Title: Object Class Recognition by Unsupervised Scale-Invariant Learning
Author: R. Fergus1 P. Perona2 A. Zisserman1
Abstract:
We present a method to learn and recognize object class
models from unlabeled and unsegmented cluttered scenes
in a scale invariant manner. Objects are modeled as flexible
constellations of parts. A probabilistic representation is
used for all aspects of the object: shape, appearance, occlusion
and relative scale. An entropy-based feature detector
is used to select regions and their scale within the image. In
learning the parameters of the scale-invariant object model
are estimated. This is done using expectation-maximization
in a maximum-likelihood setting. In recognition, this model
is used in a Bayesian manner to classify images. The flexible
nature of the model is demonstrated by excellent results
over a range of datasets including geometrically constrained
classes (e.g. faces, cars) and flexible objects (such
as animals).
Full text:
link
Tuesday, October 31, 2006
Monday, October 30, 2006
Title: Learning Dynamic Maps of Temporal Gene Regulation
Title: Learning Dynamic Maps of Temporal Gene Regulation
Speaker: Jason Ernst, CMU [Link]
Venue: NSH 1507
Date: October 30
Time: 12:00 noon
For schedules, links to papers et al, please see the web page: Link
Abstract:
Time series microarray gene expression experiments have become a widely used experimental technique to study the dynamic biological responses of organisms to a variety of stimuli. The data from these experiments are often clustered to reveal significant temporal expression patterns. These observed temporal expression patterns are largely a result of a dynamic network of protein-DNA interactions that allows the specific regulation of genes needed for the response. We have developed a novel computational method that uses an Input-Output Hidden Markov Model to model these regulatory networks while taking into account their dynamic nature. Our method works by identifying bifurcation points, places in the time series where the expression of a subset of genes diverges from the rest of the genes. These points are annotated with the transcription factors regulating these transitions resulting in a unified dynamic map. Applying our method to study yeast response to stress we derive dynamic maps that are able to recover many of the known aspects of these responses. Additionally the method has made new predictions that have been experimentally validated.
Speaker: Jason Ernst, CMU [Link]
Venue: NSH 1507
Date: October 30
Time: 12:00 noon
For schedules, links to papers et al, please see the web page: Link
Abstract:
Time series microarray gene expression experiments have become a widely used experimental technique to study the dynamic biological responses of organisms to a variety of stimuli. The data from these experiments are often clustered to reveal significant temporal expression patterns. These observed temporal expression patterns are largely a result of a dynamic network of protein-DNA interactions that allows the specific regulation of genes needed for the response. We have developed a novel computational method that uses an Input-Output Hidden Markov Model to model these regulatory networks while taking into account their dynamic nature. Our method works by identifying bifurcation points, places in the time series where the expression of a subset of genes diverges from the rest of the genes. These points are annotated with the transcription factors regulating these transitions resulting in a unified dynamic map. Applying our method to study yeast response to stress we derive dynamic maps that are able to recover many of the known aspects of these responses. Additionally the method has made new predictions that have been experimentally validated.
Sunday, October 29, 2006
Batch Mode Active Learning
When: Friday October 27, at 10amWhere: Intel Research (4th floor CIC)
Speaker: Rong Jin (MSU)Title: Batch Mode Active Learning
Abstract:
The goal of active learning is to select the most informativeexamples for manual labeling. Most of the previous studies in activelearning have focused on selecting a single unlabeled example in eachiteration. This is inefficient since the classification model has to beretrained for every labeled example that is solicited. In this paper, wepresent a framework for "batch mode active learning" that applies theFisher information matrix to select a number of informative examplessimultaneously. The key computational challenge is how to efficientlyidentify the subset of unlabeled examples that can result in thelargest reduction in the classification uncertainty. In this talk, Iwill discuss two different computational approaches: one is based onthe approximated semi-definitive programming technique and the other isbased on the property of submodular functions. Empirical studies showthe promising results of the proposed approaches for batch mode activelearning in comparison to the state-of-the-art active learning methods.
Bio:
Dr. Rong Jin is an assistant Prof. of the Computer and Science
Engineering Dept. of Michigan State University since 2003. He is working
in the areas of statistical machine learning and its application to
information retrieval. In the past, Dr. Jin has worked on a variety
of machine learning algorithms, and has presented efficient and
robust algorithms for conditional exponential models, support vector
machine, and boosting. In addition, he has extensive experience
with the application of machine learning algorithms to information
retrieval, including retrieval models, collaborative filtering, cross
lingual information retrieval, document clustering, and video/image
retrieval. In the past, he has published over sixty conference and
journal articles on the related topics. Dr. Jin holds a B.A. in
Engineering from Tianjin University, an M.S. in Physics from Beijing
University, and an M.S. and Ph.D. in the area of language technologies
from Carnegie Mellon University.
Speaker: Rong Jin (MSU)Title: Batch Mode Active Learning
Abstract:
The goal of active learning is to select the most informativeexamples for manual labeling. Most of the previous studies in activelearning have focused on selecting a single unlabeled example in eachiteration. This is inefficient since the classification model has to beretrained for every labeled example that is solicited. In this paper, wepresent a framework for "batch mode active learning" that applies theFisher information matrix to select a number of informative examplessimultaneously. The key computational challenge is how to efficientlyidentify the subset of unlabeled examples that can result in thelargest reduction in the classification uncertainty. In this talk, Iwill discuss two different computational approaches: one is based onthe approximated semi-definitive programming technique and the other isbased on the property of submodular functions. Empirical studies showthe promising results of the proposed approaches for batch mode activelearning in comparison to the state-of-the-art active learning methods.
