Probabilistic Cooperative Localization and Mapping in Practice
Author: Ioannis Rekleitis, Gregory Dudek and Evangelos Milios
From: International Conference on Robotics and Automation, 2003.
Abstract:
In this paper we present a probabilistic framework for the reduction in the uncertainty of a moving robot pose during exploration by using a second robot to assist. A Monte Carlo Simulation technique (specifically, a Particle Filter) is employed in order to model and reduce the accumulated odometric error. Furthermore, we study the requirements to obtain an accurate yet timely pose estimate. A team of two robots is employed to explore an indoor environment in this paper, although several aspects of the approach have been extended to larger groups. The concept behind our exploration strategy has been presented previously and is based on having one robot carry a sensor that acts as a “robot tracker” to estimate the position of the other robot. By suitable use of the tracker as an appropriate motion-control mechanism we can sweep areas of free space between the stationary and the moving robot and generate an accurate graph-based description of the environment. This graph is used to guide the exploration process. Complete exploration without any overlaps is guaranteed as a result of the guidance provided by the dual graph of the spatial decomposition (triangulation) of the environment. We present experimental results from indoor experiments in our laboratory and from more complex simulated experiments.
Paper: Cooperative Localization and Multi-Robot Exploration
Related Materials: Particle Filter Tutorial for Mobile Robots
Monday, February 27, 2006
Nature: Efficient auditory coding
Evan C. Smith & Michael S. Lewicki, CMU
Nature 439, 978-982 (23 February 2006)
The auditory neural code must serve a wide range of auditory tasks that require great sensitivity in time and frequency and be effective over the diverse array of sounds present in natural acoustic environments. It has been suggested that sensory systems might have evolved highly efficient coding strategies to maximize the information conveyed to the brain while minimizing the required energy and neural resources. Here we show that, for natural sounds, the complete acoustic waveform can be represented efficiently with a nonlinear model based on a population spike code. In this model, idealized spikes encode the precise temporal positions and magnitudes of underlying acoustic features. We find that when the features are optimized for coding either natural sounds or speech, they show striking similarities to time-domain cochlear filter estimates, have a frequencybandwidth dependence similar to that of auditory nerve fibres, and yield significantly greater coding efficiency than conventional signal representations. These results indicate that the auditory code might approach an information theoretic optimum and that the acoustic structure of speech might be adapted to the coding capacity of the mammalian auditory system.
[PDF]
Nature 439, 978-982 (23 February 2006)
The auditory neural code must serve a wide range of auditory tasks that require great sensitivity in time and frequency and be effective over the diverse array of sounds present in natural acoustic environments. It has been suggested that sensory systems might have evolved highly efficient coding strategies to maximize the information conveyed to the brain while minimizing the required energy and neural resources. Here we show that, for natural sounds, the complete acoustic waveform can be represented efficiently with a nonlinear model based on a population spike code. In this model, idealized spikes encode the precise temporal positions and magnitudes of underlying acoustic features. We find that when the features are optimized for coding either natural sounds or speech, they show striking similarities to time-domain cochlear filter estimates, have a frequencybandwidth dependence similar to that of auditory nerve fibres, and yield significantly greater coding efficiency than conventional signal representations. These results indicate that the auditory code might approach an information theoretic optimum and that the acoustic structure of speech might be adapted to the coding capacity of the mammalian auditory system.
[PDF]
Sunday, February 26, 2006
MIT talk: Hierarchical Abstractions for Planning & Control of Robotic Swarms
Speaker: Calin Belta, Boston University
Date: Tuesday, February 28 2006
Host: Daniela Rus, MIT
Abstract:
Specifying, planning, and controlling the motion of large groups of mobile agents (swarms) are difficult problems that received a lot of attention in recent years. I will present some recent results on reducing the dimension and complexity of such problems by defining abstractions. First, I will focus on continuous abstractions, which are obtained by extracting a small set of essential features of a swarm that can be used for planning and control. Second, I will show how discrete abstractions can be used to construct a finite dimensional description of the problem. Third, I will present an example in which the above two types of abstractions are seamlessly linked into a hierarchical abstraction framework, in which high level swarm specifications given as temporal logic formulas over features of interest are automatically converted into provably correct robot control laws.
Date: Tuesday, February 28 2006
Host: Daniela Rus, MIT
Abstract:
Specifying, planning, and controlling the motion of large groups of mobile agents (swarms) are difficult problems that received a lot of attention in recent years. I will present some recent results on reducing the dimension and complexity of such problems by defining abstractions. First, I will focus on continuous abstractions, which are obtained by extracting a small set of essential features of a swarm that can be used for planning and control. Second, I will show how discrete abstractions can be used to construct a finite dimensional description of the problem. Third, I will present an example in which the above two types of abstractions are seamlessly linked into a hierarchical abstraction framework, in which high level swarm specifications given as temporal logic formulas over features of interest are automatically converted into provably correct robot control laws.
