• Documents
  • Authors
  • Tables
  • Log in
  • Sign up
  • MetaCart
  • DMCA
  • Donate

CiteSeerX logo

Advanced Search Include Citations

Tools

Sorted by:
Try your query at:
Semantic Scholar Scholar Academic
Google Bing DBLP
Results 1 - 10 of 92,952
Next 10 →

A Study of MAP Estimation Techniques for Nonlinear Filtering

by Maryam Fatemi, Lennart Svensson, Lars Hammarstr, Mark Morelande
"... This document has been downloaded from Chalmers Publication Library (CPL). It is the author´s version of a work that was accepted for publication in: ..."
Abstract - Add to MetaCart
This document has been downloaded from Chalmers Publication Library (CPL). It is the author´s version of a work that was accepted for publication in:

The Use of MAP Estimation Techniques in the Tomographic Reconstruction of Poisson Noise Corrupted Images

by Nelson D. A Mascarenhas, Saulo S. L. Santos, Paulo E. Cruvinel, Cruvi Cruvinel, Cesareo R, Crestana R, Mascarenhas S. X , 1996
"... The problem of tomographic image reconstruction is important in many areas of applied science and technology. This work presents new methods for the tomographic reconstruction of images with Poisson noise corrupted projections. The Poisson noise comes from the discrete nature of radiation that chara ..."
Abstract - Cited by 1 (1 self) - Add to MetaCart
the filtering of simulated noisy projections and the reconstruction of simulated and real phantoms scanned with a minitomograph scanner for soil science. The obtained results indicate that, by using the MAP criterion, it is possible to obtain a lower mean square error in reconstruction, as compared

Similarity estimation techniques from rounding algorithms

by Moses S. Charikar - In Proc. of 34th STOC , 2002
"... A locality sensitive hashing scheme is a distribution on a family F of hash functions operating on a collection of objects, such that for two objects x, y, Prh∈F[h(x) = h(y)] = sim(x,y), where sim(x,y) ∈ [0, 1] is some similarity function defined on the collection of objects. Such a scheme leads ..."
Abstract - Cited by 449 (6 self) - Add to MetaCart
to a compact representation of objects so that similarity of objects can be estimated from their compact sketches, and also leads to efficient algorithms for approximate nearest neighbor search and clustering. Min-wise independent permutations provide an elegant construction of such a locality

Laplacian eigenmaps and spectral techniques for embedding and clustering.

by Mikhail Belkin , Partha Niyogi - Proceeding of Neural Information Processing Systems, , 2001
"... Abstract Drawing on the correspondence between the graph Laplacian, the Laplace-Beltrami op erator on a manifold , and the connections to the heat equation , we propose a geometrically motivated algorithm for constructing a representation for data sampled from a low dimensional manifold embedded in ..."
Abstract - Cited by 668 (7 self) - Add to MetaCart
comes from the role of the Laplacian op erator in providing an optimal emb edding. Th e Laplacian of the graph obtained from the data points may be viewed as an approximation to the Laplace-Beltrami operator defined on the manifold. The emb edding maps for the data come from approximations to a natural

A solution to the simultaneous localization and map building (SLAM) problem

by M. W. M. Gamini Dissanayake, Paul Newman, Steven Clark, Hugh F. Durrant-whyte, M. Csorba - IEEE Transactions on Robotics and Automation , 2001
"... Abstract—The simultaneous localization and map building (SLAM) problem asks if it is possible for an autonomous vehicle to start in an unknown location in an unknown environment and then to incrementally build a map of this environment while simultaneously using this map to compute absolute vehicle ..."
Abstract - Cited by 505 (30 self) - Add to MetaCart
location. Starting from the estimation-theoretic foundations of this problem developed in [1]–[3], this paper proves that a solution to the SLAM problem is indeed possible. The underlying structure of the SLAM problem is first elucidated. A proof that the estimated map converges monotonically to a relative

Tracking People with Twists and Exponential Maps

by Christoph Bregler, Jitendra Malik , 1998
"... This paper demonstrates a new visual motion estimation technique that is able to recover high degree-of-freedom articulated human body configurations in complex video sequences. We introduce the use of a novel mathematical technique, the product of exponential maps and twist motions, and its integra ..."
Abstract - Cited by 450 (5 self) - Add to MetaCart
This paper demonstrates a new visual motion estimation technique that is able to recover high degree-of-freedom articulated human body configurations in complex video sequences. We introduce the use of a novel mathematical technique, the product of exponential maps and twist motions, and its

FastSLAM: A Factored Solution to the Simultaneous Localization and Mapping Problem

by Michael Montemerlo, Sebastian Thrun, Daphne Koller, Ben Wegbreit - In Proceedings of the AAAI National Conference on Artificial Intelligence , 2002
"... The ability to simultaneously localize a robot and accurately map its surroundings is considered by many to be a key prerequisite of truly autonomous robots. However, few approaches to this problem scale up to handle the very large number of landmarks present in real environments. Kalman filter-base ..."
Abstract - Cited by 599 (10 self) - Add to MetaCart
The ability to simultaneously localize a robot and accurately map its surroundings is considered by many to be a key prerequisite of truly autonomous robots. However, few approaches to this problem scale up to handle the very large number of landmarks present in real environments. Kalman filter

Estimating the number of clusters in a dataset via the Gap statistic

by Robert Tibshirani, Guenther Walther, Trevor Hastie , 2000
"... We propose a method (the \Gap statistic") for estimating the number of clusters (groups) in a set of data. The technique uses the output of any clustering algorithm (e.g. k-means or hierarchical), comparing the change in within cluster dispersion to that expected under an appropriate reference ..."
Abstract - Cited by 502 (1 self) - Add to MetaCart
We propose a method (the \Gap statistic") for estimating the number of clusters (groups) in a set of data. The technique uses the output of any clustering algorithm (e.g. k-means or hierarchical), comparing the change in within cluster dispersion to that expected under an appropriate reference

A Probabilistic Approach to Concurrent Mapping and Localization for Mobile Robots

by Sebastian Thrun, Wolfram Burgard, Dieter Fox, Henry Hexmoor, Maja Mataric - Machine Learning , 1998
"... . This paper addresses the problem of building large-scale geometric maps of indoor environments with mobile robots. It poses the map building problem as a constrained, probabilistic maximum-likelihood estimation problem. It then devises a practical algorithm for generating the most likely map from ..."
Abstract - Cited by 483 (43 self) - Add to MetaCart
. This paper addresses the problem of building large-scale geometric maps of indoor environments with mobile robots. It poses the map building problem as a constrained, probabilistic maximum-likelihood estimation problem. It then devises a practical algorithm for generating the most likely map from

Iterative point matching for registration of free-form curves and surfaces

by Zhengyou Zhang , 1994
"... A heuristic method has been developed for registering two sets of 3-D curves obtained by using an edge-based stereo system, or two dense 3-D maps obtained by using a correlation-based stereo system. Geometric matching in general is a difficult unsolved problem in computer vision. Fortunately, in ma ..."
Abstract - Cited by 660 (8 self) - Add to MetaCart
in one set to the closest points in the other. A statistical method based on the distance distribution is used to deal with outliers, occlusion, appearance and disappearance, which allows us to do subset-subset matching. A least-squares technique is used to estimate 3-D motion from the point
Next 10 →
Results 1 - 10 of 92,952
Powered by: Apache Solr
  • About CiteSeerX
  • Submit and Index Documents
  • Privacy Policy
  • Help
  • Data
  • Source
  • Contact Us

Developed at and hosted by The College of Information Sciences and Technology

© 2007-2019 The Pennsylvania State University