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HUMAN POSE TRACKING IN LOW DIMENSIONAL SPACE ENHANCED BY LIMB CORRECTION

by Ros Moutzouris, Jesus Martinez-del-rincon, Michal Lew, Jean-christophe Nebel, Dimitrios Makris
"... This paper proposes a two-level 3D human pose tracking method for a specific action captured by several cameras. The generation of pose estimates relies on fitting a 3D articulated model on a Visual Hull generated from the input images. First, an initial pose estimate is constrained by a low dimensi ..."
Abstract - Cited by 1 (1 self) - Add to MetaCart
This paper proposes a two-level 3D human pose tracking method for a specific action captured by several cameras. The generation of pose estimates relies on fitting a 3D articulated model on a Visual Hull generated from the input images. First, an initial pose estimate is constrained by a low

Actions as space-time shapes

by Lena Gorelick, Moshe Blank, Eli Shechtman, Michal Irani, Ronen Basri - IN ICCV , 2005
"... Human action in video sequences can be seen as silhouettes of a moving torso and protruding limbs undergoing articulated motion. We regard human actions as three-dimensional shapes induced by the silhouettes in the space-time volume. We adopt a recent approach [14] for analyzing 2D shapes and genera ..."
Abstract - Cited by 651 (4 self) - Add to MetaCart
Human action in video sequences can be seen as silhouettes of a moving torso and protruding limbs undergoing articulated motion. We regard human actions as three-dimensional shapes induced by the silhouettes in the space-time volume. We adopt a recent approach [14] for analyzing 2D shapes

Laplacian Eigenmaps for Dimensionality Reduction and Data Representation

by Mikhail Belkin, Partha Niyogi , 2003
"... One of the central problems in machine learning and pattern recognition is to develop appropriate representations for complex data. We consider the problem of constructing a representation for data lying on a low-dimensional manifold embedded in a high-dimensional space. Drawing on the correspondenc ..."
Abstract - Cited by 1226 (15 self) - Add to MetaCart
One of the central problems in machine learning and pattern recognition is to develop appropriate representations for complex data. We consider the problem of constructing a representation for data lying on a low-dimensional manifold embedded in a high-dimensional space. Drawing

The geometry of graphs and some of its algorithmic applications

by Nathan Linial, Eran London, Yuri Rabinovich - COMBINATORICA , 1995
"... In this paper we explore some implications of viewing graphs as geometric objects. This approach offers a new perspective on a number of graph-theoretic and algorithmic problems. There are several ways to model graphs geometrically and our main concern here is with geometric representations that res ..."
Abstract - Cited by 524 (19 self) - Add to MetaCart
-tensions. 0 For graphs embeddable in low-dimensional spaces with a small distortion, we can find low-diameter decompositions (in the sense of [4] and [34]). The parameters of the decomposition depend only on the dimension and the distortion and not on the size of the graph. 0 In graphs embedded this way

The particel swarm: Explosion, stability, and convergence in a multi-dimensional complex space

by Maurice Clerc, James Kennedy - IEEE TRANSACTIONS ON EVOLUTIONARY COMPUTION
"... The particle swarm is an algorithm for finding optimal regions of complex search spaces through interaction of individuals in a population of particles. Though the algorithm, which is based on a metaphor of social interaction, has been shown to perform well, researchers have not adequately explained ..."
Abstract - Cited by 852 (10 self) - Add to MetaCart
The particle swarm is an algorithm for finding optimal regions of complex search spaces through interaction of individuals in a population of particles. Though the algorithm, which is based on a metaphor of social interaction, has been shown to perform well, researchers have not adequately

