@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}
}
@conference {13984,
	title = {Efficient Kriging via Fast Matrix-Vector Products},
	booktitle = {Aerospace Conference, 2008 IEEE},
	year = {2008},
	month = {2008/03//},
	pages = {1 - 7},
	abstract = {Interpolating scattered data points is a problem of wide ranging interest. Ordinary kriging is an optimal scattered data estimator, widely used in geosciences and remote sensing. A generalized version of this technique, called cokriging, can be used for image fusion of remotely sensed data. However, it is computationally very expensive for large data sets. We demonstrate the time efficiency and accuracy of approximating ordinary kriging through the use of fast matrix-vector products combined with iterative methods. We used methods based on the fast Multipole methods and nearest neighbor searching techniques for implementations of the fast matrix-vector products.},
	keywords = {cokriging, data, data;scattered, efficiency;geophysical, estimator;remotely, fusion;, fusion;interpolation;iterative, matrix-vector, methods;image, methods;nearest, methods;remote, multipole, neighbor, points;time, products;fast, scattered, searching;optimal, sensed, sensing;sensor, technique;fast, techniques;iterative},
	doi = {10.1109/AERO.2008.4526433},
	author = {Memarsadeghi,N. and Raykar,V.C. and Duraiswami, Ramani and Mount, Dave}
}
@conference {12546,
	title = {Stochastic fusion of multi-view gradient fields},
	booktitle = {Image Processing, 2008. ICIP 2008. 15th IEEE International Conference on},
	year = {2008},
	month = {2008/10//},
	pages = {1324 - 1327},
	abstract = {Image gradients form powerful cues in a host of vision and graphics applications. In this paper, we consider multiple views of a textured planar scene and consider the problem of estimating the scene texture map using these multi-view inputs. Modeling each camera view as a projective transformation of the scene, we show that the problem is equivalent to that of studying the effect of noise (and the projective imaging) on the gradient fields induced by this texture map. We show that these noisy gradient fields can be modeled as complete observers of the scene radiance. Further, the corrupting noise can be shown to be additive and linear, although spatially varying. However, the specific form of the noise term can be exploited to design linear estimators that fuse the gradient fields obtained from each of the individual views. The fused gradient field forms a robust estimate of the scene gradients and can be used for scene reconstruction.},
	keywords = {application;brightness;cameras;gradient, application;image, camera, estimators;multiview, fields;projective, fusion;image, fusion;textured, gradient, gradients;linear, gradients;scene, imaging;scene, map;stochastic, methods;image, noise;graphics, planar, radiance;scene, reconstruction;image, reconstruction;scene, scene;vision, TEXTURE, texture;, view;corrupting},
	doi = {10.1109/ICIP.2008.4712007},
	author = {Sankaranarayanan,A. C and Chellapa, Rama}
}
@conference {13139,
	title = {Hierarchical Part-Template Matching for Human Detection and Segmentation},
	booktitle = {Computer Vision, 2007. ICCV 2007. IEEE 11th International Conference on},
	year = {2007},
	month = {2007/10//},
	pages = {1 - 8},
	abstract = {Local part-based human detectors are capable of handling partial occlusions efficiently and modeling shape articulations flexibly, while global shape template-based human detectors are capable of detecting and segmenting human shapes simultaneously. We describe a Bayesian approach to human detection and segmentation combining local part-based and global template-based schemes. The approach relies on the key ideas of matching a part-template tree to images hierarchically to generate a reliable set of detection hypotheses and optimizing it under a Bayesian MAP framework through global likelihood re-evaluation and fine occlusion analysis. In addition to detection, our approach is able to obtain human shapes and poses simultaneously. We applied the approach to human detection and segmentation in crowded scenes with and without background subtraction. Experimental results show that our approach achieves good performance on images and video sequences with severe occlusion.},
