Results 1 - 10 of 66,164

Incentives Build Robustness in BitTorrent

by Bram Cohen , 2003
"... The BitTorrent file distribution system uses tit-for-tat as a method of seeking pareto efficiency. It achieves a higher level of robustness and resource utilization than any currently known cooperative technique. We explain what BitTorrent does, and how economic methods are used to achieve that goal ..."
Abstract - Cited by 1045 (1 self) - Add to MetaCart
The BitTorrent file distribution system uses tit-for-tat as a method of seeking pareto efficiency. It achieves a higher level of robustness and resource utilization than any currently known cooperative technique. We explain what BitTorrent does, and how economic methods are used to achieve

SURF: Speeded Up Robust Features

by Herbert Bay, Tinne Tuytelaars, Luc Van Gool - ECCV
"... Abstract. In this paper, we present a novel scale- and rotation-invariant interest point detector and descriptor, coined SURF (Speeded Up Ro-bust Features). It approximates or even outperforms previously proposed schemes with respect to repeatability, distinctiveness, and robustness, yet can be comp ..."
Abstract - Cited by 897 (12 self) - Add to MetaCart
Abstract. In this paper, we present a novel scale- and rotation-invariant interest point detector and descriptor, coined SURF (Speeded Up Ro-bust Features). It approximates or even outperforms previously proposed schemes with respect to repeatability, distinctiveness, and robustness, yet can

The R*-tree: an efficient and robust access method for points and rectangles

by Norbert Beckmann, Hans-Peter Kriegel, Ralf Schneider, Bernhard Seeger - INTERNATIONAL CONFERENCE ON MANAGEMENT OF DATA , 1990
"... The R-tree, one of the most popular access methods for rectangles, is based on the heuristic optimization of the area of the enclosing rectangle in each inner node. By running numerous experiments in a standardized testbed under highly varying data, queries and operations, we were able to design the ..."
Abstract - Cited by 1262 (74 self) - Add to MetaCart
The R-tree, one of the most popular access methods for rectangles, is based on the heuristic optimization of the area of the enclosing rectangle in each inner node. By running numerous experiments in a standardized testbed under highly varying data, queries and operations, we were able to design

Robust Real-time Object Detection

by Paul Viola, Michael Jones - International Journal of Computer Vision , 2001
"... This paper describes a visual object detection framework that is capable of processing images extremely rapidly while achieving high detection rates. There are three key contributions. The first is the introduction of a new image representation called the “Integral Image ” which allows the features ..."
Abstract - Cited by 1184 (4 self) - Add to MetaCart
used by our detector to be computed very quickly. The second is a learning algorithm, based on AdaBoost, which selects a small number of critical visual features and yields extremely efficient classifiers [6]. The third contribution is a method for combining classifiers in a “cascade ” which allows

Normalization for cDNA microarray data: a robust composite method addressing single and multiple slide systematic variation

by Yee Hwa Yang, Sandrine Dudoit, Percy Luu, Vivian Peng , 2002
"... There are many sources of systematic variation in cDNA microarray experiments which affect the measured gene expression levels (e.g. differences in labeling efficiency between the two fluorescent dyes). The term normalization refers to the process of removing such variation. A constant adjustment is ..."
Abstract - Cited by 718 (9 self) - Add to MetaCart
normalization methods that are based on robust local regression and account for intensity and spatial dependence in dye biases for different types of cDNA microarray experiments. The selection of appropriate controls for normalization is discussed and a novel set of controls (microarray sample pool, MSP

Fast and robust fixed-point algorithms for independent component analysis

by Aapo Hyvärinen - IEEE TRANS. NEURAL NETW , 1999
"... Independent component analysis (ICA) is a statistical method for transforming an observed multidimensional random vector into components that are statistically as independent from each other as possible. In this paper, we use a combination of two different approaches for linear ICA: Comon’s informat ..."
Abstract - Cited by 884 (34 self) - Add to MetaCart
Independent component analysis (ICA) is a statistical method for transforming an observed multidimensional random vector into components that are statistically as independent from each other as possible. In this paper, we use a combination of two different approaches for linear ICA: Comon’s

Quantization Index Modulation: A Class of Provably Good Methods for Digital Watermarking and Information Embedding

by Brian Chen, Gregory W. Wornell - IEEE TRANS. ON INFORMATION THEORY , 1999
"... We consider the problem of embedding one signal (e.g., a digital watermark), within another "host" signal to form a third, "composite" signal. The embedding is designed to achieve efficient tradeoffs among the three conflicting goals of maximizing information-embedding rate, mini ..."
Abstract - Cited by 496 (14 self) - Add to MetaCart
, minimizing distortion between the host signal and composite signal, and maximizing the robustness of the embedding. We introduce new classes of embedding methods, termed quantization index modulation (QIM) and distortion-compensated QIM (DC-QIM), and develop convenient realizations in the form of what we

A Re-Examination of Text Categorization Methods

by Yiming Yang, Xin Liu , 1999
"... This paper reports a controlled study with statistical significance tests on five text categorization methods: the Support Vector Machines (SVM), a k-Nearest Neighbor (kNN) classifier, a neural network (NNet) approach, the Linear Leastsquares Fit (LLSF) mapping and a NaiveBayes (NB) classifier. We f ..."
Abstract - Cited by 853 (24 self) - Add to MetaCart
focus on the robustness of these methods in dealing with a skewed category distribution, and their performance as function of the training-set category frequency. Our results show that SVM, kNN and LLSF significantly outperform NNet and NB when the number of positive training instances per category

A volumetric method for building complex models from range images,”

by Brian Curless , Marc Levoy - in Proceedings of the 23rd annual conference on Computer graphics and interactive techniques. ACM, , 1996
"... Abstract A number of techniques have been developed for reconstructing surfaces by integrating groups of aligned range images. A desirable set of properties for such algorithms includes: incremental updating, representation of directional uncertainty, the ability to fill gaps in the reconstruction, ..."
Abstract - Cited by 1020 (17 self) - Add to MetaCart
, and robustness in the presence of outliers. Prior algorithms possess subsets of these properties. In this paper, we present a volumetric method for integrating range images that possesses all of these properties. Our volumetric representation consists of a cumulative weighted signed distance function. Working

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
, and has recently been unified with the Metropolis algorithm to produce the "hybrid Monte Carlo" method. In computer science, Markov chain sampling is the basis of the heuristic optimization technique of "simulated annealing", and has recently been used in randomized algorithms
Results 1 - 10 of 66,164