I will present my experiments on hand postures by Hidden Conditional Random Field and a related paper from IEEE Transactions on Pattern Analysis and Machine Intelligence, 2007.
paper link
Paper Abstract:
We present a discriminative latent variable model for classification problems in structured domains where inputs can be represented by a graph of local observations. A hidden-state Conditional Random Field framework learns a set of latent variables conditioned on local features. Observations need not be independent and may overlap in space and time. We evaluate our model on object detection and gesture recognition tasks.
Friday, February 29, 2008
[Paper] Enhanced Sound Localization
Title: Enhanced Sound Localization
IEEE Transactions on Systems, Man and Cybernetics, Part B
June 2004
Abstract:
IEEE Transactions on Systems, Man and Cybernetics, Part B
June 2004
Abstract:
A new approach to sound localization, known as enhanced sound localization, is introduced, offering two major benefits over state-of-the-art algorithms. First, higher localization accuracy can be achieved compared to existing methods. Second, an estimate of the source orientation is obtained jointly, as a consequence of the proposed sound localization technique. The orientation estimates and improved localizations are a result of explicitly modeling the various factors that affect a microphone's level of access to different spatial positions and orientations in an acoustic environment. Three primary factors are accounted for, namely the source directivity, microphone directivity, and source-microphone distances. Using this model of the acoustic environment, several different enhanced sound localization algorithms are derived. Experiments are carried out in a real environment whose reverberation time is 0.1 seconds, with the average microphone SNR ranging between 10-20 dB. Using a 24-element microphone array, a weighted version of the SRP-PHAT algorithm is found to give an average localization error of 13.7 cm with 3.7% anomalies, compared to 14.7 cm and 7.8% anomalies with the standard SRP-PHAT technique.
[Lab meeting] Mar. 3rd, 2008 (Yi-liu Chao): Progress Report on Monocular Visual SLAMMOT
I'm going to present my progress in monocular visual SLAMMOT focussing primarily on the moving object detection method.
Thursday, February 28, 2008
MIT news:Learning about brains from computers, and vice versa
Learning about brains from computers, and vice versa
David Chandler, MIT News Office
February 16, 2008
For many years, Tomaso Poggio's lab at MIT ran two parallel lines of research. Some projects were aimed at understanding how the brain works, using complex computational models. Others were aimed at improving the abilities of computers to perform tasks that our brains do with ease, such as making sense of complex visual images.
But recently Poggio has found that the work has progressed so far, and the two tasks have begun to overlap to such a degree, that it's now time to combine the two lines of research.
See the full article.
David Chandler, MIT News Office
February 16, 2008
For many years, Tomaso Poggio's lab at MIT ran two parallel lines of research. Some projects were aimed at understanding how the brain works, using complex computational models. Others were aimed at improving the abilities of computers to perform tasks that our brains do with ease, such as making sense of complex visual images.
But recently Poggio has found that the work has progressed so far, and the two tasks have begun to overlap to such a degree, that it's now time to combine the two lines of research.
See the full article.
Sunday, February 24, 2008
[Lab meeting] Feb. 25th, 2008 (Der-Yeuan Yu): Indoor 3D Mapping Progress Report
I will give a brief report on my progress in constructing an indoor 3D environment map. The hardware set up would be two orthogonally placed SICK S200 laser scanners mounted on PAL5. One of the main challenges is the matter of matching maps in different floors together. Below are some interesting references.
C. Früh and A. Zakhor, "Fast 3D model generation in urban environments", in International Conference on Multisensor Fusion and Integration for Intelligent Systems 2001, Baden-Baden, Germany, August 2001, p. 165-170.
http://www-video.eecs.berkeley.edu/papers/frueh/mfi2001.pdf
Zho, H. and Shibasaki, R., "Reconstructing Urban 3D Model using Vehicle-borne Laser Range Scanners", 3-D Digital Imaging and Modeling, 2001. Proceedings, p. 349-356.
http://shiba.iis.u-tokyo.ac.jp/member/current/zhao/pub/3dim2001.pdf
C. Früh and A. Zakhor, "Fast 3D model generation in urban environments", in International Conference on Multisensor Fusion and Integration for Intelligent Systems 2001, Baden-Baden, Germany, August 2001, p. 165-170.
http://www-video.eecs.berkeley.edu/papers/frueh/mfi2001.pdf
Zho, H. and Shibasaki, R., "Reconstructing Urban 3D Model using Vehicle-borne Laser Range Scanners", 3-D Digital Imaging and Modeling, 2001. Proceedings, p. 349-356.
