@conference {13212,
	title = {A fine-structure image/video quality measure using local statistics},
	booktitle = {Image Processing, 2004. ICIP {\textquoteright}04. 2004 International Conference on},
	volume = {5},
	year = {2004},
	month = {2004/10//},
	pages = {3535 - 3538 Vol. 5 - 3535 - 3538 Vol. 5},
	abstract = {An objective no-reference measure is presented to assess line-structure image/video quality. It was designed to measure image/video quality for video surveillance applications, especially for background modeling and foreground object detection. The proposed measure using local statistics reflects image degradation well in terms of noise and blur. The experimental results on a background subtraction algorithm validate the usefulness of the proposed measure, by showing its correlation with the algorithm{\textquoteright}s performance.},
	keywords = {algorithm;, background, degradation;, detection;, foreground, image, line-structure, local, measure;, modeling;, no-reference, object, objective, processing;, QUALITY, signal, statistics;, subtraction, surveillance;, video},
	doi = {10.1109/ICIP.2004.1421879},
	author = {Kim,Kyungnam and Davis, Larry S.}
}
@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.}
}
