Use of Local Characteristics of Self-Similarity of Digital Images for Solving the Problems of Crack Detection

A.L. Zhiznyakov; D.G. Privezentsev

doi:10.4028/www.scientific.net/AMM.756.704

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HomeApplied Mechanics and MaterialsApplied Mechanics and Materials Vol. 756Use of Local Characteristics of Self-Similarity of...

Use of Local Characteristics of Self-Similarity of Digital Images for Solving the Problems of Crack Detection

Abstract:

The task of analyzing digital images on the basis of local characteristics of self-similarity is considered in this article. The algorithm of forming fractal characteristics of images and the detection algorithm, which can be used to solve the problems of task detection, are described. The results of studying the possibility of distributing the self-similarity in the problems of crack-detection are given

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Periodical:

Applied Mechanics and Materials (Volume 756)

Pages:

704-708

DOI:

https://doi.org/10.4028/www.scientific.net/AMM.756.704

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Online since:

April 2015

Authors:

A.L. Zhiznyakov*, D.G. Privezentsev

Keywords:

Fractal Characteristics of Images, Fractal Model of Images, Image Identification, Processing Images

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References

[1] Potapov А.А. The newest methods of processing images. Moscow.: Fizmatlitgiz, 2008, 496 pp.

Google Scholar

[2] Wellstead S. Fractals and wavelets for compressing images in use. Manual. Moscow: Triumph Editorial house, 2003, 320 pp.

Google Scholar

[3] A. L. Zhiznyakov and D. G. Privezentsev. Detection of Uncharacteristic Blocks in an Image with Self Similarity, Pattern Recognition and Image Analysis, 23, 3 (2013) 375–380.

DOI: 10.1134/s1054661813030164

Google Scholar

[4] Privezentsev D. G Zhiznyakov A. L. Submission of digital images by means of fractal model, 11th International Conference of Pattern Recognition and Image Analysis: New Information Tehnologies (PRIA-11-2013). Samara, September 23-28, 2013. Conference Proceedings (Vol. I-II), Volume 1, pp.363-365.

Google Scholar

[5] Privezentsev D. G Zhiznyakov A. L. Use of fractal signs of digital images for detection of surface defects, 11th International Conference of Pattern Recognition and Image Analysis: New Information Tehnologies (PRIA-11-2013). Samara, September 23-28, 2013. Conference Proceedings (Vol. I-II), Volume 1, pp.373-374.

DOI: 10.1109/crmico.2014.6959446

Google Scholar

[6] Privezentsev D. G Zhiznyakov A. L. Finding local characteristics of the self-similarity of a digital image, Methods and devices of transferring and processing information. Editorial and printing center of Vladimir state university. 12 (2010).

Google Scholar

[7] Zhiznyakov A.L., Privezentsev D.G. The use of the self-similarity distribution as a digital image feature in the problem of classification, 14th international multidisciplinary scientific GeoConference on Science and technologies in geology exploration and mining. Conference proceeding. Informatics, Geoinformatics. Volume 1. pp.393-398.

DOI: 10.5593/sgem2014/b21/s7.050

Google Scholar

[8] Privezentsev D. G Zhiznyakov A. L., Baranov А.А. Using fractal methods in processing images and signals, Algorithms, methods and systems of processing data: collect. of scient. papers. Editorial and printing center of Murom Institute of Vladimir state university. 14 (2009).

DOI: 10.18287/1613-0073-2018-2210-309-315

Google Scholar