Results 1 - 10 of 1,173,346

A non-local algorithm for image denoising

by Antoni Buades, Bartomeu Coll - In CVPR , 2005
"... We propose a new measure, the method noise, to evaluate and compare the performance of digital image denoising methods. We first compute and analyze this method noise for a wide class of denoising algorithms, namely the local smoothing filters. Second, we propose a new algorithm, the non local means ..."
Abstract - Cited by 433 (12 self) - Add to MetaCart
We propose a new measure, the method noise, to evaluate and compare the performance of digital image denoising methods. We first compute and analyze this method noise for a wide class of denoising algorithms, namely the local smoothing filters. Second, we propose a new algorithm, the non local

Winnowing: Local Algorithms for Document Fingerprinting

by Saul Schleimer - Proceedings of the 2003 ACM SIGMOD International Conference on Management of Data 2003 , 2003
"... Digital content is for copying: quotation, revision, plagiarism, and file sharing all create copies. Document fingerprinting is concerned with accurately identifying copying, including small partial copies, within large sets of documents. We introduce the class of local document fingerprinting algor ..."
Abstract - Cited by 257 (6 self) - Add to MetaCart
algorithms, which seems to capture an essential property of any fingerprinting technique guaranteed to detect copies. We prove a novel lower bound on the performance of any local algorithm. We also develop winnowing, an efficient local fingerprinting algorithm, and show that winnowing’s performance is within

A data locality optimizing algorithm

by Michael E. Wolf, Monica S. Lam , 1991
"... 1 Introduction As processor speed continues to increase faster than me-mory speed, optimizations to use the memory hierarchy efficiently become ever more important. Blocking [9] ortiling [18] is a well-known technique that improves the data locality of numerical algorithms [1, 6, 7, 12, 13].Tiling c ..."
Abstract - Cited by 805 (16 self) - Add to MetaCart
1 Introduction As processor speed continues to increase faster than me-mory speed, optimizations to use the memory hierarchy efficiently become ever more important. Blocking [9] ortiling [18] is a well-known technique that improves the data locality of numerical algorithms [1, 6, 7, 12, 13].Tiling

Robust Monte Carlo Localization for Mobile Robots

by Sebastian Thrun, Dieter Fox, Wolfram Burgard, Frank Dellaert , 2001
"... Mobile robot localization is the problem of determining a robot's pose from sensor data. This article presents a family of probabilistic localization algorithms known as Monte Carlo Localization (MCL). MCL algorithms represent a robot's belief by a set of weighted hypotheses (samples), whi ..."
Abstract - Cited by 826 (88 self) - Add to MetaCart
Mobile robot localization is the problem of determining a robot's pose from sensor data. This article presents a family of probabilistic localization algorithms known as Monte Carlo Localization (MCL). MCL algorithms represent a robot's belief by a set of weighted hypotheses (samples

Randomized Algorithms

by Rajeev Motwani , 1995
"... Randomized algorithms, once viewed as a tool in computational number theory, have by now found widespread application. Growth has been fueled by the two major benefits of randomization: simplicity and speed. For many applications a randomized algorithm is the fastest algorithm available, or the simp ..."
Abstract - Cited by 2210 (37 self) - Add to MetaCart
Randomized algorithms, once viewed as a tool in computational number theory, have by now found widespread application. Growth has been fueled by the two major benefits of randomization: simplicity and speed. For many applications a randomized algorithm is the fastest algorithm available

Planning Algorithms

by Steven M LaValle , 2004
"... This book presents a unified treatment of many different kinds of planning algorithms. The subject lies at the crossroads between robotics, control theory, artificial intelligence, algorithms, and computer graphics. The particular subjects covered include motion planning, discrete planning, planning ..."
Abstract - Cited by 1108 (51 self) - Add to MetaCart
This book presents a unified treatment of many different kinds of planning algorithms. The subject lies at the crossroads between robotics, control theory, artificial intelligence, algorithms, and computer graphics. The particular subjects covered include motion planning, discrete planning

Learning with local and global consistency

by Dengyong Zhou, Olivier Bousquet, Thomas Navin Lal, Jason Weston, Bernhard Schölkopf - Advances in Neural Information Processing Systems 16 , 2004
"... We consider the general problem of learning from labeled and unlabeled data, which is often called semi-supervised learning or transductive inference. A principled approach to semi-supervised learning is to design a classifying function which is sufficiently smooth with respect to the intrinsic stru ..."
Abstract - Cited by 666 (21 self) - Add to MetaCart
structure collectively revealed by known labeled and unlabeled points. We present a simple algorithm to obtain such a smooth solution. Our method yields encouraging experimental results on a number of classification problems and demonstrates effective use of unlabeled data. 1

Next century challenges: Scalable coordination in sensor networks

by Deborah Estrin, Ramesh Govindan, John Heidemann , 1999
"... Networked sensors-those that coordinate amongst them-selves to achieve a larger sensing task-will revolutionize information gathering and processing both in urban envi-ronments and in inhospitable terrain. The sheer numbers of these sensors and the expected dynamics in these environ-ments present un ..."
Abstract - Cited by 1103 (41 self) - Add to MetaCart
unique challenges in the design of unattended autonomous sensor networks. These challenges lead us to hypothesize that sensor network coordination applications may need to be structured differently from traditional net-work applications. In particular, we believe that localized algorithms (in which

Instance-based learning algorithms

by David W. Aha, Dennis Kibler, Marc K. Albert - Machine Learning , 1991
"... Abstract. Storing and using specific instances improves the performance of several supervised learning algorithms. These include algorithms that learn decision trees, classification rules, and distributed networks. However, no investigation has analyzed algorithms that use only specific instances to ..."
Abstract - Cited by 1359 (18 self) - Add to MetaCart
Abstract. Storing and using specific instances improves the performance of several supervised learning algorithms. These include algorithms that learn decision trees, classification rules, and distributed networks. However, no investigation has analyzed algorithms that use only specific instances

On Spectral Clustering: Analysis and an algorithm

by Andrew Y. Ng, Michael I. Jordan, Yair Weiss - ADVANCES IN NEURAL INFORMATION PROCESSING SYSTEMS , 2001
"... Despite many empirical successes of spectral clustering methods -- algorithms that cluster points using eigenvectors of matrices derived from the distances between the points -- there are several unresolved issues. First, there is a wide variety of algorithms that use the eigenvectors in slightly ..."
Abstract - Cited by 1697 (13 self) - Add to MetaCart
Despite many empirical successes of spectral clustering methods -- algorithms that cluster points using eigenvectors of matrices derived from the distances between the points -- there are several unresolved issues. First, there is a wide variety of algorithms that use the eigenvectors
Results 1 - 10 of 1,173,346