Geodesic
Surrogate data: A novel approach to object detection
International Journal of Applied Mathematics and Computer Science, Tome 20 (2010) no. 3, pp. 545-553 Cet article a éte moissonné depuis la source Library of Science

Voir la notice de l'article

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.
Keywords: surrogate data, optimal paths, fuzzy connectedness
Mots-clés : dane zastępcze, ścieżka optymalna, powiązania rozmyte 
@article{IJAMCS_2010_20_3_a9,
     author = {Tabor, Z.},
     title = {Surrogate data: {A} novel approach to object detection},
     journal = {International Journal of Applied Mathematics and Computer Science},
     pages = {545--553},
     year = {2010},
     volume = {20},
     number = {3},
     language = {en},
     url = {http://geodesic.mathdoc.fr/item/IJAMCS_2010_20_3_a9/}
}
TY  - JOUR
AU  - Tabor, Z.
TI  - Surrogate data: A novel approach to object detection
JO  - International Journal of Applied Mathematics and Computer Science
PY  - 2010
SP  - 545
EP  - 553
VL  - 20
IS  - 3
UR  - http://geodesic.mathdoc.fr/item/IJAMCS_2010_20_3_a9/
LA  - en
ID  - IJAMCS_2010_20_3_a9
ER  - 
%0 Journal Article
%A Tabor, Z.
%T Surrogate data: A novel approach to object detection
%J International Journal of Applied Mathematics and Computer Science
%D 2010
%P 545-553
%V 20
%N 3
%U http://geodesic.mathdoc.fr/item/IJAMCS_2010_20_3_a9/
%G en
%F IJAMCS_2010_20_3_a9
Tabor, Z. Surrogate data: A novel approach to object detection. International Journal of Applied Mathematics and Computer Science, Tome 20 (2010) no. 3, pp. 545-553. http://geodesic.mathdoc.fr/item/IJAMCS_2010_20_3_a9/

[1] Brunelli, R. (2009). Template Matching Techniques in Computer Vision: Theory and Practice, Wiley, New York, NY.

[2] Buades, A., Coll, B. and Morel, J.M. (2005). A review of image denoising algorithms, with a new one,Multiscale Modeling and Simulation 4 (2): 490-530.

[3] Cormen, T.H., Leiserson, C.E. and Rivest, R.L. (1990). Introduction to Algorithms, MIT Press, Cambridge, MA.

[4] Fukunaga, K. (1990). Introduction to Statistical Pattern Recognition, 2nd Edn., Academic Press, New York, NY.

[5] Fu, K.S. (1982). Syntactic Pattern Recognition and Applications, Prentice-Hall, Englewood Cliffs, NJ.

[6] Ripley, B.D. (2008). Pattern Recognition and Neural Networks, Cambridge University Press, Cambridge.

[7] Rosenfeld, A. (1983). On connectivity properties of grayscale pictures, Pattern Recognition 16(1): 47-50.

[8] Schreiber, T. and Schmitz, A. (2000). Surrogate time series, Physica D 142 (3-4): 346-382.

[9] Stauffer, D. and Aharony, A. (1994). Introduction to Percolation Theory, 2nd Edn., Taylor Francis, Philadelphia, PA.

[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.

[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.