Results 1 - 10 of 122,622

Real-Time Tracking of Non-Rigid Objects using Mean Shift

by Dorin Comaniciu, Visvanathan Ramesh, Peter Meer - IEEE CVPR 2000 , 2000
"... A new method for real-time tracking of non-rigid objects seen from a moving camera isproposed. The central computational module is based on the mean shift iterations and nds the most probable target position in the current frame. The dissimilarity between the target model (its color distribution) an ..."
Abstract - Cited by 815 (19 self) - Add to MetaCart
A new method for real-time tracking of non-rigid objects seen from a moving camera isproposed. The central computational module is based on the mean shift iterations and nds the most probable target position in the current frame. The dissimilarity between the target model (its color distribution

Constrained K-means Clustering with Background Knowledge

by Kiri Wagstaff, Claire Cardie, Seth Rogers, Stefan Schroedl - In ICML , 2001
"... Clustering is traditionally viewed as an unsupervised method for data analysis. However, in some cases information about the problem domain is available in addition to the data instances themselves. In this paper, we demonstrate how the popular k-means clustering algorithm can be pro tably modi- ed ..."
Abstract - Cited by 488 (9 self) - Add to MetaCart
Clustering is traditionally viewed as an unsupervised method for data analysis. However, in some cases information about the problem domain is available in addition to the data instances themselves. In this paper, we demonstrate how the popular k-means clustering algorithm can be pro tably modi- ed

K-means++: The advantages of careful seeding.

by David Arthur , Sergei Vassilvitskii - In Proceedings of the Eighteenth Annual ACM-SIAM Symposium on Discrete Algorithms, SODA ’07, , 2007
"... Abstract The k-means method is a widely used clustering technique that seeks to minimize the average squared distance between points in the same cluster. Although it offers no accuracy guarantees, its simplicity and speed are very appealing in practice. By augmenting k-means with a very simple, ran ..."
Abstract - Cited by 478 (8 self) - Add to MetaCart
Abstract The k-means method is a widely used clustering technique that seeks to minimize the average squared distance between points in the same cluster. Although it offers no accuracy guarantees, its simplicity and speed are very appealing in practice. By augmenting k-means with a very simple

Learning to predict by the methods of temporal differences

by Richard S. Sutton - MACHINE LEARNING , 1988
"... This article introduces a class of incremental learning procedures specialized for prediction – that is, for using past experience with an incompletely known system to predict its future behavior. Whereas conventional prediction-learning methods assign credit by means of the difference between predi ..."
Abstract - Cited by 1521 (56 self) - Add to MetaCart
This article introduces a class of incremental learning procedures specialized for prediction – that is, for using past experience with an incompletely known system to predict its future behavior. Whereas conventional prediction-learning methods assign credit by means of the difference between

Some methods for classification and analysis of multivariate observations

by J. Macqueen - In 5-th Berkeley Symposium on Mathematical Statistics and Probability , 1967
"... The main purpose of this paper is to describe a process for partitioning an N-dimensional population into k sets on the basis of a sample. The process, which is called 'k-means, ' appears to give partitions which are reasonably ..."
Abstract - Cited by 3055 (3 self) - Add to MetaCart
The main purpose of this paper is to describe a process for partitioning an N-dimensional population into k sets on the basis of a sample. The process, which is called 'k-means, ' appears to give partitions which are reasonably

ATOMIC DECOMPOSITION BY BASIS PURSUIT

by Scott Shaobing Chen , David L. Donoho , Michael A. Saunders , 1995
"... The Time-Frequency and Time-Scale communities have recently developed a large number of overcomplete waveform dictionaries -- stationary wavelets, wavelet packets, cosine packets, chirplets, and warplets, to name a few. Decomposition into overcomplete systems is not unique, and several methods for d ..."
Abstract - Cited by 2728 (61 self) - Add to MetaCart
for decomposition have been proposed, including the Method of Frames (MOF), Matching Pursuit (MP), and, for special dictionaries, the Best Orthogonal Basis (BOB). Basis Pursuit (BP) is a principle for decomposing a signal into an "optimal" superposition of dictionary elements, where optimal means having

A method for obtaining digital signatures and public-key cryptosystems.

by R L Rivest , A Shamir , L Adleman - Communications of the ACM, , 1978
"... Abstract An encryption method is presented with the novel property that publicly revealing an encryption key does not thereby reveal the corresponding decryption key. This has two important consequences: 1. Couriers or other secure means are not needed to transmit keys, since a message can be encip ..."
Abstract - Cited by 3894 (24 self) - Add to MetaCart
Abstract An encryption method is presented with the novel property that publicly revealing an encryption key does not thereby reveal the corresponding decryption key. This has two important consequences: 1. Couriers or other secure means are not needed to transmit keys, since a message can

Probabilistic Inference Using Markov Chain Monte Carlo Methods

by Radford M. Neal , 1993
"... Probabilistic inference is an attractive approach to uncertain reasoning and empirical learning in artificial intelligence. Computational difficulties arise, however, because probabilistic models with the necessary realism and flexibility lead to complex distributions over high-dimensional spaces. R ..."
Abstract - Cited by 736 (24 self) - Add to MetaCart
physics for over forty years, and, in the last few years, the related method of "Gibbs sampling" has been applied to problems of statistical inference. Concurrently, an alternative method for solving problems in statistical physics by means of dynamical simulation has been developed as well

Maximum Likelihood Phylogenetic Estimation from DNA Sequences with Variable Rates over Sites: Approximate Methods

by Ziheng Yang - J. Mol. Evol , 1994
"... Two approximate methods are proposed for maximum likelihood phylogenetic estimation, which allow variable rates of substitution across nucleotide sites. Three data sets with quite different characteristics were analyzed to examine empirically the performance of these methods. The first, called ..."
Abstract - Cited by 557 (29 self) - Add to MetaCart
the "discrete gamma model," uses several categories of rates to approximate the gamma distribution, with equal probability for each category. The mean of each category is used to represent all the rates falling in the category. The performance of this method is found to be quite good

Inverse Acoustic and Electromagnetic Scattering Theory, Second Edition

by David Colton , 1998
"... Abstract. This paper is a survey of the inverse scattering problem for time-harmonic acoustic and electromagnetic waves at fixed frequency. We begin by a discussion of “weak scattering ” and Newton-type methods for solving the inverse scattering problem for acoustic waves, including a brief discussi ..."
Abstract - Cited by 1061 (45 self) - Add to MetaCart
Abstract. This paper is a survey of the inverse scattering problem for time-harmonic acoustic and electromagnetic waves at fixed frequency. We begin by a discussion of “weak scattering ” and Newton-type methods for solving the inverse scattering problem for acoustic waves, including a brief
Results 1 - 10 of 122,622