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Your search keyword '"Ishwar, Prakash"' showing total 15 results
Start Over Author "Ishwar, Prakash" Publication Type Books
15 results on '"Ishwar, Prakash"'

2. Contributors

Author

Ahmad, Sajjad, primary, Bajeli, Jyoti, additional, Bhatt, Kalpana, additional, Bhatt, Pankaj, additional, Bind, Sandhya, additional, Bind, Sudha, additional, Chandra, Dinesh, additional, Chandra, Satish, additional, Chaudhary, Parul, additional, Chen, Shaohua, additional, Dasila, Hemant, additional, Dieudonne, Nwaga, additional, Efe, Derya, additional, Fabrice, Fekam Boyom, additional, Ghosh, Sudip Kumar, additional, Gormez, Arzu, additional, Gunjal, Aparna B., additional, Jeena, Neha, additional, Khan, Amir, additional, Kukreti, Bharti, additional, Kumar, Abhishek, additional, Kumar, Amit, additional, Kumar, Anand, additional, Kumar, Kunal, additional, Kumar, Narendra, additional, Kumar, Saurabh, additional, Kumari, Garima, additional, Maiti, Tushar Kanti, additional, Makarana, Govind, additional, Martoliya, Yogita, additional, Mitra, Soumik, additional, Mohapatra, Sridev, additional, Mondal, Sayanta, additional, Mondal, Tanushree, additional, Nikhil, P.T., additional, Orhan, Furkan, additional, Pal, Priyanka, additional, Pande, Veni, additional, Pandey, Satish Chandra, additional, Pierre, Eke, additional, Pramanik, Krishnendu, additional, Punetha, Arjita, additional, Raja Gopalan, N.S., additional, Rawat, Sunita, additional, Raymond, Fokom, additional, Samant, Mukesh, additional, Sati, Diksha, additional, Sharma, Ishwar Prakash, additional, Sharma, Raunak, additional, Singh, Ajay Veer, additional, Singh, Manish, additional, Soren, Tithi, additional, Souleymanou, Adamou, additional, Suyal, Deep Chandra, additional, Therese, Ngo Oum, additional, Verma, Aakansha, additional, and Verma, Shulbhi, additional

Published
2022
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14. Action Recognition in Video by Sparse Representation on Covariance Manifolds of Silhouette Tunnels.

Author

Guo, Kai, Ishwar, Prakash, and Konrad, Janusz

Abstract

A novel framework for action recognition in video using empirical covariance matrices of bags of low-dimensional feature vectors is developed. The feature vectors are extracted from segments of silhouette tunnels of moving objects and coarsely capture their shapes. The matrix logarithm is used to map the segment covariance matrices, which live in a nonlinear Riemannian manifold, to the vector space of symmetric matrices. A recently developed sparse linear representation framework for dictionary-based classification is then applied to the log-covariance matrices. The log-covariance matrix of a query segment is approximated by a sparse linear combination of the log-covariance matrices of training segments and the sparse coefficients are used to determine the action label of the query segment. This approach is tested on the Weizmann and the UT-Tower human action datasets. The new approach attains a segment-level classification rate of 96.74% for the Weizmann dataset and 96.15% for the UT-Tower dataset. Additionally, the proposed method is computationally and memory efficient and easy to implement. [ABSTRACT FROM AUTHOR]

Published
2010
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15. An Analysis of Empirical PMF Based Tests for Least Significant Bit Image Steganography.

Author

Barni, Mauro, Herrera-Joancomartí, Jordi, Katzenbeisser, Stefan, Pérez-González, Fernando, Draper, Stark, Ishwar, Prakash, Molnar, David, Prabhakaran, Vinod, Ramchandran, Kannan, Schonberg, Daniel, and Wagner, David

Abstract

We consider here the class of probability mass-function (PMF) based detectors of least significant bit (LSB) embedded steganography. That is, in this paper we investigate the use of frequency counts of pixel intensities as a statistic for tests detecting the presence of hidden messages. We focus on LSB replacement (though we briefly consider LSB matching) embedding as it is a simple technique where the effect on the true PMF of the resulting image can be understood mathematically. We begin our study by considering the existing tests of Westfeld and Pfitzmann [11] and Dabeer et al.[1]. These tests assume that pixel intensities are random values that are independent and identically distributed (i.i.d.). We generalize these tests by considering PMFs of neighboring pixel intensities. We argue that consideration of higher order of correlations provide only diminishing marginal returns, and thus we can make general statements on the value of PMF based detectors. We measure the performance of our tests by calculation of receiver operating curves (ROC) over a corpus of 350 digital images. We then proceed to compare to a non-PMF based test, in particular the RS tests of Fridrich et al [3]. Although our generalized tests outperform existing PMF based predecessors, they are outperformed by the RS tests. This indicates that using PMFs as a statistic for detecting hidden messages is inherently insufficient. [ABSTRACT FROM AUTHOR]

Published
2005
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