 Optical Sorting with Lasers5.4 The Optical Inspection System5.4.1 Illumination 5.4.2 Background and Aperture 5.4.3 Optical Filters5.4.4 Detectors 5.5 The Sorting System 5.5.1 Feed5.5.2 Ejection 5.5.3 Cleaning and Dust Extraction 5.5.4 The Electronic Processing System 5.6 The Lim