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Considering the problem that structure information can be easily lost when the edge and texture regions of image are denoised by the Non-Local Means (NL-Means) denoising algorithm, and a NL-Means image denoising algorithm based on edge detection is proposed in this paper. Firstly the edge detection in the noise image is got by using the improved Sobel operator, and then the detecting result is used...
In this paper we present a multiphase level set model for histology image segmentation. Global K-means energy is weighted by a Gaussian kernel to cluster image pixels in local neighborhoods. We group these local clusters into different source classes using a multiphase level set model to produce the final segmentation results. Our energy functional is formulated as the integral of local K-means energies...
This paper presents a new algorithm for hematoxylin and eosin (H&E) stained histology image segmentation. With both local and global clustering, Gaussian mixture models (GMMs) are applied sequentially to extract tissue constituents such as nuclei, stroma, and connecting contents from background. Specifically, local GMM is firstly applied to detect nuclei by scanning the input image, which is followed...
In this paper, we present a new object matching algorithm based on linear programming and a novel locally affine-invariant geometric constraint. Previous works have shown possible ways to solve the feature and object matching problem by linear programming techniques. To model and solve the matching problem in a linear formulation, all geometric constraints should be able to be exactly or approximately...
This paper presents a new local edge-based level set model that does not use initial contours. Unlike traditional edge-based active contours that use gradient to detect edges, our model derives the neighborhood distribution and edge information with two different localized region-based operators: a Gaussian mixture model-based intensity distribution estimator and the Hueckel operator. We incorporate...
This paper presents a novel anisotropic diffusion framework for color image denoising. Unlike previous approaches, our method is based on separating a color image into hue, saturation and intensity (HSI) components, and then diffusing each component with spatially varying weighted partial differential equations (PDE). The hue denoising is implemented by a new weighted orientation diffusion, and the...
This paper presents a new image denoising model for real color photo noise removal. Our model is implemented in the hue, saturation and intensity (HSI) space. The hue and saturation denoising are combined and implemented as a complex total variation (TV) diffusion. The intensity denoising is based on a diffusion flow to minimize a new energy functional, which is constructed with intensity component...
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