image segmentation is an important task in image processing and computer vision which attract many researchers attention. there are a couple of information sets pixels in an image: statistical and structural information which refer to the feature value of pixel data and local correlation of pixel data, respectively. markov random field (mrf) is a tool for modeling statistical and structural inf...

Image segmentation is an important task in image processing and computer vision which attract many researchers attention. There are a couple of information sets pixels in an image: statistical and structural information which refer to the feature value of pixel data and local correlation of pixel data, respectively. Markov random field (MRF) is a tool for modeling statistical and structural inf...

In this paper we propose an original and statistical method for the sea-oor segmentation and its classi-cation into ve kinds of regions: sand, pebbles, rocks, ridges and dunes. The proposed method is based on the identiication of the cast shadow shapes for each sea-bottom type and consists in four stages of processing. Firstly, the input image is segmented into two kinds of regions: shadow (cor...

The traditional active contour models are sensitive to the speckle noise in synthetic aperture radar (SAR) images. In this paper, Markov random field (MRF) theory is incorporated into fuzzy model detect changes of multitemporal SAR proposed method, neighboring information considered modify pointwise prior probability for exploiting mutual and spatial information. addition, we incorporate MRF ge...

texture image analysis is one of the most important working realms of image processing in medical sciences and industry. up to present, different approaches have been proposed for segmentation of texture images. in this paper, we offered unsupervised texture image segmentation based on markov random field (mrf) model. first, we used gabor filter with different parameters’ (frequency, orientatio...

This paper presents a Bayesian approach for foreground segmentation in monocular image sequences. To overcome the limitations of background modeling in dealing with pixel-wise processing, spatial coherence and temporal persistency are formulated with background model under a maximum a posterior probability (MAP)-MRF framework. Fuzzy clustering factor was introduced into the prior energy of MRFs...

texture image analysis is one of the most important working realms of imageprocessing in medical sciences and industry. up to present, different approacheshave been proposed for segmentation of texture images. in this paper, we offeredunsupervised texture image segmentation based on markov random field (mrf)model. first, we used gabor filter with different parameters’ (frequency,orientation) va...