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Two challenges in computer vision are to accommodate noisy data and missing data. Many problems in computer vision, such as segmentation, filtering, stereo, reconstruction, inpainting and optical flow seek solutions that match the data while satisfying an additional regularization, such as total variation or boundary length. A regularization which has(More)
In this paper, we introduce a novel hierarchical approach for multiphase image segmentation. The approach presents a unified framework that unifies two basic segmentation approaches; level set methods and graph cut algorithms. In the work of El-Zehiry et al. (2007), we have presented a bimodal image segmentation approach that have the advantages of the(More)
Minimization of boundary curvature is a classic regularization technique for image segmentation in the presence of noisy image data. Techniques for minimizing curvature have historically been derived from descent methods which could be trapped in a local minimum and therefore required a good initial-ization. Recently, combinatorial optimization techniques(More)
Minimization of boundary curvature is a classic regularization technique for image segmentation in the presence of noisy image data. Techniques for minimizing curvature have historically been derived from gradient descent methods which could be trapped by a local minimum and, therefore, required a good initialization. Recently, combinatorial optimization(More)
The Mumford-Shah model has been one of the most powerful models in image segmentation and denoising. The optimization of the multiphase Mumford-Shah energy functional has been performed using level sets methods that optimize the Mumford-Shah energy by evolving the level sets via the gradient descent. These methods are very slow and prone to getting stuck in(More)
This paper presents a novel graph cut based seg-mentation approach with shape priors. The model incorporates statistical shape prior information with the active contour without edges model [6]. Our model also relaxes the homogeneity constraint that assumes that the image is modeled by a piece-wise constant approximation. The major contribution of this paper(More)
The minicolumn is generally considered the basic unit of the neocortex in all the mammalian brains. Enlargement of the cor-tical surface is believed to occur through the addition of mini-columns rather than a single neuron.This study aims at testing the hypothesis that brain developmental disorders can be diagnosed and analyzed in terms of the minicolumnar(More)
The Mumford–Shah model has been one of the most influential models in image segmentation and denoising. The optimization of the multiphase Mumford–Shah energy functional has been performed using level sets methods that optimize the Mumford–Shah energy by evolving the level sets via the gradient descent. These methods are very slow and prone to getting stuck(More)