Bio:
Dr. Rong Jin is an assistant Prof. of the Computer and Science
Engineering Dept. of Michigan State University since 2003. He is working
in the areas of statistical machine learning and its application to
information retrieval. In the past, Dr. Jin has worked on a variety
of machine learning algorithms, and has presented efficient and
robust algorithms for conditional exponential models, support vector
machine, and boosting. In addition, he has extensive experience
with the application of machine learning algorithms to information
retrieval, including retrieval models, collaborative filtering, cross
lingual information retrieval, document clustering, and video/image
retrieval. In the past, he has published over sixty conference and
journal articles on the related topics. Dr. Jin holds a B.A. in
Engineering from Tianjin University, an M.S. in Physics from Beijing
University, and an M.S. and Ph.D. in the area of language technologies
from Carnegie Mellon University.
Thursday, October 26, 2006
Lab meeitng 27 Oct., 2006 (Bright): Better Motion Prediction for People-tracking
Authors: Allison Bruce and Geoffrey Gordon
From: ICRA 2004
Abstract:An important building block for intelligent mobile
robots is the ability to track people moving around in the environment.
Algorithms for person-tracking often incorporate motion
models, which can improve tracking accuracy by predicting how
people will move. More accurate motion models produce better
tracking because they allow us to average together multiple
predictions of the person’s location rather than depending
entirely on the most recent observation. Many implemented
systems, however, use simple conservative motion models such
as Brownian motion (in which the person’s direction of motion
is independent on each time step). We present an improved
motion model based on the intuition that people tend to follow
efficient trajectories through their environments rather than
random paths. Our motion model learns common destinations
within the environment by clustering training examples of actual
trajectories, then uses a path planner to predict how a person
would move along routes from his or her present location
to these destinations. We have integrated this motion model
into a particle-filter-based person-tracker, and we demonstrate
experimentally that our new motion model performs significantly
better than simpler models, especially in situations in which there
are extended periods of occlusion during tracking.
Link
From: ICRA 2004
Abstract:An important building block for intelligent mobile
robots is the ability to track people moving around in the environment.
Algorithms for person-tracking often incorporate motion
models, which can improve tracking accuracy by predicting how
people will move. More accurate motion models produce better
tracking because they allow us to average together multiple
predictions of the person’s location rather than depending
entirely on the most recent observation. Many implemented
systems, however, use simple conservative motion models such
as Brownian motion (in which the person’s direction of motion
is independent on each time step). We present an improved
motion model based on the intuition that people tend to follow
efficient trajectories through their environments rather than
random paths. Our motion model learns common destinations
within the environment by clustering training examples of actual
trajectories, then uses a path planner to predict how a person
would move along routes from his or her present location
to these destinations. We have integrated this motion model
into a particle-filter-based person-tracker, and we demonstrate
experimentally that our new motion model performs significantly
better than simpler models, especially in situations in which there
are extended periods of occlusion during tracking.
Link
[MIT CSAIL] Navigating and Reconstructing the World's Photographs
Title : Navigating and Reconstructing the World's Photographs
Speaker : Steven Seitz , University of Washington
Abstract :
There are billions of photographs on the Internet. Virtually all of the world's significant sites have been photographed under many different conditions, both from the ground and from the air. For example, a Google image search for "Notre Dame" returns half a million images, showing the cathedral from almost every conceivable viewing position and angle, different times of day and night, and changes in season, weather, and decade. In many ways, this is the dream data set for computer vision and graphics research.
Motivated by the availability of such rich data, we are exploring matching, reconstruction, and visualization algorithms that can work with very large, unorganized, and uncalibrated images sets, such as those found on the Internet. In this talk, I'll describe "Photo Tourism," (now being commercialized by Microsoft as "Photosynth"), an approach that creates immersive 3D experiences of scenes by reconstructing photographs on the Internet. I'll also describe work on multi-view stereo that reconstructs accurate 3D models from large collections of input views.
This is joint work with Noah Snavely, Rick Szeliski, Michael Goesele, Brian Curless, and Hugues Hoppe.
Bio :
Steven Seitz is Short-Dooley Associate Professor in the Department of Computer Science and Engineering at the University of Washington. He received his B.A. in computer science and mathematics at the University of California, Berkeley in 1991 and his Ph.D. in computer sciences at the University of Wisconsin, Madison in 1997. Following his doctoral work, he spent one year visiting the Vision Technology Group at Microsoft Research, and subsequently two years as an Assistant Professor in the Robotics Institute at Carnegie Mellon University. He joined the faculty at the University of Washington in July 2000. He was twice awarded the David Marr Prize for the best paper at the International Conference of Computer Vision, and has received an NSF Career Award, an ONR Young Investigator Award, and an Alfred P. Sloan Fellowship. Professor Seitz is interested in problems in computer vision and computer graphics. His current research focuses on capturing the structure, appearance, and behavior of the real world from digital imagery.
Speaker : Steven Seitz , University of Washington
Abstract :
There are billions of photographs on the Internet. Virtually all of the world's significant sites have been photographed under many different conditions, both from the ground and from the air. For example, a Google image search for "Notre Dame" returns half a million images, showing the cathedral from almost every conceivable viewing position and angle, different times of day and night, and changes in season, weather, and decade. In many ways, this is the dream data set for computer vision and graphics research.
Motivated by the availability of such rich data, we are exploring matching, reconstruction, and visualization algorithms that can work with very large, unorganized, and uncalibrated images sets, such as those found on the Internet. In this talk, I'll describe "Photo Tourism," (now being commercialized by Microsoft as "Photosynth"), an approach that creates immersive 3D experiences of scenes by reconstructing photographs on the Internet. I'll also describe work on multi-view stereo that reconstructs accurate 3D models from large collections of input views.
