Uma Mudenagudi

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We address the problem of super resolved generation of novel views of a 3D scene with the reference images obtained from cameras in general positions; a problem which has not been tackled before in the context of super resolution and is also of importance to the field of image based rendering. We formulate the problem as one of estimation of the color at(More)
We propose a method for estimating depth from images captured with a real aperture camera by fusing defocus and stereo cues. The idea is to use stereo-based constraints in conjunction with defocusing to obtain improved estimates of depth over those of stereo or defocus alone. The depth map as well as the original image of the scene are modeled as Markov(More)
This paper addresses the problem of super resolution-obtaining a single high-resolution image given a set of low resolution images which are related by small displacements. We employ reconstruction based approach using MRF-MAP formalism, and use approximate optimization using graph cuts to carry out the reconstruction. We also use the same formalism to(More)
We address the problem of super-resolution—obtaining high-resolution images and videos from multiple low-resolution inputs. The increased resolution can be in spatial or temporal dimensions, or even in both. We present a unified framework which uses a generative model of the imaging process and can address spatial super-resolution, space-time(More)
In this paper we propose to address the problem of 3D object categorization. We model the 3D object as a 2D Riemannian manifold and propose metric tensor and Christoffel symbols as a novel set of features. The proposed set of features capture the local and global geometry of 3D objects by exploiting the positional dependence of the features. The(More)
In this paper we address the problem of detection of image doctoring. Doctoring is a process of altering or modifying the contents of an authentic image with varied motivations. We propose two frameworks to address the problem of detection of image doctoring: (i) bi-spectral analysis (ii) correlation pattern of the Point Spread Function (PSF) using(More)
We address the problem of detection of image doctoring using correlations of Point Spread Function (PSF) and iterative blind deconvolution. Doctoring is a process of tampering or hampering or changing the content of an image in order to deceive people or rewrite history or exaggerate the situations or customize ground-breaking advances in research,(More)
We address the problem of 3D inpainting using ROI-based method for point cloud data. We focus on inpainting of complex, irregular and large missing regions covering prominent geometric features by considering <i>n</i> self-similar examples. The effectiveness of the proposed framework is demonstrated on 3D artifacts obtained from archaeological sites.