Results 1 - 10 of 86,411

Continuity of Varying-Feature-Set Control Laws

by Nicolas Mansard, Anthony Remazeilles, François Chaumette - ACCEPTED FOR PUBLICATION IN IEEE TRANSACTIONS ON AUTOMATIC CONTROL
"... Classical sensor-based control laws are based on the regulation of a set of features to a desired reference value. In this paper, we focus on the study of control laws whose feature set varies during the servo. In that case, we first show that the classical control laws that use an iterative leastsq ..."
Abstract - Cited by 16 (6 self) - Add to MetaCart
Classical sensor-based control laws are based on the regulation of a set of features to a desired reference value. In this paper, we focus on the study of control laws whose feature set varies during the servo. In that case, we first show that the classical control laws that use an iterative

Continuity of Varying-Feature-Set Control Laws

by Èmes Al, Nicolas Mansard, Anthony Remazeilles, Nicolas Mansard, Anthony Remazeilles, François Chaumette, Systèmes Cognitifs, Projet Lagadic , 1864
"... Classical sensor-based control laws are based on the regulation of a set of sensor-based features to a desired reference value. The feature set is generally constant. In this article, we focus on the study of sensor-based control laws whose feature set varies during the servo. In that case, we first ..."
Abstract - Add to MetaCart
Classical sensor-based control laws are based on the regulation of a set of sensor-based features to a desired reference value. The feature set is generally constant. In this article, we focus on the study of sensor-based control laws whose feature set varies during the servo. In that case, we

The pyramid match kernel: Discriminative classification with sets of image features

by Kristen Grauman, Trevor Darrell - IN ICCV , 2005
"... Discriminative learning is challenging when examples are sets of features, and the sets vary in cardinality and lack any sort of meaningful ordering. Kernel-based classification methods can learn complex decision boundaries, but a kernel over unordered set inputs must somehow solve for correspondenc ..."
Abstract - Cited by 544 (29 self) - Add to MetaCart
Discriminative learning is challenging when examples are sets of features, and the sets vary in cardinality and lack any sort of meaningful ordering. Kernel-based classification methods can learn complex decision boundaries, but a kernel over unordered set inputs must somehow solve

An extended set of Haar-like features for rapid objection detection

by Rainer Lienhart, Jochen Maydt - IEEE ICIP
"... Recently Viola et al. [5] have introduced a rapid object detection scheme based on a boosted cascade of simple feature classifiers. In this paper we introduce a novel set of rotated haar-like features. These novel features significantly enrich the simple features of [5] and can also be calculated ef ..."
Abstract - Cited by 577 (4 self) - Add to MetaCart
Recently Viola et al. [5] have introduced a rapid object detection scheme based on a boosted cascade of simple feature classifiers. In this paper we introduce a novel set of rotated haar-like features. These novel features significantly enrich the simple features of [5] and can also be calculated

Wrappers for Feature Subset Selection

by Ron Kohavi, George H. John - AIJ SPECIAL ISSUE ON RELEVANCE , 1997
"... In the feature subset selection problem, a learning algorithm is faced with the problem of selecting a relevant subset of features upon which to focus its attention, while ignoring the rest. To achieve the best possible performance with a particular learning algorithm on a particular training set, a ..."
Abstract - Cited by 1569 (3 self) - Add to MetaCart
In the feature subset selection problem, a learning algorithm is faced with the problem of selecting a relevant subset of features upon which to focus its attention, while ignoring the rest. To achieve the best possible performance with a particular learning algorithm on a particular training set

Inducing Features of Random Fields

by Stephen Della Pietra, Vincent Della Pietra, John Lafferty - IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE , 1997
"... We present a technique for constructing random fields from a set of training samples. The learning paradigm builds increasingly complex fields by allowing potential functions, or features, that are supported by increasingly large subgraphs. Each feature has a weight that is trained by minimizing the ..."
Abstract - Cited by 670 (10 self) - Add to MetaCart
We present a technique for constructing random fields from a set of training samples. The learning paradigm builds increasingly complex fields by allowing potential functions, or features, that are supported by increasingly large subgraphs. Each feature has a weight that is trained by minimizing

Face recognition: features versus templates

by Roberto Brunelli, Tomaso Poggio - IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE , 1993
"... Over the last 20 years, several different techniques have been proposed for computer recognition of human faces. The purpose of this paper is to compare two simple but general strategies on a common database (frontal images of faces of 47 people: 26 males and 21 females, four images per person). We ..."
Abstract - Cited by 749 (25 self) - Add to MetaCart
). We have developed and implemented two new algorithms; the first one is based on the computation of a set of geometrical features, such as nose width and length, mouth position, and chin shape, and the second one is based on almost-grey-level template matching. The results obtained on the testing sets

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

Selection of relevant features and examples in machine learning

by Avrim L. Blum, Pat Langley - ARTIFICIAL INTELLIGENCE , 1997
"... In this survey, we review work in machine learning on methods for handling data sets containing large amounts of irrelevant information. We focus on two key issues: the problem of selecting relevant features, and the problem of selecting relevant examples. We describe the advances that have been mad ..."
Abstract - Cited by 606 (2 self) - Add to MetaCart
In this survey, we review work in machine learning on methods for handling data sets containing large amounts of irrelevant information. We focus on two key issues: the problem of selecting relevant features, and the problem of selecting relevant examples. We describe the advances that have been

Toward optimal feature selection

by Daphne Koller, Mehran Sahami - In 13th International Conference on Machine Learning , 1995
"... In this paper, we examine a method for feature subset selection based on Information Theory. Initially, a framework for de ning the theoretically optimal, but computationally intractable, method for feature subset selection is presented. We show that our goal should be to eliminate a feature if it g ..."
Abstract - Cited by 480 (9 self) - Add to MetaCart
. The conditions under which the approximate algorithm is successful are examined. Empirical results are given on a number of data sets, showing that the algorithm e ectively handles datasets with a very large number of features.
Results 1 - 10 of 86,411