This is joint work with Noah Snavely, Rick Szeliski, Michael Goesele, Brian Curless, and Hugues Hoppe.
Bio :
Steven Seitz is Short-Dooley Associate Professor in the Department of Computer Science and Engineering at the University of Washington. He received his B.A. in computer science and mathematics at the University of California, Berkeley in 1991 and his Ph.D. in computer sciences at the University of Wisconsin, Madison in 1997. Following his doctoral work, he spent one year visiting the Vision Technology Group at Microsoft Research, and subsequently two years as an Assistant Professor in the Robotics Institute at Carnegie Mellon University. He joined the faculty at the University of Washington in July 2000. He was twice awarded the David Marr Prize for the best paper at the International Conference of Computer Vision, and has received an NSF Career Award, an ONR Young Investigator Award, and an Alfred P. Sloan Fellowship. Professor Seitz is interested in problems in computer vision and computer graphics. His current research focuses on capturing the structure, appearance, and behavior of the real world from digital imagery.
[CMU Intelligence Seminar] Robust Autonomous Color Learning on a Mobile Robot
Title : Robust Autonomous Color Learning on a Mobile Robot
Abstract :
The scientific community is slowly but surely working towards the creation of fully autonomous mobile robots capable of interacting with the proverbial real world. To operate in the real world, autonomous robots rely on their sensory information, but the ability to accurately sense the complex world is still missing. Visual input, in the form of color images from a camera, should be an excellent and rich source of such information, considering the significant amount of progress made in machine vision. But color, and images in general, have been used sparingly on mobile robots, where people have mostly focused their attention on other sensors such as tactile sensors, sonar and laser.
This talk presents the challenges raised and solutions introduced in our efforts to create a robust, color-based visual system for the Sony Aibo robot. We enable the robot to learn its color map autonomously and demonstrate a degree of illumination invariance under changing lighting conditions. Our contributions are fully implemented and operate in real time within the limited processing resources available onboard the robot. The system has been deployed in periodic robot soccer competitions, enabling teams of four Aibo robots to play soccer as a part of the international RoboCup initiative.
Bio :
Dr. Peter Stone is an Alfred P. Sloan Research Fellow and Assistant Professor in the Department of Computer Sciences at the University of Texas at Austin. He received his Ph.D in Computer Science in 1998 from Carnegie Mellon University. From 1999 to 2002 he was a Senior Technical Staff Member in the Artificial Intelligence Principles Research Department at AT&T Labs - Research. Peter's research interests include machine learning, multiagent systems, robotics, and e-commerce. In 2003, he won a CAREER award from the National Science Foundation for his research on learning agents in dynamic, collaborative, and adversarial multiagent environments. In 2004, he was named an ONR Young Investigator for his research on machine learning on physical robots. Most recently, he was awarded the prestigious IJCAI 2007 Computers and Thought award.
Abstract :
The scientific community is slowly but surely working towards the creation of fully autonomous mobile robots capable of interacting with the proverbial real world. To operate in the real world, autonomous robots rely on their sensory information, but the ability to accurately sense the complex world is still missing. Visual input, in the form of color images from a camera, should be an excellent and rich source of such information, considering the significant amount of progress made in machine vision. But color, and images in general, have been used sparingly on mobile robots, where people have mostly focused their attention on other sensors such as tactile sensors, sonar and laser.
This talk presents the challenges raised and solutions introduced in our efforts to create a robust, color-based visual system for the Sony Aibo robot. We enable the robot to learn its color map autonomously and demonstrate a degree of illumination invariance under changing lighting conditions. Our contributions are fully implemented and operate in real time within the limited processing resources available onboard the robot. The system has been deployed in periodic robot soccer competitions, enabling teams of four Aibo robots to play soccer as a part of the international RoboCup initiative.
Bio :
Dr. Peter Stone is an Alfred P. Sloan Research Fellow and Assistant Professor in the Department of Computer Sciences at the University of Texas at Austin. He received his Ph.D in Computer Science in 1998 from Carnegie Mellon University. From 1999 to 2002 he was a Senior Technical Staff Member in the Artificial Intelligence Principles Research Department at AT&T Labs - Research. Peter's research interests include machine learning, multiagent systems, robotics, and e-commerce. In 2003, he won a CAREER award from the National Science Foundation for his research on learning agents in dynamic, collaborative, and adversarial multiagent environments. In 2004, he was named an ONR Young Investigator for his research on machine learning on physical robots. Most recently, he was awarded the prestigious IJCAI 2007 Computers and Thought award.
[FRC seminar] Dynamic Tire-Terrain Models for Obstacle Detection
Speaker:
Dean Anderson
Ph.D. Candidate
Robotics Institute
Abstract:
What is an obstacle? In mobile robots, this is a question implicitly addressed by a perception system, but rarely directly studied itself.
As robots achieve higher speeds and venture into rougher terrain, dynamic effects become significant and cost metrics based on quasi-static analysis and heuristics perform sub-optimally.
In this talk, we present a calibrated, fully-dynamic deformable tire model for terrain evaluation. The tire model is based on penetrating volumes and includes both rolling and slipping friction forces. We will also discuss an experimental platform used to calibrate the model and insights gained in studying the effects of vehicle speed, obstacle height and slope on the "lethality" of an obstacle. Lastly, we propose a metric of terrain traversability based on our force model, and compare it to previous perception algorithms.
Speaker Bio:
Dean Anderson is a fourth-year Ph.D. student working with Alonzo Kelly. His research interests include sensors and perception algorithms for outdoor mobile robots, as well as dynamic vehicle modeling for perception and planning purposes.
Dean Anderson
Ph.D. Candidate
Robotics Institute
Abstract:
What is an obstacle? In mobile robots, this is a question implicitly addressed by a perception system, but rarely directly studied itself.