	keywords = {analysis;global, approach;background, articulations;video, Bayesian, detection;human, detectors;hierarchical, detectors;partial, framework;Bayesian, human, likelihood, MAP, matching;human, matching;image, methods;image, occlusion, occlusions;shape, part-based, part-template, re-evaluation;global, segmentation;image, segmentation;local, sequences;, sequences;Bayes, SHAPE, subtraction;fine, template-based},
	doi = {10.1109/ICCV.2007.4408975},
	author = {Zhe Lin and Davis, Larry S. and David Doermann and DeMenthon,D.}
}
@conference {13145,
	title = {Multimodal Tracking for Smart Videoconferencing and Video Surveillance},
	booktitle = {Computer Vision and Pattern Recognition, 2007. CVPR {\textquoteright}07. IEEE Conference on},
	year = {2007},
	month = {2007/06//},
	pages = {1 - 2},
	abstract = {Many applications require the ability to track the 3-D motion of the subjects. We build a particle filter based framework for multimodal tracking using multiple cameras and multiple microphone arrays. In order to calibrate the resulting system, we propose a method to determine the locations of all microphones using at least five loudspeakers and under assumption that for each loudspeaker there exists a microphone very close to it. We derive the maximum likelihood (ML) estimator, which reduces to the solution of the non-linear least squares problem. We verify the correctness and robustness of the multimodal tracker and of the self-calibration algorithm both with Monte-Carlo simulations and on real data from three experimental setups.},
	keywords = {(numerical, 3D, algorithm;smart, analysis;least, approximations;particle, arrays;nonlinear, cameras;multiple, Carlo, estimator;multimodal, filter;self-calibration, Filtering, least, likelihood, methods);teleconferencing;video, methods;image, microphone, MOTION, motion;Monte-Carlo, problem;particle, processing;video, signal, simulations;maximum, squares, surveillance;, surveillance;Monte, tracking;multiple, videoconferencing;video},
	doi = {10.1109/CVPR.2007.383525},
	author = {Zotkin,Dmitry N and Raykar,V.C. and Duraiswami, Ramani and Davis, Larry S.}
}
@article {14891,
	title = {Classification with nonmetric distances: image retrieval and class representation},
	journal = {Pattern Analysis and Machine Intelligence, IEEE Transactions on},
	volume = {22},
	year = {2000},
	month = {2000/06//},
	pages = {583 - 600},
	abstract = {A key problem in appearance-based vision is understanding how to use a set of labeled images to classify new images. Systems that model human performance, or that use robust image matching methods, often use nonmetric similarity judgments; but when the triangle inequality is not obeyed, most pattern recognition techniques are not applicable. Exemplar-based (nearest-neighbor) methods can be applied to a wide class of nonmetric similarity functions. The key issue, however, is to find methods for choosing good representatives of a class that accurately characterize it. We show that existing condensing techniques are ill-suited to deal with nonmetric dataspaces. We develop techniques for solving this problem, emphasizing two points: First, we show that the distance between images is not a good measure of how well one image can represent another in nonmetric spaces. Instead, we use the vector correlation between the distances from each image to other previously seen images. Second, we show that in nonmetric spaces, boundary points are less significant for capturing the structure of a class than in Euclidean spaces. We suggest that atypical points may be more important in describing classes. We demonstrate the importance of these ideas to learning that generalizes from experience by improving performance. We also suggest ways of applying parametric techniques to supervised learning problems that involve a specific nonmetric distance functions, showing how to generalize the idea of linear discriminant functions in a way that may be more useful in nonmetric spaces},
	keywords = {appearance-based, by, classification;image, correlation;correlation, dataspaces;nonmetric, distances;nonmetric, example;, functions;nonmetric, image, inequality;vector, judgments;robust, MATCHING, methods;image, methods;nonmetric, methods;triangle, points;boundary, points;class, representation;exemplar-based, representation;image, retrieval;learning, retrieval;nearest-neighbor, similarity, vision;atypical},
	isbn = {0162-8828},
	doi = {10.1109/34.862197},
	author = {Jacobs, David W. and Weinshall,D. and Gdalyahu,Y.}
}
