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Optics Express

Optics Express

  • Editor: C. Martijn de Sterke
  • Vol. 15, Iss. 1 — Jan. 8, 2007
  • pp: 62–75

Discrimination among similar looking noisy color patches using Margin Setting

Kaveh Heidary and H. John Caulfield  »View Author Affiliations


Optics Express, Vol. 15, Issue 1, pp. 62-75 (2007)
http://dx.doi.org/10.1364/OE.15.000062


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Abstract

The goal of discrimination of one color from many other similar-appearing colors even when the colored objects show substantial variation or noise is of obvious import. We show how to accomplish that using a technique called Margin Setting. It is possible not only to have very low error rates but also to have some control over the types of errors that do occur. Robust spectral filtering prior to spatial pattern recognition allows subsequent filtering processes to be based on conventional coherent optical correlation that can be done monochromatically.

© 2007 Optical Society of America

OCIS Codes
(100.2000) Image processing : Digital image processing
(100.2980) Image processing : Image enhancement
(330.1690) Vision, color, and visual optics : Color

ToC Category:
Image Processing

History
Original Manuscript: August 18, 2006
Revised Manuscript: December 9, 2006
Manuscript Accepted: December 18, 2006
Published: January 8, 2007

Virtual Issues
Vol. 2, Iss. 2 Virtual Journal for Biomedical Optics

Citation
Kaveh Heidary and H. John Caulfield, "Discrimination among similar looking noisy color patches using Margin Setting," Opt. Express 15, 62-75 (2007)
http://www.opticsinfobase.org/oe/abstract.cfm?URI=oe-15-1-62


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References

  1. H. J. Caulfield, "Artificial Color," Neurocomputing,  51, 463-465 (2003).
  2. H. J. Caulfield, Kaveh Heidary, "Exploring Margin Setting for good generalization in multiple class discrimination," Pattern Recognition 38, 1225-1238 (2005).
  3. H. J. Caulfield, A. Karavolos, J. E. Ludman, "Improving optical Fourier pattern recognition by accommodating the missing information," Information Sciences 162, 35-52 (2004).
  4. C. J. Burges, A tutorial on Support Vector Machines for Pattern Recognition, (Kluwer Academic Publishers, New York, 1998).
  5. L. G. Valiant, "A theory of learnability," Communications ACM 27, 1134-1142 (1984). [CrossRef]
  6. R. E. Schapire, "The strength of weak learnability," Machine Learning 5(2), 197-227 (1990). [CrossRef]
  7. http://www.vision.caltech.edu/Image_Datasets/Caltech101/Caltech101.html.
  8. R.C. Gonzalez, R.E. Woods, Digital Image Processing, (Prentice Hall, New York, 2002).

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