As robots achieve higher speeds and venture into rougher terrain, dynamic effects become significant and cost metrics based on quasi-static analysis and heuristics perform sub-optimally.
In this talk, we present a calibrated, fully-dynamic deformable tire model for terrain evaluation. The tire model is based on penetrating volumes and includes both rolling and slipping friction forces. We will also discuss an experimental platform used to calibrate the model and insights gained in studying the effects of vehicle speed, obstacle height and slope on the "lethality" of an obstacle. Lastly, we propose a metric of terrain traversability based on our force model, and compare it to previous perception algorithms.
Speaker Bio:
Dean Anderson is a fourth-year Ph.D. student working with Alonzo Kelly. His research interests include sensors and perception algorithms for outdoor mobile robots, as well as dynamic vehicle modeling for perception and planning purposes.
[CMU Intelligence Seminar]] Improving Systems Management Policies Using Hybrid Reinforcement Learning
Other information of Intelligence Seminar [link]
Topic: Improving Systems Management Policies Using Hybrid Reinforcement Learning
Speaker: Gerry Tesauro (IBM Watson Research)
Abstract:
Reinforcement Learning (RL) provides a promising new approach to systems
performance management that differs radically from standard
queuing-theoretic approaches making use of explicit system performance
models. In principle, RL can automatically learn high-quality management
policies without explicit performance models or traffic models, and with
little or no built-in system specific knowledge. Previously we showed
that online RL can learn to make high-quality server allocation
decisions in a multi-application prototype Data Center scenario. The
present work shows how to combine the strengths of both RL and queuing
models in a hybrid approach, in which RL trains offline on data
collected while a queuing model policy controls the system. By training
offline we avoid suffering potentially poor performance in live online
training. Our latest results show that, in both open-loop and
closed-loop traffic, hybrid RL training can achieve significant
performance improvements over a variety of initial model-based policies.
We also give several interesting insights as to how RL, as expected, can
deal effectively with both transients and switching delays, which lie
outside the scope of traditional steady-state queuing theory.
Speaker Bio:
Gerry Tesauro received a PhD in theoretical physics from Princeton
University in 1986, and owes his subsequent conversion to machine
learning research in no small part to the first Connectionist Models
Summer School, held at Carnegie Mellon in 1986. Since then he has worked
on a variety of ML applications, including computer virus recognition,
intelligent e-commerce agents, and most notoriously, TD-Gammon, a
self-teaching program that learned to play backgammon at human world
championship level. He has also been heavily involved for many years in
the annual NIPS conference, and was NIPS Program Chair in 1993 and
General Chair in 1994. He is currently interested in applying the latest
and greatest ML approaches to a huge emerging application domain of
self-managing computing systems, where he foresees great opportunities
for improvements over current state-of-the-art approaches.
Topic: Improving Systems Management Policies Using Hybrid Reinforcement Learning
Speaker: Gerry Tesauro (IBM Watson Research)
Abstract:
Reinforcement Learning (RL) provides a promising new approach to systems
performance management that differs radically from standard
queuing-theoretic approaches making use of explicit system performance
models. In principle, RL can automatically learn high-quality management
policies without explicit performance models or traffic models, and with
little or no built-in system specific knowledge. Previously we showed
that online RL can learn to make high-quality server allocation
decisions in a multi-application prototype Data Center scenario. The
present work shows how to combine the strengths of both RL and queuing
models in a hybrid approach, in which RL trains offline on data
collected while a queuing model policy controls the system. By training
offline we avoid suffering potentially poor performance in live online
training. Our latest results show that, in both open-loop and
closed-loop traffic, hybrid RL training can achieve significant
performance improvements over a variety of initial model-based policies.
We also give several interesting insights as to how RL, as expected, can
deal effectively with both transients and switching delays, which lie
outside the scope of traditional steady-state queuing theory.
Speaker Bio:
Gerry Tesauro received a PhD in theoretical physics from Princeton
University in 1986, and owes his subsequent conversion to machine
learning research in no small part to the first Connectionist Models
Summer School, held at Carnegie Mellon in 1986. Since then he has worked
on a variety of ML applications, including computer virus recognition,
intelligent e-commerce agents, and most notoriously, TD-Gammon, a
self-teaching program that learned to play backgammon at human world
championship level. He has also been heavily involved for many years in
the annual NIPS conference, and was NIPS Program Chair in 1993 and
General Chair in 1994. He is currently interested in applying the latest
and greatest ML approaches to a huge emerging application domain of
self-managing computing systems, where he foresees great opportunities
for improvements over current state-of-the-art approaches.
Tuesday, October 24, 2006
Inference in Large-Scale Graphical Models and its application to SFM, SAM, and SLAM
Inference in Large-Scale Graphical Models and its application to SFM, SAM, and SLAM
Frank Dellaert, Georgia Tech.
Intelligence Seminar at School of Computer Science at Carnegie Mellon University
Abstract:
Simultaneous Localization and Mapping (SLAM), Smoothing and Mapping (SAM), and Structure from Motion (SFM) are important and closely related problems in robotics and vision. Not surprisingly, there is a large literature describing solutions to each problem, and more and more connections are established between the two fields. At the same time, robotics and vision researchers alike are becoming increasingly familiar with the power of graphical models as a language in which to represent inference problems. In this talk I will show how SFM, SAM, and SLAM can be posed in terms of this graphical model language, and how inference in them can be explained in a purely graphical manner via the concept of variable elimination. I will then present a new way of looking at inference that is equivalent to the junction tree algorithm yet is — in my view — much more insightful. I will also show that, when applied to linear(ized) Gaussian problems, the algorithm yields the familiar QR and Cholesky factorization algorithms, and that this connection with linear algebra leads to strategies for very fast inference in arbitrary graphs. I will conclude by showing some published and preliminary work that exploits this connection to the fullest.