Probabilistic Roadmaps for Path Planning in High-Dimensional Configuration Spaces

by Lydia Kavraki, Petr Svestka, Jean-claude Latombe, Mark Overmars - IEEE INTERNATIONAL CONFERENCE ON ROBOTICS AND AUTOMATION , 1996
"... A new motion planning method for robots in static workspaces is presented. This method proceeds in two phases: a learning phase and a query phase. In the learning phase, a probabilistic roadmap is constructed and stored as a graph whose nodes correspond to collision-free configurations and whose edg ..."
Abstract - Cited by 1277 (120 self) - Add to MetaCart
A new motion planning method for robots in static workspaces is presented. This method proceeds in two phases: a learning phase and a query phase. In the learning phase, a probabilistic roadmap is constructed and stored as a graph whose nodes correspond to collision-free configurations and whose edges correspond to feasible paths between these configurations. These paths are computed using a simple and fast local planner. In the query phase, any given start and goal configurations of the robot are connected to two nodes of the roadmap; the roadmap is then searched for a path joining these two nodes. The method is general and easy to implement. It can be applied to virtually any type of holonomic robot. It requires selecting certain parameters (e.g., the duration of the learning phase) whose values depend on the scene, that is the robot and its workspace. But these values turn out to be relatively easy to choose, Increased efficiency can also be achieved by tailoring some components of the method (e.g., the local planner) to the considered robots. In this paper the method is applied to planar articulated robots with many degrees of freedom. Experimental results show that path planning can be done in a fraction of a second on a contemporary workstation (=150 MIPS), after learning for relatively short periods of time (a few dozen seconds)

Functional Connectivity among Spikes in Low Dimensional Space during Working Memory Task in Rat

by Mei Ouyang, Shuangyan Li, Xin Tian , 2014
"... Working memory (WM) is critically important in cognitive tasks. The functional connectivity has been a powerful tool for understanding the mechanism underlying the information processing during WM tasks. The aim of this study is to investigate how to effectively characterize the dynamic variations o ..."
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of the functional connectivity in low dimensional space among the principal components (PCs) which were extracted from the instantaneous firing rate series. Spikes were obtained from medial prefrontal cortex (mPFC) of rats with implanted microelectrode array and then transformed into continuous series via

A Synthetic Minority Oversampling Method Based on Local Densities in Low-Dimensional Space for Imbalanced Learning

by Zhipeng Xie, Liyang Jiang, Tengju Ye, Xiao-li Li
"... Abstract. Imbalanced class distribution is a challenging problem in many real-life classification problems. Existing synthetic oversampling do suffer from the curse of dimensionality because they rely heavily on Euclidean distance. This paper proposed a new method, called Minority Oversampling Techn ..."
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Technique based on Local Densities in Low-Dimensional Space (or MOT2LD in short). MOT2LD first maps each training sample into a low-dimensional space, and makes clus-tering of their low-dimensional representations. It then assigns weight to each minority sample as the product of two quantities: local

Estimating the Support of a High-Dimensional Distribution

by Bernhard Schölkopf, John C. Platt, John Shawe-taylor, Alex J. Smola, Robert C. Williamson , 1999
"... Suppose you are given some dataset drawn from an underlying probability distribution P and you want to estimate a "simple" subset S of input space such that the probability that a test point drawn from P lies outside of S is bounded by some a priori specified between 0 and 1. We propo ..."
Abstract - Cited by 783 (29 self) - Add to MetaCart
Suppose you are given some dataset drawn from an underlying probability distribution P and you want to estimate a "simple" subset S of input space such that the probability that a test point drawn from P lies outside of S is bounded by some a priori specified between 0 and 1. We

The X-tree: An index structure for high-dimensional data

by Stefan Berchtold, Daniel A. Keim, Hans-peter Kriegel - In Proceedings of the Int’l Conference on Very Large Data Bases , 1996
"... In this paper, we propose a new method for index-ing large amounts of point and spatial data in high-dimensional space. An analysis shows that index structures such as the R*-tree are not adequate for indexing high-dimensional data sets. The major problem of R-tree-based index structures is the over ..."
Abstract - Cited by 592 (17 self) - Add to MetaCart
In this paper, we propose a new method for index-ing large amounts of point and spatial data in high-dimensional space. An analysis shows that index structures such as the R*-tree are not adequate for indexing high-dimensional data sets. The major problem of R-tree-based index structures
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