Pulak Purkait

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Multiscale morphological operators are studied extensively in the literature for image processing and feature extraction purposes. In this paper, we model a nonlinear regularization method based on multiscale morphology for edge-preserving super resolution (SR) image reconstruction. We formulate SR image reconstruction as a deblurring problem and then solve(More)
Linking a person based on handwritten documents is one of the oldest techniques that is used by crime investigators and forensic scientists. The importance of writer recognition in anthrax letter cases has made this examination popular in recent years. In this paper we propose four feature set namely directional opening, directional closing, direction(More)
We propose a new high-quality up-scaling technique that extends the existing example based super-resolution (SR) framework. Our approach is based on the fundamental idea that a low-resolution (LR) image could be generated from any of the multiple possible high-resolution (HR) images. Therefore it would be more natural to use multiple predictors of HR patch(More)
Maximum consensus is one of the most popular criteria for robust estimation in computer vision. Despite its widespread use, optimising the criterion is still customarily done by randomised sample-and-test techniques, which do not guarantee optimality of the result. Several globally optimal algorithms exist, but they are too slow to challenge the dominance(More)
In this paper, a novel fuzzy rule-based prediction framework is developed for high-quality image zooming. In classical interpolation-based image zooming, resolution is increased by inserting pixels using certain interpolation techniques. Here, we propose a patch-based image zooming technique, where each low-resolution (LR) image patch is replaced by an(More)
The extension of conventional clustering to hypergraph clustering, which involves higher order similarities instead of pairwise similarities, is increasingly gaining attention in computer vision. This is due to the fact that many clustering problems require an affinity measure that must involve a subset of data of size more than two. In the context of(More)
In this paper, we address an interesting application of computer vision technique, namely classification of Indian Classical Dance (ICD). With the best of our knowledge, the problem has not been addressed so far in computer vision domain. To deal with this problem, we use a sparse representation based dictionary learning technique. First, we represent each(More)