Links:
[Author info]
Frank Dellaert, Georgia Tech.
Intelligence Seminar at School of Computer Science at Carnegie Mellon University
Abstract:
Simultaneous Localization and Mapping (SLAM), Smoothing and Mapping (SAM), and Structure from Motion (SFM) are important and closely related problems in robotics and vision. Not surprisingly, there is a large literature describing solutions to each problem, and more and more connections are established between the two fields. At the same time, robotics and vision researchers alike are becoming increasingly familiar with the power of graphical models as a language in which to represent inference problems. In this talk I will show how SFM, SAM, and SLAM can be posed in terms of this graphical model language, and how inference in them can be explained in a purely graphical manner via the concept of variable elimination. I will then present a new way of looking at inference that is equivalent to the junction tree algorithm yet is — in my view — much more insightful. I will also show that, when applied to linear(ized) Gaussian problems, the algorithm yields the familiar QR and Cholesky factorization algorithms, and that this connection with linear algebra leads to strategies for very fast inference in arbitrary graphs. I will conclude by showing some published and preliminary work that exploits this connection to the fullest.
Links:
[Author info]
Lab meeitng 27 Oct., 2006 (Eric): Rapid Shape Acquisition Using Color Structured Light and Multi-pass Dynamic Programming
Authors:Li Zhang, Brian Curless, and Steven M. Seitz
From:In Proceedings of the 1st International Symposium on 3D Data Processing, Visualization, and Transmission (3DPVT) 2002
Abstract:This paper presents a color structured light technique forrecovering object shape from one or more images. The techniqueworks by projecting a pattern of stripes of alternatingcolors and matching the projected color transitions with observededges in the image. The correspondence problem issolved using a novel, multi-pass dynamic programming algorithmthat eliminates global smoothness assumptions andstrict ordering constraints present in previous formulations.The resulting approach is suitable for generating both highspeedscans of moving objects when projecting a single stripepattern and high-resolution scans of static scenes using ashort sequence of time-shifted stripe patterns. In the lattercase, spacetime analysis is used at each sensor pixel to obtaininter-frame depth localization. Results are demonstratedfor a variety of complex scenes.
From:In Proceedings of the 1st International Symposium on 3D Data Processing, Visualization, and Transmission (3DPVT) 2002
Abstract:This paper presents a color structured light technique forrecovering object shape from one or more images. The techniqueworks by projecting a pattern of stripes of alternatingcolors and matching the projected color transitions with observededges in the image. The correspondence problem issolved using a novel, multi-pass dynamic programming algorithmthat eliminates global smoothness assumptions andstrict ordering constraints present in previous formulations.The resulting approach is suitable for generating both highspeedscans of moving objects when projecting a single stripepattern and high-resolution scans of static scenes using ashort sequence of time-shifted stripe patterns. In the lattercase, spacetime analysis is used at each sensor pixel to obtaininter-frame depth localization. Results are demonstratedfor a variety of complex scenes.
[Link]
[My talk] The Identity Management Kalman Filter (IMKF)
Title: The Identity Management Kalman Filter (IMKF)
Authors: Brad Schumitsch, Sebastian Thrun, Leonidas Guibas, Kunle Olukotun
Robotics: Science and Systems II (RSS 2006)
August 16-19, 2006
University of Pennsylvania
Philadelphia, Pennsylvania
Abstract: Tracking posteriors estimates for problems with data association uncertainty is one of the big open problems in the literature on filtering and tracking. This paper presents a new filter for online tracking of many individual objects with data association ambiguities. It tightly integrates the continuous aspects of the problem -- locating the objects -- with the discrete aspects -- the data association ambiguity. The key innovation is a probabilistic information matrix that efficiently does identity management, that is, it links entities with internal tracks of the filter, enabling it to maintain a full posterior over the system amid data association uncertainties. The filter scales quadratically in complexity, just like a conventional Kalman filter. We derive the algorithm formally and present large-scale results.
PDF:
http://www.roboticsproceedings.org/rss02/p29.pdf
Authors: Brad Schumitsch, Sebastian Thrun, Leonidas Guibas, Kunle Olukotun
Robotics: Science and Systems II (RSS 2006)
August 16-19, 2006
University of Pennsylvania
Philadelphia, Pennsylvania
Abstract: Tracking posteriors estimates for problems with data association uncertainty is one of the big open problems in the literature on filtering and tracking. This paper presents a new filter for online tracking of many individual objects with data association ambiguities. It tightly integrates the continuous aspects of the problem -- locating the objects -- with the discrete aspects -- the data association ambiguity. The key innovation is a probabilistic information matrix that efficiently does identity management, that is, it links entities with internal tracks of the filter, enabling it to maintain a full posterior over the system amid data association uncertainties. The filter scales quadratically in complexity, just like a conventional Kalman filter. We derive the algorithm formally and present large-scale results.
PDF:
http://www.roboticsproceedings.org/rss02/p29.pdf
Friday, October 20, 2006
News: Robot swarm works together to shift heavy objects
* 18:47 17 October 2006
* NewScientist.com news service
* Tom Simonite
A "swarm" of simple-minded robots that teams up to move an object too heavy for them to manage individually has been demonstrated by robotics researchers.
The robots cannot communicate and must act only on what they can see around them. They follow simple rules to fulfil their task - mimicking the way insects work together in a swarm.
The robots were developed by Marco Dorigo at the Free University of Brussels, Belgium, along with colleagues at the Institute of Cognitive Science and Technology in Italy and the Autonomous Systems Laboratory and Dalle Molle Institute for the Study of Artificial Intelligence, both in Switzerland.
