Robust Fractal characterization of 1-D and 2-D signals

Niranjan Avadhanam; Sunanda Mitra

doi:10.1117/12.162041

Robust Fractal characterization of 1-D and 2-D signals

Niranjan Avadhanam, Sunanda Mitra

Electrical and Computer Engineering

Research output: Contribution to journal › Conference article › peer-review

1 Scopus citations

Abstract

Fractal characterization of signals is well suited in analysis of some time series data and in classification of natural shapes and textures. A Maximum Likelihood Estimator (MLE) is used to measure the parameter H which is directly related to the fractal dimension. The robustness of the estimator and the performance of the method are demonstrated on datasets generated using a variety of techniques. Finally the characterization is used in segmentation of composite images of natural textures.

Original language	English
Pages (from-to)	232-244
Number of pages	13
Journal	Proceedings of SPIE - The International Society for Optical Engineering
Volume	2032
DOIs	https://doi.org/10.1117/12.162041
State	Published - Oct 29 1993
Event	Neural and Stochastic Methods in Image and Signal Processing II 1993 - San Diego, United States Duration: Jul 11 1993 → Jul 16 1993

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abstract = "Fractal characterization of signals is well suited in analysis of some time series data and in classification of natural shapes and textures. A Maximum Likelihood Estimator (MLE) is used to measure the parameter H which is directly related to the fractal dimension. The robustness of the estimator and the performance of the method are demonstrated on datasets generated using a variety of techniques. Finally the characterization is used in segmentation of composite images of natural textures.",

author = "Niranjan Avadhanam and Sunanda Mitra",

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AU - Mitra, Sunanda

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N2 - Fractal characterization of signals is well suited in analysis of some time series data and in classification of natural shapes and textures. A Maximum Likelihood Estimator (MLE) is used to measure the parameter H which is directly related to the fractal dimension. The robustness of the estimator and the performance of the method are demonstrated on datasets generated using a variety of techniques. Finally the characterization is used in segmentation of composite images of natural textures.

AB - Fractal characterization of signals is well suited in analysis of some time series data and in classification of natural shapes and textures. A Maximum Likelihood Estimator (MLE) is used to measure the parameter H which is directly related to the fractal dimension. The robustness of the estimator and the performance of the method are demonstrated on datasets generated using a variety of techniques. Finally the characterization is used in segmentation of composite images of natural textures.

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U2 - 10.1117/12.162041

DO - 10.1117/12.162041

M3 - Conference article

AN - SCOPUS:85063314936

SN - 0277-786X

VL - 2032

SP - 232

EP - 244

JO - Proceedings of SPIE - The International Society for Optical Engineering

JF - Proceedings of SPIE - The International Society for Optical Engineering

T2 - Neural and Stochastic Methods in Image and Signal Processing II 1993

Y2 - 11 July 1993 through 16 July 1993

ER -

Robust Fractal characterization of 1-D and 2-D signals

Abstract

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