MIT talk: Medical Image Registration in Healthcare, Biomedical Research and Drug Discovery
Speaker: Daniel Rueckert , Imperial College London
Date: Tuesday, February 28 2006
Contact: Polina Golland, x38005, polina@csail.mit.edu
Abstract:
Imaging technologies are developing at a rapid pace allowing for in-vivo 3D and 4D imaging of the anatomy and physiology in humans and animals. This is opening up unprecedented opportunities for research and clinical applications ranging from imaging for drug discovery and delivery, over imaging for diagnosis and therapy, to imaging for basic research such as brain mapping. In this talk we will focus on how computational techniques based on non-rigid image registration can be used to address the image analysis challenges in healthcare, biomedical research and drug discovery.
Date: Tuesday, February 28 2006
Contact: Polina Golland, x38005, polina@csail.mit.edu
Abstract:
Imaging technologies are developing at a rapid pace allowing for in-vivo 3D and 4D imaging of the anatomy and physiology in humans and animals. This is opening up unprecedented opportunities for research and clinical applications ranging from imaging for drug discovery and delivery, over imaging for diagnosis and therapy, to imaging for basic research such as brain mapping. In this talk we will focus on how computational techniques based on non-rigid image registration can be used to address the image analysis challenges in healthcare, biomedical research and drug discovery.
Saturday, February 25, 2006
CMU FRC talk: Online and Structured Learning Techniques for Outdoor Robotics
Speaker: Drew Bagnell, Research Scientist, Robotics Institute
Date: Thursday, March 2, 2006
Abstract:
This presentation is based on joint work with Nathan Ratliff, Boris Sofman, Ellie Lin, Nicolas Vandapel, and Anthony Stentz
Programming behaviors for outdoor mobile robot navigation is hard. Machine learning promises to alleviate this difficulty but existing techniques often fall short. For instance, it is often the case that some features that, while potentially powerful for improving navigation, prove difficult to profit from as they generalize poorly to novel situations. Overhead imagery data, for instance, has the potential to greatly enhance autonomous robot navigation in complex outdoor environments. In practice, reliable and effective automated interpretation of imagery from diverse terrain, environmental conditions, and sensor varieties proves challenging. I'll discuss online, probabilistic models to effectively learn to use these scope-limited features by leveraging other features that, while perhaps otherwise more limited, generalize reliably.
I'll also discuss work on mobile robot learning based on demonstrated trajectories. This is a natural and potentially powerful approach to teaching a system. Unfortunately, most existing techniques to learn based on demonstrated trajectories face at least two important difficulties. First, it very hard to get "negative examples", in this framework; we can't actually drive the robot off a cliff or into a boulder. Secondly, it is very difficult to acquire long-horizon and goal-directed behavior by imitating a trainer. I'll talk about a new approach that addresses both concerns. It learns to map features of the world into costs for a planner in such a way so that resulting optimal plans mimic the trainer's behavior. This approach is powerful, as the behavior that a designer wishes the planner to execute is often clear, while specifying costs that engender this behavior is often much more difficult.
Date: Thursday, March 2, 2006
Abstract:
This presentation is based on joint work with Nathan Ratliff, Boris Sofman, Ellie Lin, Nicolas Vandapel, and Anthony Stentz
Programming behaviors for outdoor mobile robot navigation is hard. Machine learning promises to alleviate this difficulty but existing techniques often fall short. For instance, it is often the case that some features that, while potentially powerful for improving navigation, prove difficult to profit from as they generalize poorly to novel situations. Overhead imagery data, for instance, has the potential to greatly enhance autonomous robot navigation in complex outdoor environments. In practice, reliable and effective automated interpretation of imagery from diverse terrain, environmental conditions, and sensor varieties proves challenging. I'll discuss online, probabilistic models to effectively learn to use these scope-limited features by leveraging other features that, while perhaps otherwise more limited, generalize reliably.
I'll also discuss work on mobile robot learning based on demonstrated trajectories. This is a natural and potentially powerful approach to teaching a system. Unfortunately, most existing techniques to learn based on demonstrated trajectories face at least two important difficulties. First, it very hard to get "negative examples", in this framework; we can't actually drive the robot off a cliff or into a boulder. Secondly, it is very difficult to acquire long-horizon and goal-directed behavior by imitating a trainer. I'll talk about a new approach that addresses both concerns. It learns to map features of the world into costs for a planner in such a way so that resulting optimal plans mimic the trainer's behavior. This approach is powerful, as the behavior that a designer wishes the planner to execute is often clear, while specifying costs that engender this behavior is often much more difficult.
