Machine Vision for the Inspection of Natural Products, Softcover reprint of the original 1st ed. 2003

Machine vision technology has revolutionised the process of automated inspection in manufacturing. The specialist techniques required for inspection of natural products, such as food, leather, textiles and stone is still a challenging area of research. Topological variations make image processing algorithm development, system integration and mechanical handling issues much more complex. The practical issues of making machine vision systems operate robustly in often hostile environments together with the latest technological advancements are reviewed in this volume. Features:
- Case studies based on real-world problems to demonstrate the practical application of machine vision systems.
- In-depth description of system components including image processing, illumination, real-time hardware, mechanical handling, sensing and on-line testing.
- Systems-level integration of constituent technologies for bespoke applications across a variety of industries.
- A diverse range of example applications that a system may be required to handle from live fish to ceramic tiles.
Machine Vision for the Inspection of Natural Products will be a valuable resource for researchers developing innovative machine vision systems in collaboration with food technology, textile and agriculture sectors. It will also appeal to practising engineers and managers in industries where the application of machine vision can enhance product safety and process efficiency.

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