See the full article & videos.
http://www.swarm-bots.org/
* NewScientist.com news service
* Tom Simonite
A "swarm" of simple-minded robots that teams up to move an object too heavy for them to manage individually has been demonstrated by robotics researchers.
The robots cannot communicate and must act only on what they can see around them. They follow simple rules to fulfil their task - mimicking the way insects work together in a swarm.
The robots were developed by Marco Dorigo at the Free University of Brussels, Belgium, along with colleagues at the Institute of Cognitive Science and Technology in Italy and the Autonomous Systems Laboratory and Dalle Molle Institute for the Study of Artificial Intelligence, both in Switzerland.
See the full article & videos.
http://www.swarm-bots.org/
Lab Meeting 20 Oct., 2006 (Jim) : Constrained Initialisation for Bearing-Only SLAM
Constrained Initialisation for Bearing-Only SLAM
Tim Bailey, ICRA2003, PDF
This paper investigates the feature initialisation problem for bearing-only SLAM. Bearing-only SLAM is an attractive capability due to its relationship with cheap vision sensing, but initialising landmarks is difficult. First, the landmark location is unconstrained by a single measurement, and second, the location estimate due to several measurements may be ill-conditioned. This paper presents a solution to the the feature initialisation problem via the method of "constrained initialisation", where measurements are stored and initialisation is deferred until sufficient constraints exist for a well-conditioned solution. A primary contribution of this paper is a measure of "well-conditioned" for initialisation within the traditional extended Kalman Filter (EKF) framework.
Tim Bailey, ICRA2003, PDF
This paper investigates the feature initialisation problem for bearing-only SLAM. Bearing-only SLAM is an attractive capability due to its relationship with cheap vision sensing, but initialising landmarks is difficult. First, the landmark location is unconstrained by a single measurement, and second, the location estimate due to several measurements may be ill-conditioned. This paper presents a solution to the the feature initialisation problem via the method of "constrained initialisation", where measurements are stored and initialisation is deferred until sufficient constraints exist for a well-conditioned solution. A primary contribution of this paper is a measure of "well-conditioned" for initialisation within the traditional extended Kalman Filter (EKF) framework.
Thursday, October 19, 2006
IEEE news: TECHNOLOGIES THAT MAKE YOU SMILE: ADDING HUMOR TO TEXT-BASED APPLICATIONS
Humor is an essential element of interpersonal communication, but surprisingly, research often neglects the topic. But according to a new article in "IEEE Intelligent Systems," computational approaches can be successfully applied to the recognition and use of verbally expressed humor. Read more (PDF).
Should we add humor to our robots? -Bob
Should we add humor to our robots? -Bob
IEEE news: Robotic surgery
Surgical robots offer a tantalizing possibility. They could allow military doctors stationed safely distant from the front line, for example, to perform operations without once putting their hands on patients. For that vision to become reality, however, surgical robots need plenty of improvement. One challenge is designing systems that can work under conditions very different from those of pristine operating rooms.
See "Doc at a Distance," by Jacob Rosen and Blake Hannaford: the link
See "Doc at a Distance," by Jacob Rosen and Blake Hannaford: the link
Wednesday, October 18, 2006
[CMU VASC seminar series] High Resolution Acquisition, Tracking and Transfer of Dynamic 3D Facial
The advent of new technologies that allow the capture of massive amounts of high resolution, high frame rate face data, leads us to propose data-driven face models that describe detailed appearance of static faces as well as to track subtle geometry changes during expressions. However, since the dense data in these 3D scans are not registered in object space, inter-frame correspondences can not be established, which makes the tracking of facial features, estimation of facial expression dynamics and other analysis difficult.
In order to use such data for the temporal study of the subtle dynamics in expressions, an efficient non-rigid 3D motion tracking algorithm is needed to establish inter-frame correspondences. In this talk, I will present two frameworks for high resolution, non-rigid dense 3D point tracking. The first framework is a hierarchical scheme using a deformable generic face model. To begin with,a generic face mesh is first deformed to fit the data at a coarse level. Then in order to capture the highly local deformations, we use a variational algorithm for non-rigid shape registration based on the integration of an implicit shape representation and the Free-Form Deformations (FFD). The second framework, a fully automatic tracking method, is presented using harmonic maps with interior feature correspondence constraints. The novelty of this work is the development of an algorithmic framework for 3D tracking that unifies tracking of intensity and geometric features, using harmonic maps with added feature correspondence constraints. Due to the strong implicit and explicit smoothness constraints imposed by both algorithms and the high-resolution data, the resulting registration/deformation field is smooth and continuous. Both our methods are validated through a series of experiments demonstrating its accuracy and efficiency.
Furthermore, the availability of high quality dynamic expression data opens a number of research directions in face modeling. In this talk, several graphics applications will be demonstrated to use the motion data to synthesize new expressions as expression transfer from a source face to a target face.
Bio:
Yang Wang received his B.S. degree and M.Sc. degree in Computer Science from Tsinghua University in 1998 and 2000 respectively. He is a Ph.D. student in the Computer Science Department at the State University of New York at Stony Brook, where he has been working with Prof. Dimitris Samaras since 2000. He specializes in illumination modeling and estimation, 3D non-rigid motion tracking and facial expression analysis and synthesis.He is a member of ACM and IEEE.
