# Surrogate data: A novel approach to object detection

International Journal of Applied Mathematics and Computer Science (2010)

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

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topZbisław 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 -

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