Fractal dimension is a very useful metric for the analysis of the images with self-similar content, such as textures. For its computation there exist several approaches, the probabilistic algorithm being accepted as the most elegant approach. However, all the existing methods are defined for 1-D signals or binary images, with extension to grayscale images. Our purpose is to propose a color version of the probabilistic algorithm for the computation of the fractal dimension. To validate this new approach, we also propose an extension of the existing algorithm for the generation of probabilistic fractals, in order to obtain color fractal images. Then we show the results of our experiments and conclude this paper.
"However, the proposed method needs more than 3 colour channels to be accurate and the computational resources it requires are very high. An extension to the above-mentioned mass distribution method in colour space has been done recently in an unpublished work of Ivanovici and Richard . For a certain square of size ε in the x − y plane, they count the number of data elements (pixels) that fall inside a 3-D cube of size ε centered in the current pixel. "
[Show abstract][Hide abstract] ABSTRACT: The box counting method for fractal dimension estimation had not been applied
to large or colour images thus far due to the processing time required. In this
letter we present a fast, easy to implement and very easily expandable to any
number of dimensions variation, the box merging method. It is applied here in
RGB images which are considered as sets in 5-D space.
[Show abstract][Hide abstract] ABSTRACT: Vision is a complex process that integrates multiple aspects of an image: spatial frequencies, topology and colour. Unfortunately, so far, all these elements were independently took into consideration for the development of image and video quality metrics, therefore we propose an approach that blends together all of them. Our approach allows for the analysis of the complexity of colour images in the RGB colour space, based on the probabilistic algorithm for calculating the fractal dimension and lacunarity. Given that all the existing fractal approaches are defined only for gray-scale images, we extend them to the colour domain. We show how these two colour fractal features capture the multiple aspects that characterize the degradation of the video signal, based on the hypothesis that the quality degradation perceived by the user is directly proportional to the modification of the fractal complexity. We claim that the two colour fractal measures can objectively assess the quality of the video signal and they can be used as metrics for the user-perceived video quality degradation and we validated them through experimental results obtained for an MPEG-4 video streaming application; finally, the results are compared against the ones given by unanimously-accepted metrics and subjective tests.
EURASIP Journal on Image and Video Processing 10/2010; 2010(7). DOI:10.1155/2010/308035 · 0.74 Impact Factor
[Show abstract][Hide abstract] ABSTRACT: We propose a computer-based e-health system in dermatology, particularly adapted for psoriasis management, in order to assist the dermatologist to objectively evaluate the severity of skin lesions and to choose the adequate treatment. The system comprises a digital color camera, a personal computer and an image processing software application. In order to assess the severity of psoriatic lesions we use the unanimously-accepted PASI score, which is a subjective score computed by the dermatologist based on the following characteristics of the affected skin regions: area, erythema, induration and scaliness. Therefore, the precise and objective measurement of the lesion area is extremely important. However, this step has to be preceded by a segmentation operation, therefore we propose a color image segmentation approach using fractal features for the local characterization of the color texture. We show our results, then we conclude this paper.
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