In order to use such data for the temporal study of the subtle dynamics in expressions, an efficient non-rigid 3D motion tracking algorithm is needed to establish inter-frame correspondences. In this talk, I will present two frameworks for high resolution, non-rigid dense 3D point tracking. The first framework is a hierarchical scheme using a deformable generic face model. To begin with,a generic face mesh is first deformed to fit the data at a coarse level. Then in order to capture the highly local deformations, we use a variational algorithm for non-rigid shape registration based on the integration of an implicit shape representation and the Free-Form Deformations (FFD). The second framework, a fully automatic tracking method, is presented using harmonic maps with interior feature correspondence constraints. The novelty of this work is the development of an algorithmic framework for 3D tracking that unifies tracking of intensity and geometric features, using harmonic maps with added feature correspondence constraints. Due to the strong implicit and explicit smoothness constraints imposed by both algorithms and the high-resolution data, the resulting registration/deformation field is smooth and continuous. Both our methods are validated through a series of experiments demonstrating its accuracy and efficiency.
Furthermore, the availability of high quality dynamic expression data opens a number of research directions in face modeling. In this talk, several graphics applications will be demonstrated to use the motion data to synthesize new expressions as expression transfer from a source face to a target face.
Bio:
Yang Wang received his B.S. degree and M.Sc. degree in Computer Science from Tsinghua University in 1998 and 2000 respectively. He is a Ph.D. student in the Computer Science Department at the State University of New York at Stony Brook, where he has been working with Prof. Dimitris Samaras since 2000. He specializes in illumination modeling and estimation, 3D non-rigid motion tracking and facial expression analysis and synthesis.He is a member of ACM and IEEE.
Monday, October 16, 2006
Lab Meeting 20 Oct.,2006 (Nelson) : Metric-Based Iterative Closest Point Scan Matching
Link
Javier Minguez, Luis Montesano, and Florent Lamiraux
Abstract—This paper addresses the scan matching problem
for mobile robot displacement estimation. The contribution is
a new metric distance and all the tools necessary to be used
within the iterative closest point framework. The metric
distance is defined in the configuration space of the sensor,
and takes into account both translation and rotation error of
the sensor. The new scan matching technique ameliorates
previous methods in terms of robustness, precision,
convergence, and computational load. Furthermore, it has
been extensively tested tovalidate and compare this
technique with existing methods.
Javier Minguez, Luis Montesano, and Florent Lamiraux
Abstract—This paper addresses the scan matching problem
for mobile robot displacement estimation. The contribution is
a new metric distance and all the tools necessary to be used
within the iterative closest point framework. The metric
distance is defined in the configuration space of the sensor,
and takes into account both translation and rotation error of
the sensor. The new scan matching technique ameliorates
previous methods in terms of robustness, precision,
convergence, and computational load. Furthermore, it has
been extensively tested tovalidate and compare this
technique with existing methods.
Robotics Institute Thesis Proposal: Planning with Uncertainty in Position Using High-Resolution Maps
Author:
Juan Pablo Gonzalez
Robotics Institute
Carnegie Mellon University
Abstract:
Navigating autonomously is one of the most important problems facing outdoor mobile robots. This task can be extremely difficult if no prior information is available, and would be trivial if perfect prior information existed. In practice prior maps are usually available, but their quality and resolution varies significantly.
When accurate, high-resolution prior maps are available and the position of the robot is precisely known, many existing approaches can reliably perform the navigation task for an autonomous robot. However, if the position of the robot is not precisely known, most existing approaches would fail or would have to discard the prior map and perform the much harder task of navigating without prior information.
Most outdoor robotic platforms have two ways of determining their position: a dead-reckoning system and Global Position Systems (GPS). The dead reckoning system provides a locally accurate and locally consistent estimate that drifts slowly, and the GPS provides globally accurate estimate that does not drift, but is not necessarily locally consistent. A Kalman filter usually combines these two estimates to provide an estimate that has the best of both position estimates.
While for many scenarios this combination suffices, there are many others in which GPS is not available, or its reliability is compromised by different types of interference such as mountains, buildings, foliage or jamming. In these cases, the only position estimate available is that of the dead-reckoning system which drifts with time and does not provide a position estimate accurate enough for most navigation approaches.
This proposal addresses the problem of planning with uncertainty in position using high-resolution maps. The objective is to be able to reliably navigate distances of up to one kilometer without GPS through the use of accurate, high resolution prior maps and a good dead-reckoning system. Different approaches to the problem are analyzed, depending on the types of landmarks available, the quality of the map and the quality of the perception system.
Further Details:
A copy of the thesis proposal document can be found at http://www.ri.cmu.edu/pubs/pub_5571.html.
Juan Pablo Gonzalez
Robotics Institute
Carnegie Mellon University
Abstract:
Navigating autonomously is one of the most important problems facing outdoor mobile robots. This task can be extremely difficult if no prior information is available, and would be trivial if perfect prior information existed. In practice prior maps are usually available, but their quality and resolution varies significantly.
When accurate, high-resolution prior maps are available and the position of the robot is precisely known, many existing approaches can reliably perform the navigation task for an autonomous robot. However, if the position of the robot is not precisely known, most existing approaches would fail or would have to discard the prior map and perform the much harder task of navigating without prior information.
Most outdoor robotic platforms have two ways of determining their position: a dead-reckoning system and Global Position Systems (GPS). The dead reckoning system provides a locally accurate and locally consistent estimate that drifts slowly, and the GPS provides globally accurate estimate that does not drift, but is not necessarily locally consistent. A Kalman filter usually combines these two estimates to provide an estimate that has the best of both position estimates.
While for many scenarios this combination suffices, there are many others in which GPS is not available, or its reliability is compromised by different types of interference such as mountains, buildings, foliage or jamming. In these cases, the only position estimate available is that of the dead-reckoning system which drifts with time and does not provide a position estimate accurate enough for most navigation approaches.
