Edge examination using H&amp;#x00F6;lder exponent and image statistics

Paskas, Milorad P.; Gavrovska, Ana; Jevtic, Dubravka R.; Slavković, Marijeta; Reljin, Branimir

doi:10.1109/telsks.2011.6112063

Cited by 4 publications

(2 citation statements)

References 6 publications

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“…Filtering techniques used in this paper represent adapted fractal techniques proposed in [3] and [4], where they were used for filtering of natural images. Edges are detected within these techniques using local fractal dimensions instead of gradients.…”

Section: Introductionmentioning

confidence: 99%

Adaptive fractal filtering of echocardiograms

Paskas

Gavrovska

Dujkovic

et al. 2014

12th Symposium on Neural Network Applications in Electrical Engineering (NEUREL)

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Echocardiograms are inherently corrupted by the speckle noise. Elimination of the noise is usually treated with lowpass filters which can degrade edges in the image. Adaptive approaches employ masks for edges and restrict low-pass filtering mainly to homogeneous regions. Masks are based on statistical parameters or gradients. In this paper are applied local dimension matrices from fractal model as masks. Experimental tests are conducted for two simple low-pass filters (i) average filter and Gaussian filter (ii) and using three multifractal measures known from the literature -MIN, MAX and OSC measure. Obtained results for adaptive approaches show improvements over non-adaptive approaches in all analyzed scenarios.Index Terms-Echocardiogram, fractal dimension, image denoising, speckle noise, ultrasound.

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Section: Introductionmentioning

confidence: 99%

Adaptive fractal filtering of echocardiograms

Paskas

Gavrovska

Dujkovic

et al. 2014

12th Symposium on Neural Network Applications in Electrical Engineering (NEUREL)

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“…It has many applications in edge detection [5,6], texture classification [7], image analysis [8], image segmentation [9], and image enhancement [10]. Its advantage over other existing models is seen in data complexity treatment with respect to dimensionality of data structure.…”

Section: Introductionmentioning

confidence: 99%

Image analysis using modified multifractal measure based on sigmoid function

Paskas

Gavrovska

Milivojević

et al. 2012

11th Symposium on Neural Network Applications in Electrical Engineering

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In this paper we propose the new multifractal measure inspired by sigmoid activation function usually used in neural networks. By using new measure the Hölder exponent and multifractal spectrum are determined in classical way. New measure is applied to image processing, especially in texture classification. It was shown that by changing the slope of the sigmoid function different details can be extracted from analyzed image. Initial results are promising and indicate to high potential of new measure in image processing.Index Terms-Multifractal measure, sigmoid function, multifractal spectrum, Hölder exponent, image analysis.

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