Výsledky vyhledávání - "Sengupta, Debapriya"

Akademický článek

A survey on mutual information based medical image registration algorithms

Autor: Sengupta, Debapriya, Gupta, Phalguni, Biswas, Arindam

Publikováno v: In Neurocomputing 14 May 2022 486:174-188

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Akademický článek

Risk-Based Screening Mammography for Women Aged

Autor: Lee, Cindy S., Ashih, Heidi, Sengupta, Debapriya, Sickles, Edward A., Zuley, Margarita, Pisano, Etta

Publikováno v: In Journal of the American College of Radiology March 2020 17(3):368-376

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Akademický článek

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A spatio-spectral hybridization for edge preservation and noisy image restoration via local parametric mixtures and Lagrangian relaxation

Autor: Basu, Kinjal, Sengupta, Debapriya

This paper investigates a fully unsupervised statistical method for edge preserving image restoration and compression using a spatial decomposition scheme. Smoothed maximum likelihood is used for local estimation of edge pixels from mixture parametri

Externí odkaz: http://arxiv.org/abs/1209.1826

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Spline Smoothing for Estimation of Circular Probability Distributions via Spectral Isomorphism and its Spatial Adaptation

Autor: Basu, Kinjal, Sengupta, Debapriya

Consider the problem when $X_1,X_2,..., X_n$ are distributed on a circle following an unknown distribution $F$ on $S^1$. In this article we have consider the absolute general set-up where the density can have local features such as discontinuities an

Externí odkaz: http://arxiv.org/abs/1209.1740

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Projected likelihood contrasts for testing homogeneity in finite mixture models with nuisance parameters

Autor: Sengupta, Debapriya, Mazumder, Rahul

Publikováno v: IMS Collections 2008, Vol. 1, 272-281

This paper develops a test for homogeneity in finite mixture models where the mixing proportions are known a priori (taken to be 0.5) and a common nuisance parameter is present. Statistical tests based on the notion of Projected Likelihood Contrasts

Externí odkaz: http://arxiv.org/abs/0805.2460

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Pitch Tracking of Acoustic Signals based on Average Squared Mean Difference Function

Autor: Chakraborty, Roudra, Sengupta, Debapriya, Sinha, Sagnik

In this paper, a method of pitch tracking based on variance minimization of locally periodic subsamples of an acoustic signal is presented. Replicates along the length of the periodically sampled data of the signal vector are taken and locally averag

Externí odkaz: http://arxiv.org/abs/cs/0701177

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