This proposal addresses the problem of planning with uncertainty in position using high-resolution maps. The objective is to be able to reliably navigate distances of up to one kilometer without GPS through the use of accurate, high resolution prior maps and a good dead-reckoning system. Different approaches to the problem are analyzed, depending on the types of landmarks available, the quality of the map and the quality of the perception system.
Further Details:
A copy of the thesis proposal document can be found at http://www.ri.cmu.edu/pubs/pub_5571.html.
FRC Seminar: Sliding Autonomy for Complex Coordinated Multi-Robot Tasks: Analysis & Experiments
Speaker:
Frederik Heger
Ph.D. Candidate, Robotics Institute
Abstract:
Autonomous systems are efficient but often unreliable. In domains where reliability is paramount, efficiency is sacrificed by putting an operator in control via teleoperation. We are investigating a mode of shared control called "Sliding Autonomy" that combines the efficiency of autonomy and the reliability of human control in the performance of complex tasks, such as the assembly of large structures by a team of robots. Here we introduce an approach based on Markov models that captures interdependencies between the team members and predicts system performance. We report results from a study in which three robots work cooperatively with an operator to assemble a structure. The scenario requires high precision and has a large number of failure modes. Our results support both our expectations and modeling and show that our combined robot-human team is able to perform the assembly at a level of efficiency approaching that of fully autonomous operation while increasing overall reliability to near-teleoperation levels. This increase in performance is achieved while simultaneously reducing mental operator workload.
Speaker Bio:
Frederik Heger is a third-year Ph.D. student working with Sanjiv Singh. His research interests are in enabling robots to perform complex tasks efficiently and reliably using "Sliding utonomy," and in motion planning for teams of robots working together on complex, coordinated tasks in
constrained environments.
Frederik Heger
Ph.D. Candidate, Robotics Institute
Abstract:
Autonomous systems are efficient but often unreliable. In domains where reliability is paramount, efficiency is sacrificed by putting an operator in control via teleoperation. We are investigating a mode of shared control called "Sliding Autonomy" that combines the efficiency of autonomy and the reliability of human control in the performance of complex tasks, such as the assembly of large structures by a team of robots. Here we introduce an approach based on Markov models that captures interdependencies between the team members and predicts system performance. We report results from a study in which three robots work cooperatively with an operator to assemble a structure. The scenario requires high precision and has a large number of failure modes. Our results support both our expectations and modeling and show that our combined robot-human team is able to perform the assembly at a level of efficiency approaching that of fully autonomous operation while increasing overall reliability to near-teleoperation levels. This increase in performance is achieved while simultaneously reducing mental operator workload.
Speaker Bio:
Frederik Heger is a third-year Ph.D. student working with Sanjiv Singh. His research interests are in enabling robots to perform complex tasks efficiently and reliably using "Sliding utonomy," and in motion planning for teams of robots working together on complex, coordinated tasks in
constrained environments.
Saturday, October 14, 2006
Clever cars shine at intelligent transport conference (A scanner-based car tracking system)
full article
A prototype system developed by German company Ibeo enables a car to automatically follow the vehicle ahead. At the press of a button an infrared laser scanner in the car's bumpermeasures the distance to the next vehicle and a computer maintains a safe distance, stopping and starting if it becomes stuck in traffic.
A prototype system developed by German company Ibeo enables a car to automatically follow the vehicle ahead. At the press of a button an infrared laser scanner in the car's bumpermeasures the distance to the next vehicle and a computer maintains a safe distance, stopping and starting if it becomes stuck in traffic.
The scanner can track stationary and moving objects from up to 200 metres away at speeds of up to 180 kilometres (112 miles) per hour. "It gives a very precise image of what's going on," Max Mandt-Merck of Ibeo told New Scientist.
"Our software can distinguish cars and pedestrians from the distinctive shapes the scanner detects." A video shows the information collected by the scanner (2.1MB, mov format)Airbag activation
Mandt-Merck says the scanner can also be used to warn a driver when they stray out of lane or try to overtake too close to another vehicle. It could even activate airbags 0.3 seconds before an impact, he says.
Other systems at the show aim to prevent accidents altogether, by alerting drivers when they become distracted. A video shows one that sounds an audible alarm and vibrates the driver's seat when their head turns away from the road ahead (2.75MB WMV format). "There's an infrared camera just behind the steering wheel," explains Kato Kazuya, from Japanese automotive company Aisin. "It detects the face turning by tracking its bilateral symmetry."
A video shows another system, developed by Japanese company DENSO Corporation, that uses an infrared camera to determine whether a driver is becoming drowsy (1.91MB WMV format). "It recognises the shape of your eyes and tracks the height of that shape to watch if they close," explains Takuhiro Oomi. If a driver shuts their eyes for more than a few seconds their seat vibrates and a cold draught hits their neck.
Gaze following
The same camera system could offer other functions, Oomi says. "It can also allow the headlight beams to follow your gaze, or recognise the face of a driver and adjust the seat to their saved preferences," he says.
In the car park outside the conference centre Toyota demonstrated an intelligent parking system. A video shows the system prompting a driver to identify their chosen parking spot, which is identified using ultrasonic sensors (9.8MB, WMV format).
Once the space has been selected, the wheel turns automatically and the driver needs only to limit the car's speed using the brake pedal. When reversing into a parking bay, a camera at rear of the car is used to recognise white lines on the tarmac.
The system needs 7 metres of space for parallel parking, but can fit into a regular parking bay with just 30 centimetres clearance on either side.
"Future developments will probably see a system that lets you get out and leave the car to park itself," says a Toyota spokesman. The intelligent parking system has been available on some Toyota models in Japan since November 2005 and will be available in Europe and the US from January 2007.
Subscribe to:
Posts (Atom)