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Machine Vision for the Inspection of Natural Products

  • Book
  • © 2003

Overview

Editors:
  1. Mark Graves

    1. Spectral Fusion Technologies, Coleshill, Birmngham

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    You can also search for this editor in PubMed   Google Scholar

  2. Bruce Batchelor

    1. Department of Computer Science, University of Cardiff, Cardiff

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    You can also search for this editor in PubMed   Google Scholar

  • Inspection of any production process can be tedious and repetitive and humans make mistakes.
  • This book shows how even natural products with a high degree of variation can be subjected to the untiring and less error-prone inspection of a machine.
  • Gives practical advice on the implementation of systems for the inspection of a wide variety of naturally occurring materials from stones to live animals.
  • Includes supplementary material: sn.pub/extras

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Table of contents (17 chapters)

  1. Front Matter

    Pages i-xxiv
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  2. Like Two Peas in a Pod

    • B. G. Batchelor
    Pages 1-33

  3. Basic Machine Vision Techniques

    • B. G. Batchelor, P. F. Whelan
    Pages 35-86

  4. Intelligent Image Processing

    • B. G. Batchelor
    Pages 87-140

  5. Using Natural Phenomena to Aid Food Produce Inspection

    • G. Long
    Pages 141-161

  6. Colour Sorting in the Food Industry

    • S. C. Bee, M. J. Honeywood
    Pages 163-189

  7. Surface Defect Detection on Ceramics

    • A. K. Forrest
    Pages 191-213

  8. On-line Automated Visual Grading of Fruit: Practical Challenges

    • P. Ngan, D. Penman, C. Bowman
    Pages 215-239

  9. Vision-based Quality Control in Poultry Processing

    • W. Daley, D. Britton
    Pages 241-258

  10. Quality Classification of Wooden Surfaces Using Gabor Filters and Genetic Feature Optimisation

    • W. Pölzleitner
    Pages 259-277

  11. An Intelligent Approach to Fabric Defect Detection in Textile Processes

    • M. Mufti, G. Vachtsevanos, L. Dorrity
    Pages 279-304

  12. Automated Cutting of Natural Products: A Practical Packing Strategy

    • P. F. Whelan
    Pages 305-329

  13. Model-based Stereo Imaging for Estimating the Biomass of Live Fish

    • R. D. Tillett, J. A. Lines, D. Chan, N. J. B. McFarlane, L. G. Ross
    Pages 331-345

  14. A System for Estimating the Size and Shape of Live Pigs

    • J. A. Marchant, C. P. Schofield
    Pages 347-366

  15. Sheep Pelt Inspection

    • P. Hilton, W. Power, M. Hayes, C. Bowman
    Pages 367-392

  16. Design of Object Location Algorithms and Their Use for Food and Cereals Inspection

    • E. R. Davies
    Pages 393-420

  17. X-ray Bone Detection in Further Processed Poultry Production

    • M. Graves
    Pages 421-449

  18. Final Remarks

    • B. G. Batchelor, M. Graves
    Pages 451-457

  19. Back Matter

    Pages 459-471
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Keywords

About this book

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.

Editors and Affiliations

  • Spectral Fusion Technologies, Coleshill, Birmngham

    Mark Graves

  • Department of Computer Science, University of Cardiff, Cardiff

    Bruce Batchelor

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