Mithun Das Gupta

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Non-negative matrix factorization (NMF) has previously been shown to be a useful decomposition tool for multivariate data. Non-negative bases allow strictly additive combinations which have been shown to be part-based as well as relatively sparse. We pursue a discriminative decomposition by coupling NMF objective with a maximum margin classifier,(More)
We present a novel learning-based framework for zooming and recognizing images of digits obtained from vehicle registration plates, which have been blurred using an unknown kernel. We model the image as an undirected graphical model over image patches in which the compatibility functions are represented as nonparametric kernel densities. The crucial feature(More)
In this paper, we present a novel learning based framework for performing super-resolution using multiple images. We model the image as an undirected graphical model over image patches in which the compatibility functions are represented as non-parametric kernel densities which are learnt from training data. The observed images are translation rectified and(More)
In this paper we present a novel learning based method for restoring and recognizing images of digits that have been blurred using an unknown kernel. The novelty of our work is an iterative loop that alternates between recognition and restoration stages. In the restoration stage we model the image as an undirected graphical model over the image patches with(More)
In this paper we present a supervised learning approach for object-category specific restoration, recognition and segmentation of images which are blurred using an unknown kernel. The feature of this work is a multi layer graphical model which unifies the low level vision task of restoration, and the high level vision task of recognition in a cooperative(More)
In recent days Lifetime of Wireless Sensor Network (WSN) is an important consideration. The lifetime of a WSN depends on the power consumption of sensor nodes. To minimize the power consumption of WSN we propose "Bubbling Mechanism" in which the transmission ranges of nodes are altered temporarily, a concept "Border Cluster" in which we keep more number of(More)
In this paper we present a symmetric KL divergence based agglomerative clustering framework to segment multiple levels of depigmentation in Vitiligo images. The proposed framework starts with a simple merge cost based on symmetric KL divergence. We extend the recent body of work related to Bregman divergence based agglomerative clustering and prove that the(More)
We present a supervised learning-based approach for subpixel motion estimation which is then used to perform video superresolution. The novelty of this work is the formulation of the problem of subpixel motion estimation in a ranking framework. The ranking formulation is a variant of classification and regression formulation, in which the ordering present(More)