Near optimum estimation of local fractal dimension for image segmentation

Novianto, Sonny; Suzuki, Yusuke; Maeda, Junji

doi:10.1016/s0167-8655(02)00261-1

Cited by 65 publications

(29 citation statements)

References 15 publications

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“…Several practical box-counting dimension methods were put forward for FD estimation [33][34][35][36][37]. Buczkowski et al [38] developed a modified box-counting dimension method eliminating the border effect and non-integer values of the length of box.…”

Section: Methodsmentioning

confidence: 99%

Fractal study on collective evolution of short fatigue cracks under complex stress conditions

Wang

Zheng

Xie

et al. 2012

International Journal of Fatigue

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Section: Methodsmentioning

confidence: 99%

Fractal study on collective evolution of short fatigue cracks under complex stress conditions

Wang

Zheng

Xie

et al. 2012

International Journal of Fatigue

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“…The proposed methods are based on the structure assessment using the difference between the covering areas. As noticed in FD calculation and texture/pattern estimation [25], the first few iterations are mainly sensitive to the structure, that is, to the most noticeable notches. Thus, the adaptive technique in the first iterations seems to be useful in the identification.…”

Section: Multiscale Heart Sound Identificationmentioning

confidence: 92%

Identification of S1 and S2 Heart Sound Patterns Based on Fractal Theory and Shape Context

et al. 2017

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There has been a sustained effort in the research community over the recent years to develop algorithms that automatically analyze heart sounds. One of the major challenges is identifying primary heart sounds, S1 and S2, as they represent reference events for the analysis. The study presented in this paper analyzes the possibility of improving the structure characterization based on shape context and structure assessment using a small number of descriptors. Particularly, for the primary sound characterization, an adaptive waveform filtering is applied based on blanket fractal dimension for each preprocessed sound candidate belonging to pediatric subjects. This is followed by applying the shape based methods selected for the structure assessment of primary heart sounds. Different methods, such as the fractal ones, are used for the comparison. The analysis of heart sound patterns is performed using support vector machine classifier showing promising results (above 95% accuracy). The obtained results suggest that it is possible to improve the identification process using the shape related methods which are rarely applied. This can be helpful for applications involving automatic heart sound analysis.

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“…Kuklinski [2] applied fractal analysis techniques to medical images, for automatic identification of cancerous growth regions; Zwiggelaar & Bull [12] applied them to the problem of plant recognition in synthetic aperture radar (SAR) images. These and other applications can be seen in [7,9,10,11]. These are just a few applications that clearly demonstrate serious interest in fractal based methods for low-level recognition problems.…”

Section: Introductionmentioning

confidence: 95%

Co-occurrence Matrix and fractal dimension for image segmentation

Marón

2012

RMTA

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One of the most important tasks in image processing problem and machine vision is object recognition, and the success of many proposed methods relies on a suitable choice of algorithm for the segmentation of an image. This paper focuses on how to apply texture operators based on the concept of fractal dimension and cooccurence matrix, to the problem of object recognition and a new method based on fractal dimension is introduced. Several images, in which the result of the segmentation can be shown, are used to illustrate the use of each method and a comparative study of each operator is made.

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Near optimum estimation of local fractal dimension for image segmentation

Cited by 65 publications

References 15 publications

Fractal study on collective evolution of short fatigue cracks under complex stress conditions

Fractal study on collective evolution of short fatigue cracks under complex stress conditions

Identification of S1 and S2 Heart Sound Patterns Based on Fractal Theory and Shape Context

Co-occurrence Matrix and fractal dimension for image segmentation

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