@conference {13940,
	title = {Scalable fast multipole methods on distributed heterogeneous architectures},
	booktitle = {High Performance Computing, Networking, Storage and Analysis (SC), 2011 International Conference for},
	year = {2011},
	month = {2011/11//},
	pages = {1 - 12},
	abstract = {We fundamentally reconsider implementation of the Fast Multipole Method (FMM) on a computing node with a heterogeneous CPU-GPU architecture with multicore CPU(s) and one or more GPU accelerators, as well as on an interconnected cluster of such nodes. The FMM is a divide- and-conquer algorithm that performs a fast N-body sum using a spatial decomposition and is often used in a time- stepping or iterative loop. Using the observation that the local summation and the analysis-based translation parts of the FMM are independent, we map these respectively to the GPUs and CPUs. Careful analysis of the FMM is performed to distribute work optimally between the multicore CPUs and the GPU accelerators. We first develop a single node version where the CPU part is parallelized using OpenMP and the GPU version via CUDA. New parallel algorithms for creating FMM data structures are presented together with load balancing strategies for the single node and distributed multiple-node versions. Our implementation can perform the N-body sum for 128M particles on 16 nodes in 4.23 seconds, a performance not achieved by others in the literature on such clusters.},
	keywords = {accelerators;OpenMP;analysis, algorithm;iterative, and, architecture;CUDA;FMM, architectures;, architectures;divide-and-conquer, based, conquer, CPU-GPU, CPU;scalable, data, fast, heterogeneous, loop;data, loop;multicore, methods;graphics, methods;multiprocessing, methods;time, multipole, parts;distributed, PROCESSING, stepping, structures;divide, structures;GPU, systems;parallel, translation, units;iterative},
	author = {Hu,Qi and Gumerov, Nail A. and Duraiswami, Ramani}
}
@conference {13196,
	title = {Efficient mean-shift tracking via a new similarity measure},
	booktitle = {Computer Vision and Pattern Recognition, 2005. CVPR 2005. IEEE Computer Society Conference on},
	volume = {1},
	year = {2005},
	month = {2005/06//},
	pages = {176 - 183 vol. 1 - 176 - 183 vol. 1},
	abstract = {The mean shift algorithm has achieved considerable success in object tracking due to its simplicity and robustness. It finds local minima of a similarity measure between the color histograms or kernel density estimates of the model and target image. The most typically used similarity measures are the Bhattacharyya coefficient or the Kullback-Leibler divergence. In practice, these approaches face three difficulties. First, the spatial information of the target is lost when the color histogram is employed, which precludes the application of more elaborate motion models. Second, the classical similarity measures are not very discriminative. Third, the sample-based classical similarity measures require a calculation that is quadratic in the number of samples, making real-time performance difficult. To deal with these difficulties we propose a new, simple-to-compute and more discriminative similarity measure in spatial-feature spaces. The new similarity measure allows the mean shift algorithm to track more general motion models in an integrated way. To reduce the complexity of the computation to linear order we employ the recently proposed improved fast Gauss transform. This leads to a very efficient and robust nonparametric spatial-feature tracking algorithm. The algorithm is tested on several image sequences and shown to achieve robust and reliable frame-rate tracking.},
	keywords = {algorithm;, analysis;, Bhattacharyya, coefficient;, Color, colour, density, divergence;, estimates;, extraction;, fast, feature, frame-rate, Gauss, Gaussian, histograms;, image, Kernel, Kullback-Leibler, matching;, Mean-shift, measures;, nonparametric, processes;, sample-based, sequences;, similarity, spaces;, spatial-feature, tracking, tracking;, transform;},
	doi = {10.1109/CVPR.2005.139},
	author = {Yang,Changjiang and Duraiswami, Ramani and Davis, Larry S.}
}
@conference {13186,
	title = {Fast multiple object tracking via a hierarchical particle filter},
	booktitle = {Computer Vision, 2005. ICCV 2005. Tenth IEEE International Conference on},
	volume = {1},
	year = {2005},
	month = {2005/10//},
	pages = {212 - 219 Vol. 1 - 212 - 219 Vol. 1},
	abstract = {A very efficient and robust visual object tracking algorithm based on the particle filter is presented. The method characterizes the tracked objects using color and edge orientation histogram features. While the use of more features and samples can improve the robustness, the computational load required by the particle filter increases. To accelerate the algorithm while retaining robustness we adopt several enhancements in the algorithm. The first is the use of integral images for efficiently computing the color features and edge orientation histograms, which allows a large amount of particles and a better description of the targets. Next, the observation likelihood based on multiple features is computed in a coarse-to-fine manner, which allows the computation to quickly focus on the more promising regions. Quasi-random sampling of the particles allows the filter to achieve a higher convergence rate. The resulting tracking algorithm maintains multiple hypotheses and offers robustness against clutter or short period occlusions. Experimental results demonstrate the efficiency and effectiveness of the algorithm for single and multiple object tracking.},
