Radon and Projection Transform-Based Computer Vision

Algorithms, A Pipeline Architecture, and Industrial Applications

  • Jorge L. C. Sanz
  • Eric B. Hinkle
  • Anil K. Jain

Part of the Springer Series in Information Sciences book series (SSINF, volume 16)

Table of contents

  1. Front Matter
    Pages I-VIII
  2. Jorge J. C. Sanz, Eric B. Hinkle, Anil K. Jain
    Pages 1-14
  3. Jorge J. C. Sanz, Eric B. Hinkle, Anil K. Jain
    Pages 15-26
  4. Jorge J. C. Sanz, Eric B. Hinkle, Anil K. Jain
    Pages 27-36
  5. Jorge J. C. Sanz, Eric B. Hinkle, Anil K. Jain
    Pages 37-41
  6. Jorge J. C. Sanz, Eric B. Hinkle, Anil K. Jain
    Pages 43-70
  7. Jorge J. C. Sanz, Eric B. Hinkle, Anil K. Jain
    Pages 71-85
  8. Jorge J. C. Sanz, Eric B. Hinkle, Anil K. Jain
    Pages 95-104
  9. Jorge J. C. Sanz, Eric B. Hinkle, Anil K. Jain
    Pages 105-105
  10. Back Matter
    Pages 107-123

About this book

Introduction

This book deals with novel machine vision architecture ideas that make real-time projection-based algorithms a reality. The design is founded on raster-mode processing, which is exploited in a powerful and flexible pipeline. We concern ourselves with several image analysis algorithms for computing: projections of gray-level images along linear patterns (i. e. , the Radon transform) and other curved contours; convex hull approximations; the Hough transform for line and curve detection; diameters; moments and principal components, etc. Addition­ ally, we deal with an extensive list of key image processing tasks, which involve generating: discrete approximations of the inverse Radon transform operator; computer tomography reconstructions; two-dimensional convolutions; rotations and translations; multi-color digital masks; the discrete Fourier transform in polar coordinates; autocorrelations, etc. Both the image analysis and image processing algorithms are supported by a similar architecture. We will also of some of the above algorithms to the solution of demonstrate the applicability various industrial visual inspection problems. The algorithms and architectural ideas surveyed here unleash the power of the Radon and other non-linear transformations for machine vision applications. We provide fast methods to transform images into projection space representa­ tions and to backtrace projection-space information into the image domain. The novelty of this approach is that the above algorithms are suitable for implementa­ tion in a pipeline architecture. Specifically, random access memory and other dedicated hardware components which are necessary for implementation of clas­ sical techniques are not needed for our algorithms.

Keywords

Hough transform Radon transform algorithms computer vision image analysis image processing machine vision

Authors and affiliations

  • Jorge L. C. Sanz
    • 1
  • Eric B. Hinkle
    • 1
  • Anil K. Jain
    • 2
  1. 1.Computer Science DepartmentIBM Almaden Research CenterSan JoseUSA
  2. 2.Electrical and Computer Engineering DepartmentUniversity of California at DavisDavisUSA

Bibliographic information

  • DOI https://doi.org/10.1007/978-3-642-73012-2
  • Copyright Information Springer-Verlag Berlin Heidelberg 1988
  • Publisher Name Springer, Berlin, Heidelberg
  • eBook Packages Springer Book Archive
  • Print ISBN 978-3-642-73014-6
  • Online ISBN 978-3-642-73012-2
  • Series Print ISSN 0720-678X
  • About this book