@conference {12551,
	title = {Statistical analysis on Stiefel and Grassmann manifolds with applications in computer vision},
	booktitle = {Computer Vision and Pattern Recognition, 2008. CVPR 2008. IEEE Conference on},
	year = {2008},
	month = {2008/06//},
	pages = {1 - 8},
	abstract = {Many applications in computer vision and pattern recognition involve drawing inferences on certain manifold-valued parameters. In order to develop accurate inference algorithms on these manifolds we need to a) understand the geometric structure of these manifolds b) derive appropriate distance measures and c) develop probability distribution functions (pdf) and estimation techniques that are consistent with the geometric structure of these manifolds. In this paper, we consider two related manifolds - the Stiefel manifold and the Grassmann manifold, which arise naturally in several vision applications such as spatio-temporal modeling, affine invariant shape analysis, image matching and learning theory. We show how accurate statistical characterization that reflects the geometry of these manifolds allows us to design efficient algorithms that compare favorably to the state of the art in these very different applications. In particular, we describe appropriate distance measures and parametric and non-parametric density estimators on these manifolds. These methods are then used to learn class conditional densities for applications such as activity recognition, video based face recognition and shape classification.},
	keywords = {algorithm;learning, analysis;computer, analysis;statistical, analysis;video, based, classification;image, classification;spatio-temporal, distribution, distributions;, Face, functions;shape, Grassmann, invariant, manifold;activity, manifold;Stiefel, matching;inference, matching;spatiotemporal, measures;estimation, modeling;statistical, parameters;pattern, phenomena;statistical, recognition;affine, recognition;computer, recognition;probability, SHAPE, structure;image, technique;geometric, theory;manifold-valued, vision;distance, vision;image},
	doi = {10.1109/CVPR.2008.4587733},
	author = {Turaga,P. and Veeraraghavan,A. and Chellapa, Rama}
}
@conference {13216,
	title = {Iterative figure-ground discrimination},
	booktitle = {Pattern Recognition, 2004. ICPR 2004. Proceedings of the 17th International Conference on},
	volume = {1},
	year = {2004},
	month = {2004/08//},
	pages = {67 - 70 Vol.1 - 67 - 70 Vol.1},
	abstract = {Figure-ground discrimination is an important problem in computer vision. Previous work usually assumes that the color distribution of the figure can be described by a low dimensional parametric model such as a mixture of Gaussians. However, such approach has difficulty selecting the number of mixture components and is sensitive to the initialization of the model parameters. In this paper, we employ non-parametric kernel estimation for color distributions of both the figure and background. We derive an iterative sampling-expectation (SE) algorithm for estimating the color, distribution and segmentation. There are several advantages of kernel-density estimation. First, it enables automatic selection of weights of different cues based on the bandwidth calculation from the image itself. Second, it does not require model parameter initialization and estimation. The experimental results on images of cluttered scenes demonstrate the effectiveness of the proposed algorithm.},
	keywords = {algorithm;, analysis;, Bandwidth, calculation;, Color, colour, Computer, density, dimensional, discrimination;, distribution;, distributions;, Estimation, estimation;, expectation, figure, Gaussian, ground, image, initialization;, iterative, Kernel, low, methods;, mixture;, model, model;, nonparametric, parameter, parametric, processes;, sampling, sampling;, segmentation, segmentation;, statistics;, theory;, vision;},
	doi = {10.1109/ICPR.2004.1334006},
	author = {Zhao, L. and Davis, Larry S.}
}
@conference {12721,
	title = {An appearance based approach for human and object tracking},
	booktitle = {Image Processing, 2003. ICIP 2003. Proceedings. 2003 International Conference on},
	volume = {2},
	year = {2003},
	month = {2003/09//},
	pages = {II - 85-8 vol.3 - II - 85-8 vol.3},
	abstract = {A system for tracking humans and detecting human-object interactions in indoor environments is described. A combination of correlogram and histogram information is used to model object and human color distributions. Humans and objects are detected using a background subtraction algorithm. The models are built on the fly and used to track them on a frame by frame basis. The system is able to detect when people merge into groups and segment them during occlusion. Identities are preserved during the sequence, even if a person enters and leaves the scene. The system is also able to detect when a person deposits or removes an object from the scene. In the first case the models are used to track the object retroactively in time. In the second case the objects are tracked for the rest of the sequence. Experimental results using indoor video sequences are presented.},
	keywords = {algorithm;, analysis;, background, basis;, by, Color, colour, correlogram, detection;, distributions;, frame, histogram, human, image, information;, object, processing;, segmentation;, sequences;, signal, subtraction, tracking;, video},
	doi = {10.1109/ICIP.2003.1246622},
	author = {Capellades,M. B and David Doermann and DeMenthon,D. and Chellapa, Rama}
}
@conference {13514,
	title = {Binarization of low quality text using a Markov random field model},
	booktitle = {Pattern Recognition, 2002. Proceedings. 16th International Conference on},
	volume = {3},
	year = {2002},
	month = {2002///},
	pages = {160 - 163 vol.3 - 160 - 163 vol.3},
	abstract = {Binarization techniques have been developed in the document analysis community for over 30 years and many algorithms have been used successfully. On the other hand, document analysis tasks are more and more frequently being applied to multimedia documents such as video sequences. Due to low resolution and lossy compression, the binarization of text included in the frames is a non-trivial task. Existing techniques work without a model of the spatial relationships in the image, which makes them less powerful. We introduce a new technique based on a Markov random field model of the document. The model parameters (clique potentials) are learned from training data and the binary image is estimated in a Bayesian framework. The performance is evaluated using commercial OCR software.},
	keywords = {analysis;, annealing;, Bayes, Bayesian, binarization;, computing;, distributions;, document, documents;, field;, Gibbs, image, low, Markov, method;, methods;, multimedia, optimization;, probability;, processes;, processing;, QUALITY, random, simulated, text},
	doi = {10.1109/ICPR.2002.1047819},
	author = {Wolf,C. and David Doermann}
}
