Titlebook: Image Analysis and Recognition; 7th International Co Aurélio Campilho,Mohamed Kamel Conference proceedings 2010 Springer-Verlag Berlin Heid

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Denoising of Three Dimensional Data Cube Using Bivariate Wavelet Shrinkingts infancy to denoise high dimensional data. In this paper, we extended Sendur and Selesnick’s bivariate wavelet thresholding from two-dimensional image denoising to three dimensional data denoising. Our study shows that bivariate wavelet thresholding is still valid for three dimensional data. Exper

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Segmentation Based Noise Variance Estimation from Background MRI Datages, background data is well suited for noise estimation since (theoretically) it lacks contributions from object signal. However, background data not only suffers from small contributions of object signal but also from quantization of the intensity values. In this paper, we propose a noise variance

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Morphological Thick Line Center Detectione to deal with some additional difficulties, such as the thick line distortion produced by interlaced broadcast video cameras or large shaded areas in the scene. We propose a technique to properly extract the thick lines and their centers using mathematical morphological operators. In order to illus

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Image Segmentation under Occlusion Using Selective Shape Priorsd boundaries, prior knowledge of shape of objects is introduced using the Nitzberg-Mumford-Shiota variational formulation within the segmentation energy. The novelty of our model is that the use of shape prior knowledge is automatically restricted only to occluded parts of the object boundaries. Exp

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Fusion of Edge Information in Markov Random Fields Region Growing Image Segmentationegment the image in a way that takes edge information into consideration. This is achieved by modifying the energy function minimization process so that it would penalize merging regions that have real edges in the boundary between them. Experimental results confirming the hypothesis that the additi