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Computer Intensive Statistics APTS 2013/14 Preliminary Material

by unknown authors , 2014
"... apts.ac.ukAcademyforPhDTraininginStatistics ..."
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apts.ac.ukAcademyforPhDTraininginStatistics

Survey of clustering algorithms

by Rui Xu, Donald Wunsch II - IEEE TRANSACTIONS ON NEURAL NETWORKS , 2005
"... Data analysis plays an indispensable role for understanding various phenomena. Cluster analysis, primitive exploration with little or no prior knowledge, consists of research developed across a wide variety of communities. The diversity, on one hand, equips us with many tools. On the other hand, the ..."
Abstract - Cited by 499 (4 self) - Add to MetaCart
, the profusion of options causes confusion. We survey clustering algorithms for data sets appearing in statistics, computer science, and machine learning, and illustrate their applications in some benchmark data sets, the traveling salesman problem, and bioinformatics, a new field attracting intensive efforts

Multimodality Image Registration by Maximization of Mutual Information

by Frederik Maes, André Collignon, Dirk Vandermeulen, Guy Marchal, Paul Suetens - IEEE TRANSACTIONS ON MEDICAL IMAGING , 1997
"... A new approach to the problem of multimodality medical image registration is proposed, using a basic concept from information theory, mutual information (MI), or relative entropy, as a new matching criterion. The method presented in this paper applies MI to measure the statistical dependence or in ..."
Abstract - Cited by 791 (10 self) - Add to MetaCart
A new approach to the problem of multimodality medical image registration is proposed, using a basic concept from information theory, mutual information (MI), or relative entropy, as a new matching criterion. The method presented in this paper applies MI to measure the statistical dependence

Fast Bilateral Filtering for the Display of High-Dynamic-Range Images

by Frédo Durand, Julie Dorsey , 2002
"... We present a new technique for the display of high-dynamic-range images, which reduces the contrast while preserving detail. It is based on a two-scale decomposition of the image into a base layer, encoding large-scale variations, and a detail layer. Only the base layer has its contrast reduced, the ..."
Abstract - Cited by 453 (10 self) - Add to MetaCart
, thereby preserving detail. The base layer is obtained using an edge-preserving filter called the bilateral filter. This is a non-linear filter, where the weight of each pixel is computed using a Gaussian in the spatial domain multiplied by an influence function in the intensity domain that decreases

On the Unification Line Processes, Outlier Rejection, and Robust Statistics with Applications in Early Vision

by Michael J. Black, Anand Rangarajan , 1996
"... The modeling of spatial discontinuities for problems such as surface recovery, segmentation, image reconstruction, and optical flow has been intensely studied in computer vision. While "line-process" models of discontinuities have received a great deal of attention, there has been recent ..."
Abstract - Cited by 271 (8 self) - Add to MetaCart
The modeling of spatial discontinuities for problems such as surface recovery, segmentation, image reconstruction, and optical flow has been intensely studied in computer vision. While "line-process" models of discontinuities have received a great deal of attention, there has been recent

Raxml-iii: a fast program for maximum likelihood-based inference of large phylogenetic trees

by A. Stamatakis, T. Ludwig, H. Meier - Bioinformatics , 2005
"... Motivation: The computation of large phylogenetic trees with statistical models such as maximum likelihood or bayesian inference is computationally extremely intensive. It has repeatedly been demonstrated that these models are able to recover the true tree or a tree which is topologically closer to ..."
Abstract - Cited by 259 (17 self) - Add to MetaCart
Motivation: The computation of large phylogenetic trees with statistical models such as maximum likelihood or bayesian inference is computationally extremely intensive. It has repeatedly been demonstrated that these models are able to recover the true tree or a tree which is topologically closer

Robust Fragments-based Tracking using the Integral Histogram

by Amit Adam, Ehud Rivlin, Ilan Shimshoni - In IEEE Conf. Computer Vision and Pattern Recognition (CVPR , 2006
"... We present a novel algorithm (which we call “Frag-Track”) for tracking an object in a video sequence. The template object is represented by multiple image fragments or patches. The patches are arbitrary and are not based on an object model (in contrast with traditional use of modelbased parts e.g. l ..."
Abstract - Cited by 224 (0 self) - Add to MetaCart
.g. limbs and torso in human tracking). Every patch votes on the possible positions and scales of the object in the current frame, by comparing its histogram with the corresponding image patch histogram. We then minimize a robust statistic in order to combine the vote maps of the multiple patches. A key

APTS 2013/14: de-classifying spatial statistics

by Peter J Diggle, X Coord
"... combining health information, computation and statistics CHICAS Spatial statistics according to Cressie ..."
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combining health information, computation and statistics CHICAS Spatial statistics according to Cressie

A fast approximation of the bilateral filter using a signal processing approach

by Sylvain Paris, Frédo Durand - In Proceedings of the European Conference on Computer Vision , 2006
"... The bilateral filter is a nonlinear filter that smoothes a signal while preserving strong edges. It has demonstrated great effectiveness for a variety of problems in computer vision and computer graphics, and fast versions have been proposed. Unfortunately, little is known about the accuracy of such ..."
Abstract - Cited by 179 (7 self) - Add to MetaCart
of such accelerations. In this paper, we propose a new signal-processing analysis of the bilateral filter which complements the recent studies that analyzed it as a PDE or as a robust statistical estimator. The key to our analysis is to express the filter in a higher-dimensional space where the signal intensity

Computer-Intensive Statistics

by B. D. Ripley - APTS 2012–13 LECTURE MATERIAL , 2012
"... ‘Computer-intensive statistics’ is statistics that could only be done with ‘modern‘ computing resources, typically either • Statistical inference on small problems which needs a lot of computation to do at all, or to do well. Quite small datasets can need complex models to explain, and even simple m ..."
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‘Computer-intensive statistics’ is statistics that could only be done with ‘modern‘ computing resources, typically either • Statistical inference on small problems which needs a lot of computation to do at all, or to do well. Quite small datasets can need complex models to explain, and even simple
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