Intelligent Visual Inspection

Using artificial neural networks

  • Ryan G. Rosandich

Part of the Intelligent Engineering Systems Series book series (IESS)

Table of contents

  1. Front Matter
    Pages i-xii
  2. Introduction

    1. Front Matter
      Pages 1-1
    2. Ryan G. Rosandich
      Pages 3-12
    3. Ryan G. Rosandich
      Pages 13-28
  3. Fundamentals of Artificial Vision Systems

    1. Front Matter
      Pages 29-29
    2. Ryan G. Rosandich
      Pages 31-53
  4. Artificial Vision Systems Design

    1. Front Matter
      Pages 95-95
    2. Ryan G. Rosandich
      Pages 97-122
    3. Ryan G. Rosandich
      Pages 123-136
    4. Ryan G. Rosandich
      Pages 137-155
    5. Ryan G. Rosandich
      Pages 156-179
    6. Ryan G. Rosandich
      Pages 180-216
    7. Ryan G. Rosandich
      Pages 217-259
  5. Case Studies

    1. Front Matter
      Pages 261-261
    2. Ryan G. Rosandich
      Pages 263-273
    3. Ryan G. Rosandich
      Pages 274-282
  6. Back Matter
    Pages 283-306

About this book


A great deal of research is being done in the areas of artificial vision and neural networks. Although much of this research has been theoretical in nature, many of the techniques developed through these efforts are now mature enough for use in practical applications. Automated Visual Inspection Using Artificial Neural Networks explains the application of recently emerging technology in the areas of artificial vision and neural networks to automated visual inspection. The information is organised in a clear, informative manner, bridging the gap between theoretical research and practical application. Significantly this book includes: * broad coverage of all aspects of the automated visual inspection problem, * details of the HAVENET neural network and the CAMERA vision model, and * detailed descriptions of practical applications of intelligent visual inspection.


artificial neural network artificial neural networks backpropagation cognition color filtering image processing intelligence manufacturing neural networks object recognition pattern recognition physiology self-organizing map

Authors and affiliations

  • Ryan G. Rosandich
    • 1
  1. 1.Department of Industrial EngineeringUniversity of Minnesota-DuluthDuluthUSA

Bibliographic information