Mithun Das Gupta

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Non-negative matrix factorization (NMF) has previously been shown to be a useful decomposition tool for multivari-ate 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 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 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)
We propose a new filter called Bi-affinity filter for color images. This filter is similar in structure to the bilateral filter. The proposed filter is based on the color line model, which does not require the explicit conversion of the RGB values to perception based spaces such as CIELAB. The bi-affinity filter measures the affinity of a pixel to a small(More)
This paper studies the problem of simultaneously registering and segmenting a pair of images despite the presence of non-smooth boundaries. We assume one of the images is well segmented by automated algorithm or user interaction. This image acts as an atlas for the segmentation process. For the remaining images in the set, we propose a novel L1 minimization(More)
Automated robust segmentation of intra-ventricular septum (IVS) from B-mode echocardiographic images is an enabler for early quantification of cardiac disease. Segmentation of septum from ultrasound images is very challenging due to variations in intensity/contrast in and around the septum, speckle noise and non-rigid shape variations of the septum(More)