@conference {12511,
	title = {Enhancing sparsity using gradients for compressive sensing},
	booktitle = {Image Processing (ICIP), 2009 16th IEEE International Conference on},
	year = {2009},
	month = {2009/11//},
	pages = {3033 - 3036},
	abstract = {In this paper, we propose a reconstruction method that recovers images assumed to have a sparse representation in a gradient domain by using partial measurement samples that are collected in the Fourier domain. A key improvement of this technique is that it makes use of a robust generalized Poisson solver that greatly aids in achieving a significantly improved performance over similar proposed methods. Experiments provided also demonstrate that this new technique is more flexible to work with either random or restricted sampling scenarios better than its competitors.},
	keywords = {analysis;gradient, domain;compressive, domain;image, Fourier, generalized, measurement, methods;, methods;image, Poisson, reconstruction;image, reconstruction;partial, representation;Fourier, representation;sampling, samples;robust, scenarios;sparse, sensing;enhancing, solver;sampling, sparsity;gradient},
	doi = {10.1109/ICIP.2009.5414411},
	author = {Patel, Vishal M. and Easley,G. R and Chellapa, Rama and Healy,D. M}
}
@article {14023,
	title = {Speaker Localization Using Excitation Source Information in Speech},
	journal = {Speech and Audio Processing, IEEE Transactions on},
	volume = {13},
	year = {2005},
	month = {2005/09//},
	pages = {751 - 761},
	abstract = {This paper presents the results of simulation and real room studies for localization of a moving speaker using information about the excitation source of speech production. The first step in localization is the estimation of time-delay from speech collected by a pair of microphones. Methods for time-delay estimation generally use spectral features that correspond mostly to the shape of vocal tract during speech production. Spectral features are affected by degradations due to noise and reverberation. This paper proposes a method for localizing a speaker using features that arise from the excitation source during speech production. Experiments were conducted by simulating different noise and reverberation conditions to compare the performance of the time-delay estimation and source localization using the proposed method with the results obtained using the spectrum-based generalized cross correlation (GCC) methods. The results show that the proposed method shows lower number of discrepancies in the estimated time-delays. The bias, variance and the root mean square error (RMSE) of the proposed method is consistently equal or less than the GCC methods. The location of a moving speaker estimated using the time-delays obtained by the proposed method are closer to the actual values, than those obtained by the GCC method.},
	keywords = {correlation, correlation;, cross, Delay, error, error;, estimation;, excitation, generalized, information;, localization;, mean, methods;, processing;, production;, root, source, speaker, speech, square, TIME},
	isbn = {1063-6676},
	doi = {10.1109/TSA.2005.851907},
	author = {Raykar,V.C. and Yegnanarayana,B. and Prasanna,S. R.M and Duraiswami, Ramani}
}
@article {12719,
	title = {Accurate dense optical flow estimation using adaptive structure tensors and a parametric model},
	journal = {Image Processing, IEEE Transactions on},
	volume = {12},
	year = {2003},
	month = {2003/10//},
	pages = {1170 - 1180},
	abstract = {An accurate optical flow estimation algorithm is proposed in this paper. By combining the three-dimensional (3D) structure tensor with a parametric flow model, the optical flow estimation problem is converted to a generalized eigenvalue problem. The optical flow can be accurately estimated from the generalized eigenvectors. The confidence measure derived from the generalized eigenvalues is used to adaptively adjust the coherent motion region to further improve the accuracy. Experiments using both synthetic sequences with ground truth and real sequences illustrate our method. Comparisons with classical and recently published methods are also given to demonstrate the accuracy of our algorithm.},
	keywords = {3D, accuracy;, adaptive, and, coherent, confidence, dense, eigenfunctions;, eigenvalue, eigenvalues, eigenvectors;, estimation;, flow, generalized, ground, image, measure;, model;, MOTION, optical, parameter, parametric, problem;, real, region;, sequences;, structure, synthetic, tensor;, tensors;, three-dimensional, truth;},
	isbn = {1057-7149},
	doi = {10.1109/TIP.2003.815296},
	author = {Liu,Haiying and Chellapa, Rama and Rosenfeld, A.}
}
