Ashish Phophalia

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A new denoising filter is proposed for human brain MR image. The proposed filter is based on the notion of existing bilateral filter whose objective is to get a noise-free smooth image, preserving edges and other features intact. We have introduced a weighing function that controls the impact of existing bilateral filter for denoising. It is conditioned by(More)
Eye Localization is a preprocessing step for operations such as orientation correction and scaling required in face recognition problem. The success of facial feature analysis and face recognition system depends on eye position detected and the other facial features estimated on this. This paper presents a hybrid approach for eye localization in a video(More)
A Rough Set Theory based closed form object boundary detection method has been suggested in this paper. Most of the edge detection methods fail in getting closed boundary of objects of any shape present in the image. Active contour based methods are available to get such object boundaries. The Multiphase Chan-Vese Active Contour Method is one of the most(More)
The Finite Mixture Model (FMM) based approaches have been applied in Magnetic Resonance Imaging (MRI) to extract information about human anatomy. The idea is to model feature vector of a tissue using some known distribution (such as Gaussian, known as GMM). The performance of FMM deteriorates with increase in noise within data which may occur due to(More)
In this paper, we have presented a two stage method, using kernel principal component analysis (KPCA) and rough set theory (RST), for denoising volumetric MRI data. A rough set theory (RST) based clustering technique has been used for voxel based processing. The method groups similar voxels (3D cubes) using class and edge information derived from noisy(More)
Current state-of-the-art research on denoising involves patch similarity. The similar patches are obtained either from image itself or from dictionary of patches. This paper proposes a new way to find similar patches from a given image using Rough Set Theory (RST). Search for similar patches is usually restricted locally. However, a global search could(More)
In this paper, we propose a novel approach to explore self-similarity of an image for patch based image processing application. The motivation of this work is to search for a similar set of pixels from a given image for each pixel or patch present in the image. So far, the search for similarity exploration in the image is a time consuming task and(More)