http://shiba.iis.u-tokyo.ac.jp/member/current/zhao/pub/3dim2001.pdf
Saturday, February 23, 2008
[Lab meeting] 02/25/08 (Ekker): 3D-Odometry for rough terrain-Towards real 3D navigation
Pierre Lamon and Roland Siegwart
Swiss Federal Institute of Technology, Lausanne (EPFL)
Pierre.Lamon@epfl.ch, Roland.Siegwart@epfl.ch
From :Robotics and Automation, 2003. Proceedings. ICRA '03. IEEE International
Abstract
Up to recently autonomous mobile robots were mostly
designed to run within an indoor, yet partly structured
and flat, environment. In rough terrain many problems
arise and position tracking becomes more difficult. The
robot has to deal with wheel slippage and large
orientation changes. In this paper we will first present the
recent developments on the off-road rover Shrimp. Then a
new method, called 3D-Odometry, which extends the
standard 2D odometry to the 3D space will be developed.
Since it accounts for transitions, the 3D-Odometry
provides better position estimates. It will certainly help to
go towards real 3D navigation for outdoor robots.
fulltext
Swiss Federal Institute of Technology, Lausanne (EPFL)
Pierre.Lamon@epfl.ch, Roland.Siegwart@epfl.ch
From :Robotics and Automation, 2003. Proceedings. ICRA '03. IEEE International
Abstract
Up to recently autonomous mobile robots were mostly
designed to run within an indoor, yet partly structured
and flat, environment. In rough terrain many problems
arise and position tracking becomes more difficult. The
robot has to deal with wheel slippage and large
orientation changes. In this paper we will first present the
recent developments on the off-road rover Shrimp. Then a
new method, called 3D-Odometry, which extends the
standard 2D odometry to the 3D space will be developed.
Since it accounts for transitions, the 3D-Odometry
provides better position estimates. It will certainly help to
go towards real 3D navigation for outdoor robots.
fulltext
Friday, February 22, 2008
[ML Lunch] Shuheng Zhou on High Dimensional Sparse Regression and Structure Estimation
Speaker: Shuheng Zhou
Title: High Dimensional Sparse Regression and Structure Estimation
Venue: NSH 1507
Date: Monday February 25Time: 12:00 noon
Abstract:
Recent research has demonstrated that sparsity is a powerful technique insignal reconstruction and in statistical inference. Recent work shows that$\ell_1$-regularized least squares regression can accurately estimate a sparsemodel from n noisy samples in $p$ dimensions, even if p is much larger than n.My talk focuses on studying the role of sparsity in high dimensionalregression when the original noisy samples are compressed, and onstructure estimation in Gaussian graphical models when the graphs evolveover time.
In high-dimensional regression, the sparse object is a vector \betain Y = X \beta + \epsilon, where X is n by p matrix such that $n <<> n even for the case when $\epsilon =0$. However, when the vector \betais sparse, one can recover an empirical $\hat \beta$ that is consistent interms of its support with true $\beta$. In joint work with John Lafferty andLarry Wasserman, we studied the regression problem under the setting that theoriginal n input variables are compressed by a random Gaussian ensemble to m
examples in $p$ dimensions, where m << n or p. A primary motivation for thiscompression procedure is to anonymize the data and preserve privacy byrevealing little information about the original data. We establishedsufficient mutual incoherence conditions on X, under which a sparse linearmodel can be successfully recovered from the compressed data. Wecharacterized the number of random projections that are required for$\ell_1$-regularized compressed regression to identify the nonzerocoefficients in the true model with probability approaching one. Inaddition, we showed that $\ell_1$-regularized compressed regressionasymptotically predicts as well as an oracle linear model, a propertycalled ``persistence''. Finally, we established upper bounds on the mutualinformation between the compressed and uncompressed data that decay to zero.
Undirected graphs are often used to describe high dimensional distributions.Under sparsity conditions, the graph can be estimated using $L_1$ penalizationmethods. However, current methods assume that the data are independent andidentically distributed. If the distribution---and hence the graph--- evolvesover time then the data are not longer identically distributed. In the secondpart of the talk, I show how to estimate the sequence of graphs fornon-identically distributed data and establish some theoretical results onconvergence rate in the predictive risks and the Frobenius norm of theinverse covariance matrix. This is joint work with John Lafferty and LarryWasserman.
Title: High Dimensional Sparse Regression and Structure Estimation
Venue: NSH 1507
Date: Monday February 25Time: 12:00 noon
Abstract:
Recent research has demonstrated that sparsity is a powerful technique insignal reconstruction and in statistical inference. Recent work shows that$\ell_1$-regularized least squares regression can accurately estimate a sparsemodel from n noisy samples in $p$ dimensions, even if p is much larger than n.My talk focuses on studying the role of sparsity in high dimensionalregression when the original noisy samples are compressed, and onstructure estimation in Gaussian graphical models when the graphs evolveover time.