	keywords = {(numerical, algorithm;, analysis;, Color, colour, Computer, Convergence, detection;, edge, fast, filter;, Filtering, hierarchical, histogram;, image, images;, integral, likelihood;, methods);, methods;, multiple, numerical, object, observation, of, orientation, particle, processes;, quasirandom, random, sampling;, tracking, tracking;, vision;, visual},
	doi = {10.1109/ICCV.2005.95},
	author = {Yang,Changjiang and Duraiswami, Ramani and Davis, Larry S.}
}
@conference {13209,
	title = {Multi-level fast multipole method for thin plate spline evaluation},
	booktitle = {Image Processing, 2004. ICIP {\textquoteright}04. 2004 International Conference on},
	volume = {3},
	year = {2004},
	month = {2004/10//},
	pages = {1683 - 1686 Vol. 3 - 1683 - 1686 Vol. 3},
	abstract = {Image registration is an important problem in image processing and computer vision. Much recent work in image registration is on matching non-rigid deformations. Thin plate splines are an effective image registration method when the deformation between two images can be modeled as the bending of a thin metal plate on point constraints such that the topology is preserved (non-rigid deformation). However, because evaluating the computed TPS model at all the image pixels is computationally expensive, we need to speed it up. We introduce the use of multi-level fast muitipole method (MLFMM) for this purpose. Our contribution lies in the presentation of a clear and concise MLFMM framework for TPS, which will be useful for future application developments. The achieved speedup using MLFMM is an improvement from O(N<sup>2</sup>) to O(N log N). We show that the fast evaluation outperforms the brute force method while maintaining acceptable error bound.},
	keywords = {(mathematics);, Computer, deformation;, evaluation;, fast, image, MATCHING, matching;, metal, method;, multilevel, multipole, nonrigid, pixel;, plate, plate;, processing;, registration;, resolution;, spline, splines, thin, vision;},
	doi = {10.1109/ICIP.2004.1421395},
	author = {Zandifar,A. and Lim,S. and Duraiswami, Ramani and Gumerov, Nail A. and Davis, Larry S.}
}
@article {13235,
	title = {Efficient kernel density estimation using the fast gauss transform with applications to color modeling and tracking},
	journal = {Pattern Analysis and Machine Intelligence, IEEE Transactions on},
	volume = {25},
	year = {2003},
	month = {2003/11//},
	pages = {1499 - 1504},
	abstract = {Many vision algorithms depend on the estimation of a probability density function from observations. Kernel density estimation techniques are quite general and powerful methods for this problem, but have a significant disadvantage in that they are computationally intensive. In this paper, we explore the use of kernel density estimation with the fast Gauss transform (FGT) for problems in vision. The FGT allows the summation of a mixture of ill Gaussians at N evaluation points in O(M+N) time, as opposed to O(MN) time for a naive evaluation and can be used to considerably speed up kernel density estimation. We present applications of the technique to problems from image segmentation and tracking and show that the algorithm allows application of advanced statistical techniques to solve practical vision problems in real-time with today{\textquoteright}s computers.},
	keywords = {algorithms;, Color, Computer, density, estimation;, fast, function;, Gauss, image, Kernel, modeling;, segmentation;, tracking;, transform;, transforms;, VISION, vision;},
	isbn = {0162-8828},
	doi = {10.1109/TPAMI.2003.1240123},
	author = {Elgammal,A. and Duraiswami, Ramani and Davis, Larry S.}
}
@conference {14052,
	title = {Three dimensional acoustic scattering from an M multilayered domain via an integral equation approach},
	booktitle = {Antennas and Propagation Society International Symposium, 2003. IEEE},
	volume = {1},
	year = {2003},
	month = {2003/06//},
	pages = {669 - 672 vol.1 - 669 - 672 vol.1},
	abstract = {An integral equation approach is derived for three dimensional acoustic scattering from an M multilayered domain. The integral equation is solved using the collocation method. To validate the method, we compare the exact and approximate results. The numerous matrix vector multiplications of our method may be thought a disadvantage. This problem can be overcome by using fast multiple methods where these operations are done very quickly.},
	keywords = {acoustic, collocation, dimensional, domain;, equation;, equations;, fast, inhomogeneous, integral, matrix, media;, method;, methods;, multilayered, multilayers;, multiple, multiplication;, multiplications;, scattering;, three, vector, wave},
	doi = {10.1109/APS.2003.1217547},
	author = {Seydou,F. and Duraiswami, Ramani and Seppanen,T.}
}
@conference {15577,
	title = {Algorithms for fast vector quantization},
	booktitle = {Data Compression Conference, 1993. DCC {\textquoteright}93.},
	year = {1993},
	month = {1993///},
	pages = {381 - 390},
	abstract = {This paper shows that if one is willing to relax the requirement of finding the true nearest neighbor, it is possible to achieve significant improvements in running time and at only a very small loss in the performance of the vector quantizer. The authors present three algorithms for nearest neighbor searching: standard and priority <e1>k </e1>-<e1>d</e1> tree search algorithms and a neighborhood graph search algorithm in which a directed graph is constructed for the point set and edges join neighboring points},
	keywords = {algorithm;, algorithms;, directed, fast, graph, graph;, graphs;, Neighborhood, performance;, problems;, quantisation;, quantization;, running, search, time;, tree, vector},
	doi = {10.1109/DCC.1993.253111},
	author = {Arya,S. and Mount, Dave}
}
