Texture segmentation using fractal dimension

Unsupervised texture segmentation using feature distributions

“…Early methods proposed for unsupervised region-based texture segmentation include approaches based on split-and-merge methods [5], pyramid node linking [6], selective feature smoothing with clustering [7], and a quadtree method combining statistical and spatial information [8]. Examples of more recent approaches are methods based on local linear transforms and multiresolution feature extraction [9], feature smoothing and probabilistic relaxation [10], autoregressive models [11,12], Markov random field models [13][14][15][16], multichannel filtering [17][18][19], neural network-based generalization of the multichannel approach [20], wavelets [21,22], fractal dimension [23], and hidden Markov models [24]. A method for unsupervised segmentation of color textures using Markov random fields and a split-and-merge type algorithm was proposed by Panjwani and Healey [25].…”

SAR Image Segmentation using Statistical Techniques

“…The probability density function of Weibull distribution is given by p(|C m,n |) = [.]. In this paper, the scale parameter, is considered for image segmentation [1,2]. Another parameter considered is the Fractal parameter, namely the Fractal dimension.…”

“…Fractal dimension is computed for an image pixel by considering a window that surrounds the pixel. Variation method defines Fractal dimension as the slope of the line that best fits the points (log(R/ ), log {(R/ ) 3 E }) where R is the window size and ranges from 1 to max, E is taken as the average of V (x,y), where V (x,y) is the th variation (difference between maximum and minimum pixel value) over a window of size 2 +1 [2,3,4]. The range of Weibull and Fractal parameters varies for optical and SAR images.…”

Object-based image classification for burned area mapping of Creus Cape, Spain, using NOAA-AVHRR imagery

“…Each image object has a large number of characteristic properties; the so-called object features or attributes. In this sense, the best segmentation result is that which provides optimal information for further processing (Baatz & Schäpe, 2000;Chaudhuri & Sarkar, 1995;Hofmann et al, 1998;Laine & Fan, 1996;Mao & Jain, 1992).…”