In high-dimensional regression, the sparse object is a vector \betain Y = X \beta + \epsilon, where X is n by p matrix such that $n <<> n even for the case when $\epsilon =0$. However, when the vector \betais sparse, one can recover an empirical $\hat \beta$ that is consistent interms of its support with true $\beta$. In joint work with John Lafferty andLarry Wasserman, we studied the regression problem under the setting that theoriginal n input variables are compressed by a random Gaussian ensemble to m
examples in $p$ dimensions, where m << n or p. A primary motivation for thiscompression procedure is to anonymize the data and preserve privacy byrevealing little information about the original data. We establishedsufficient mutual incoherence conditions on X, under which a sparse linearmodel can be successfully recovered from the compressed data. Wecharacterized the number of random projections that are required for$\ell_1$-regularized compressed regression to identify the nonzerocoefficients in the true model with probability approaching one. Inaddition, we showed that $\ell_1$-regularized compressed regressionasymptotically predicts as well as an oracle linear model, a propertycalled ``persistence''. Finally, we established upper bounds on the mutualinformation between the compressed and uncompressed data that decay to zero.
Undirected graphs are often used to describe high dimensional distributions.Under sparsity conditions, the graph can be estimated using $L_1$ penalizationmethods. However, current methods assume that the data are independent andidentically distributed. If the distribution---and hence the graph--- evolvesover time then the data are not longer identically distributed. In the secondpart of the talk, I show how to estimate the sequence of graphs fornon-identically distributed data and establish some theoretical results onconvergence rate in the predictive risks and the Frobenius norm of theinverse covariance matrix. This is joint work with John Lafferty and LarryWasserman.
Thursday, February 21, 2008
Robotics Institute Thesis Proposal : Mapping Large Urban Environments with GPS-Aided SLAM
Mapping Large Urban Environments with GPS-Aided SLAM
Justin Carlson
Robotics Institute
Carnegie Mellon University
Place and time
NSH 3305
9:00 AM 27 Feb 2008
Abstract
Simultaneous Localization and Mapping (SLAM) has been an active area of research for several decades, and has become a foundation of indoor mobile robotics. Although the scale and quality of results has improved markedly in that time period, no current technique can effectively handle city-sized urban areas.
The Global Positioning System (GPS) is an extraordinarily useful source of localization information. Unfortunately, the noise characteristics of the system are complex, arising from a large number of sources, some of which have large autocorrelation. Incorporation of GPS signals into SLAM algorithms requires using low-level system information and explicit models of the underlying system to appropriately make use of the information. The potential benefits of combining GPS and SLAM include increased robustness, increased scalability, and improved accuracy of localization.
This proposal will present a theoretical background for GPS-SLAM fusion, initial results in simulation, and initial results using data gathered near the Carnegie Mellon Qatar Campus. Future work will look into specific GPS-SLAM pairings, and demonstrate the ability to generate large-scale maps of urban areas.
Further Details
A copy of the thesis proposal document can be found at http://www.cs.cmu.edu/~justinca/jcarlson_proposal.pdf.
Thesis Committee
Charles Thorpe, Chair
Brett Browning
Martial Hebert
Frank Dellaert, Georgia Institute of Technology
Justin Carlson
Robotics Institute
Carnegie Mellon University
Place and time
NSH 3305
9:00 AM 27 Feb 2008
Abstract
Simultaneous Localization and Mapping (SLAM) has been an active area of research for several decades, and has become a foundation of indoor mobile robotics. Although the scale and quality of results has improved markedly in that time period, no current technique can effectively handle city-sized urban areas.
The Global Positioning System (GPS) is an extraordinarily useful source of localization information. Unfortunately, the noise characteristics of the system are complex, arising from a large number of sources, some of which have large autocorrelation. Incorporation of GPS signals into SLAM algorithms requires using low-level system information and explicit models of the underlying system to appropriately make use of the information. The potential benefits of combining GPS and SLAM include increased robustness, increased scalability, and improved accuracy of localization.
This proposal will present a theoretical background for GPS-SLAM fusion, initial results in simulation, and initial results using data gathered near the Carnegie Mellon Qatar Campus. Future work will look into specific GPS-SLAM pairings, and demonstrate the ability to generate large-scale maps of urban areas.
Further Details
A copy of the thesis proposal document can be found at http://www.cs.cmu.edu/~justinca/jcarlson_proposal.pdf.
