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Subjective analysis of edge detectors in color image processing

  • P. Androutsos
  • D. Androutsos
  • K. N. Plataniotis
  • A. N. Venetsanopoulos
Poster Session A: Color & Texture, Enchancement, Image Analysis & Pattern Recognition, Segmentation
Part of the Lecture Notes in Computer Science book series (LNCS, volume 1310)

Abstract

There exist many experimental situations in which a subjective rather than an objective test of a specific variable proves to be a much more relevant method of investigation. Examples of such cases abound in experiments involving human perception or human interaction. When performing tests of the human visual process, one particular subject may view something differently than another. In such situations, objective tests are very difficult to generate and often completely unfeasable due to the fact that they do not accurately model human perception. Because of the intimate relationship between image processing and the human eye, subjective tests are extremely important when the final judgement if an image is passed by the human eye. In this paper insight into what method of colour edge detection results in edgemaps which are in best accordance with what the human eye sees. In particular, this paper presents a comparison of the relative subjectively based performances of a group of basic order statistic and difference vector operator detectors.

Keywords

Difference Vector Pixel Window Vector Range Neighbour Filter Noise Scenario 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

References

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    P.E. Trahanias and A.N. Venetsanopoulos “Colour edge detection using vector order statistics,” IEEE Transaction on Image Processing, pp1–18, Sept. 1995.Google Scholar
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    I. Pitas and A.N. Venetsanopoulos, “Nonlinear Digital Filters: Principles and Applications” Kluwer Academic Publishers, 1990.Google Scholar
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    S. Sanwalka “Vector order statistic filters for colour image processing,” University of Toronto M.A.Sc. Thesis, Sept. 1992.Google Scholar
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    Y. Yang “Colour edge detection and segmentation using vecotr analysis,” University of Toronto M.A.Sc. Thesis, Sept. 1995.Google Scholar
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    K.N. Plataniotis, D. Androutsos, A.N. Venetsanopoulos “Nearest Neighbour multichannel filters for image processing,” Signal Processing VIII, Theories and Applications vol 3, Sept. 1996.Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 1997

Authors and Affiliations

  • P. Androutsos
    • 1
  • D. Androutsos
    • 1
  • K. N. Plataniotis
    • 1
  • A. N. Venetsanopoulos
    • 1
  1. 1.Department of Electrical and Computer EngineeringUniversity of TorontoTorontoCanada

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