research-article

Fast Algorithms for Low-Level Vision

Author:

R. DericheAuthors Info & Claims

IEEE Transactions on Pattern Analysis and Machine Intelligence, Volume 12, Issue 1

Pages 78 - 87

https://doi.org/10.1109/34.41386

Published: 01 January 1990 Publication History

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Abstract

A recursive filtering structure is proposed that drastically reduces the computational effort required for smoothing, performing the first and second directional derivatives, and carrying out the Laplacian of an image. These operations are done with a fixed number of multiplications and additions per output point independently of the size of the neighborhood considered. The key to the approach is, first, the use of an exponentially based filter family and, second, the use of the recursive filtering. Applications to edge detection problems and multiresolution techniques are considered, and an edge detector allowing the extraction of zero-crossings of an image with only 14 operations per output element at any resolution is proposed. Various experimental results are shown.

References

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Index Terms

Fast Algorithms for Low-Level Vision
1. Computing methodologies
  1. Artificial intelligence
    1. Computer vision
      1. Computer vision problems
        Interest point and salient region detections
  2. Computer graphics
    1. Image manipulation

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cover image IEEE Transactions on Pattern Analysis and Machine Intelligence

IEEE Transactions on Pattern Analysis and Machine Intelligence Volume 12, Issue 1

January 1990

112 pages

ISSN:0162-8828

Editor:
Steven L. Tanimoto
Univ. of Washington, Seattle

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IEEE Computer Society

United States

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Published: 01 January 1990

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Li BDong WGao Y(2021)WiProg: A WebAssembly-based Approach to Integrated IoT ProgrammingIEEE INFOCOM 2021 - IEEE Conference on Computer Communications10.1109/INFOCOM42981.2021.9488424(1-10)Online publication date: 10-May-2021
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References

Cited By

Index Terms

Recommendations

Fast LoG filtering using recursive filters

Recursive Regularization Filters: Design, Properties, and Applications

On the Localization Performance Measure and Optimal Edge Detection

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