Surrogate data: A novel approach to object detection

Zbisław Tabor

International Journal of Applied Mathematics and Computer Science (2010)

  • Volume: 20, Issue: 3, page 545-553
  • ISSN: 1641-876X

Abstract

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In the present study a novel method is introduced to detect meaningful regions of a gray-level noisy images of binary structures. The method consists in generating surrogate data for an analyzed image. A surrogate image has the same (or almost the same) power spectrum and histogram of gray-level values as the original one but is random otherwise. Then minmax paths are generated in the original image, each characterized by its length, minmax intensity and the intensity of the starting point. If the probability of the existence of a path with the same characteristics but within surrogate images is lower than some user-specified threshold, it is concluded that the path in the original image passes through a meaningful object. The performance of the method is tested on images corrupted by noise with varying intensity.

How to cite

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Zbisław Tabor. "Surrogate data: A novel approach to object detection." International Journal of Applied Mathematics and Computer Science 20.3 (2010): 545-553. <http://eudml.org/doc/208006>.

@article{ZbisławTabor2010,
abstract = {In the present study a novel method is introduced to detect meaningful regions of a gray-level noisy images of binary structures. The method consists in generating surrogate data for an analyzed image. A surrogate image has the same (or almost the same) power spectrum and histogram of gray-level values as the original one but is random otherwise. Then minmax paths are generated in the original image, each characterized by its length, minmax intensity and the intensity of the starting point. If the probability of the existence of a path with the same characteristics but within surrogate images is lower than some user-specified threshold, it is concluded that the path in the original image passes through a meaningful object. The performance of the method is tested on images corrupted by noise with varying intensity.},
author = {Zbisław Tabor},
journal = {International Journal of Applied Mathematics and Computer Science},
keywords = {surrogate data; optimal paths; fuzzy connectedness},
language = {eng},
number = {3},
pages = {545-553},
title = {Surrogate data: A novel approach to object detection},
url = {http://eudml.org/doc/208006},
volume = {20},
year = {2010},
}

TY - JOUR
AU - Zbisław Tabor
TI - Surrogate data: A novel approach to object detection
JO - International Journal of Applied Mathematics and Computer Science
PY - 2010
VL - 20
IS - 3
SP - 545
EP - 553
AB - In the present study a novel method is introduced to detect meaningful regions of a gray-level noisy images of binary structures. The method consists in generating surrogate data for an analyzed image. A surrogate image has the same (or almost the same) power spectrum and histogram of gray-level values as the original one but is random otherwise. Then minmax paths are generated in the original image, each characterized by its length, minmax intensity and the intensity of the starting point. If the probability of the existence of a path with the same characteristics but within surrogate images is lower than some user-specified threshold, it is concluded that the path in the original image passes through a meaningful object. The performance of the method is tested on images corrupted by noise with varying intensity.
LA - eng
KW - surrogate data; optimal paths; fuzzy connectedness
UR - http://eudml.org/doc/208006
ER -

References

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  9. Stauffer, D. and Aharony, A. (1994). Introduction to Percolation Theory, 2nd Edn., Taylor & Francis, Philadelphia, PA. Zbl0862.60092
  10. Theiler, J., Eubank, S., Longtin, A., Galdrikian, B. and Farmer, J.D. (1992). Testing for nonlinearity in time series: The method of surrogate data, Physica D 58(1-4): 77-94. Zbl1194.37144
  11. Udupa, J.K. and Saha, P.K. (2003). Fuzzy connectedness and image segmentation, Proceedings of the IEEE 91(10): 1649-1669. 
  12. Watanabe, S. (1985). Pattern Recognition: Human and Mechanical, Wiley, New York, NY. 

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