Thesis Committee
Charles Thorpe, Chair
Brett Browning
Martial Hebert
Frank Dellaert, Georgia Institute of Technology
Friday, February 01, 2008
CMU Intelligence Seminar: Understanding Shape using Probabilistic Correspondence
Title: Understanding Shape using Probabilistic Correspondence
Daphne Koller
Stanford University
Faculty Host: Carlos Guestrin
Physical objects in a given class often have a characteristic shape: we can all recognize a giraffe or a coffee mug even from a simple line drawing. This talk describes a characterization of object shape, both in 3D and in 2D, as a probabilistic graphical model, and demonstrates its application to problems in both vision and graphics. Our shape modeling framework encompasses signification variation both of general object shape and of object pose. We show how to learn this model from a collection of unlabeled instances of object shape. A key building block in this approach is the correspondence task, where we map points in the shape of one objects to the points in another. We describe a probabilistic formulation of this task and solutions for addressing it. We also present a method for automatically decomposing a shape into its articulated parts, and for learning a probabilistic model for its shape variation. Finally, we present applications of this framework to a variety of tasks. In the context of graphics, we show applications to shape completion and to shape synthesis from motion capture data. In the context of vision, we show how shape models can be used to precisely outline objects in a cluttered image. We also show how a semantically consistent shape model for an object class, learned from an unlabeled set of object shapes, can be used, with only a handful of labeled instances, to accurately answer semantic queries such as whether a cheetah is running or whether an airplane is taking off. Thus, a more detailed model of object shape can be used as a building block in semantic interpretation of the physical world.
Speaker Bio
Daphne Koller received her BSc and MSc degrees from the Hebrew University of Jerusalem, Israel, and her PhD from Stanford University in 1993. After a two-year postdoc at Berkeley, she returned to Stanford, where she is now a Professor in the Computer Science Department. Her main research interest is in developing and using machine learning and probabilistic methods to model and analyze complex domains. Her current research projects include models in computational biology and in reasoning about the physical worls. Daphne Koller is the author of over 100 refereed publications, which have appeared in venues spanning Science, Nature Genetics, the Journal of Games and Economic Behavior, and a variety of conferences and journals in AI and Computer Science. She was the program co-chair of the NIPS 2007 and UAI 2001 conferences, and has served on numerous program committees and as associate editor of the Journal of Artificial Intelligence Research and of the Machine Learning Journal. She was awarded the Arthur Samuel Thesis Award in 1994, the Sloan Foundation Faculty Fellowship in 1996, the ONR Young Investigator Award in 1998, the Presidential Early Career Award for Scientists and Engineers (PECASE) in 1999, the IJCAI Computers and Thought Award in 2001, the Cox Medal for excellence in fostering undergraduate research at Stanford in 2003, and the MacArthur Foundation Fellowship in 2004.
Daphne Koller
Stanford University
Faculty Host: Carlos Guestrin
Physical objects in a given class often have a characteristic shape: we can all recognize a giraffe or a coffee mug even from a simple line drawing. This talk describes a characterization of object shape, both in 3D and in 2D, as a probabilistic graphical model, and demonstrates its application to problems in both vision and graphics. Our shape modeling framework encompasses signification variation both of general object shape and of object pose. We show how to learn this model from a collection of unlabeled instances of object shape. A key building block in this approach is the correspondence task, where we map points in the shape of one objects to the points in another. We describe a probabilistic formulation of this task and solutions for addressing it. We also present a method for automatically decomposing a shape into its articulated parts, and for learning a probabilistic model for its shape variation. Finally, we present applications of this framework to a variety of tasks. In the context of graphics, we show applications to shape completion and to shape synthesis from motion capture data. In the context of vision, we show how shape models can be used to precisely outline objects in a cluttered image. We also show how a semantically consistent shape model for an object class, learned from an unlabeled set of object shapes, can be used, with only a handful of labeled instances, to accurately answer semantic queries such as whether a cheetah is running or whether an airplane is taking off. Thus, a more detailed model of object shape can be used as a building block in semantic interpretation of the physical world.
Speaker Bio
Daphne Koller received her BSc and MSc degrees from the Hebrew University of Jerusalem, Israel, and her PhD from Stanford University in 1993. After a two-year postdoc at Berkeley, she returned to Stanford, where she is now a Professor in the Computer Science Department. Her main research interest is in developing and using machine learning and probabilistic methods to model and analyze complex domains. Her current research projects include models in computational biology and in reasoning about the physical worls. Daphne Koller is the author of over 100 refereed publications, which have appeared in venues spanning Science, Nature Genetics, the Journal of Games and Economic Behavior, and a variety of conferences and journals in AI and Computer Science. She was the program co-chair of the NIPS 2007 and UAI 2001 conferences, and has served on numerous program committees and as associate editor of the Journal of Artificial Intelligence Research and of the Machine Learning Journal. She was awarded the Arthur Samuel Thesis Award in 1994, the Sloan Foundation Faculty Fellowship in 1996, the ONR Young Investigator Award in 1998, the Presidential Early Career Award for Scientists and Engineers (PECASE) in 1999, the IJCAI Computers and Thought Award in 2001, the Cox Medal for excellence in fostering undergraduate research at Stanford in 2003, and the MacArthur Foundation Fellowship in 2004.
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