FRACTAL-BASED TEXTURE ANALYSIS

Research output: Chapters, Conference Papers, Creative and Literary Works (RGC: 12, 32, 41, 45)32_Refereed conference paper (with ISBN/ISSN)peer-review

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Detail(s)

Original languageEnglish
Title of host publicationProceedings - Singapore ICCS/ISITA 1992: ''Communications on the Move''
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages102-106
Volume1
ISBN (Print)0780308034, 9780780308039
Publication statusPublished - Nov 1992

Conference

Title1992 Singapore: Communications on the Move, ICCS/ISITA 1992
PlaceSingapore
CitySingapore
Period16 - 20 November 1992

Abstract

Texture analysis hai been widely applied in image segmentation, image classification, pattern recognition, etc. One approach is to extract the textural information from the region of interest by calculating first-order or second-order statistics. Many studies have found that spatial grey level co-occurrence matrices statistics are good textural parameters. However, they are not particular effective in the characterization of textures which are of low contrast and random in nature.
Recently, another approach to texture analysis begins to emerge, which is based on fractal geometry. The mathematical model of fractal geometry has been used to analyze many natural and physical phenomena. In this investigation, it was applied in the modelling of natural texture exhibited in digital image. One important parameter that can be derived and then used for texture characterization is the fractal dimension, which can be determined using two separate methods. In the first method, the fractal dimension is estimated based on the average intensity difference of the pixels within the region of interest. In the second method, the fractal dimension is determined from the Fourier power spectrum of the image data. In order to assess the effectiveness of the fractal dimension, the regions of interest were also analyzed using spatial grey level matrices statistics.
Fractal dimension seems to be good in describing many natural textures as well as many human tissues as displayed in medical images such as the ultrasonic image. The results also show that the fractal dimension is a better parameter than many secondorder statistics in texture characterization. The possible reasons are that many natural textures are very similar to fractal surfaces, and also the way the fractal model describing surface roughness correlates closely to human perception of texture. © 1992 IEEE.

Citation Format(s)

FRACTAL-BASED TEXTURE ANALYSIS. / Chan, K. L.

Proceedings - Singapore ICCS/ISITA 1992: ''Communications on the Move''. Vol. 1 Institute of Electrical and Electronics Engineers Inc., 1992. p. 102-106 254931.

Research output: Chapters, Conference Papers, Creative and Literary Works (RGC: 12, 32, 41, 45)32_Refereed conference paper (with ISBN/ISSN)peer-review