CMU ML talk: Machine Learning in TAC SCM (Trading Agent Competition in Supply)
Speaker: Michael Benisch, COS, CMU. http://www.cs.cmu.edu/~mbenisch/
Date: February 27
Abstract:
Supply chains aid the manufacturing of many complex goods. Traditionally, supply chains have been maintained by human negotiators through long-term, static contracts, despite uncertain and dynamic market conditions. However, there has been a recent growing interest, from both industry and academia, in the potential for automating more efficient supply chain processes. The TAC SCM (Trading Agent Comeptition in Supply Chain Management) scenario is an international competition that provides a research platform facilitating the application of new academic technologies to the problem of managing a dynamic supply chain. Since the inception of TAC SCM, machine learning has emerged an essential aspect of successful agent design. Many agents, such as Carnegie Mellon's 2005 entry, CMieux, utilize learning techniques to estimate market conditions, and model opponent behavior. In this talk, we will discuss some specific learning problems faced by these agents, including the problem of forecasting future demand, the problem of predicting auction closing prices, and the problem of approximating supply availability. We will also discuss various solutions developed by researchers to address them, including a new extension of M5 regression trees used by CMieux, called distribution trees.
Date: February 27
Abstract:
Supply chains aid the manufacturing of many complex goods. Traditionally, supply chains have been maintained by human negotiators through long-term, static contracts, despite uncertain and dynamic market conditions. However, there has been a recent growing interest, from both industry and academia, in the potential for automating more efficient supply chain processes. The TAC SCM (Trading Agent Comeptition in Supply Chain Management) scenario is an international competition that provides a research platform facilitating the application of new academic technologies to the problem of managing a dynamic supply chain. Since the inception of TAC SCM, machine learning has emerged an essential aspect of successful agent design. Many agents, such as Carnegie Mellon's 2005 entry, CMieux, utilize learning techniques to estimate market conditions, and model opponent behavior. In this talk, we will discuss some specific learning problems faced by these agents, including the problem of forecasting future demand, the problem of predicting auction closing prices, and the problem of approximating supply availability. We will also discuss various solutions developed by researchers to address them, including a new extension of M5 regression trees used by CMieux, called distribution trees.
CMU thesis proposal: Real-time Planning for Single Agents and Multi-agent Teams in Unknown and Dynamic Environments
David Ferguson, Robotics Institute, Carnegie Mellon University
3 Mar 2006
Abstract
As autonomous agents make the transition from solving simple, well-behaved problems to being useful entities in the real world, they must deal with the added complexity and uncertainty inherent in real environments. In particular, agents navigating through the real world can be confronted with incomplete or imperfect information (e.g. when prior maps are absent or incomplete), large state spaces (e.g. for robots with several degrees of freedom or teams of robots), and dynamic elements (e.g. when there are humans or other agents in the environment). In this work, we propose to address the problem of path planning and replanning in both static and dynamic environments for which prior information may be incomplete or imperfect. We intend to develop a set of planning algorithms that will enable single agents and multi-agent teams to operate more effectively in a wider range of realistic scenarios.
A copy of the thesis proposal document can be found at http://gs2045.sp.cs.cmu.edu/downloads/proposal.pdf.
3 Mar 2006
Abstract
As autonomous agents make the transition from solving simple, well-behaved problems to being useful entities in the real world, they must deal with the added complexity and uncertainty inherent in real environments. In particular, agents navigating through the real world can be confronted with incomplete or imperfect information (e.g. when prior maps are absent or incomplete), large state spaces (e.g. for robots with several degrees of freedom or teams of robots), and dynamic elements (e.g. when there are humans or other agents in the environment). In this work, we propose to address the problem of path planning and replanning in both static and dynamic environments for which prior information may be incomplete or imperfect. We intend to develop a set of planning algorithms that will enable single agents and multi-agent teams to operate more effectively in a wider range of realistic scenarios.
A copy of the thesis proposal document can be found at http://gs2045.sp.cs.cmu.edu/downloads/proposal.pdf.
Thursday, February 23, 2006
What's New @ IEEE in Wireless, February 2006
4. WHEELED NETWORKS REQUIRE NEW SECURITY SOLUTIONS
Greater numbers of vehicles equipped for wireless networking present new security challenges due to the short contact times between different mobile nodes and the large size of the networks, according to researchers studying the issue. The German-funded Network on Wheels (NoW) project incorporates security considerations into network development. Researchers say those concerns include continuous system availability (a system is robust even in the presence of malicious or faulty nodes); privacy, including un-traceability of actions to a user and un-linkability of the actions of a node; and secure communication. Current work on NoW includes detecting attacks on the different parts of the system and estimating both their impact and probability, researchers say. Read more: http://www.primidi.com/2006/02/01.html
7. WIRELESS RESCUE SYSTEM TO BE TESTED IN U.S. MINES
Wireless systems that locate trapped miners and send them text messages are being tested by the U.S. Mine Safety and Health Administration (MSHA), including one system which pinpoints the location of individual miners, according to researchers. One of the systems uses a transmitter worn by miners that sends out a signal unique to each individual, researchers say, while another device is a personal receiver that allows rescuers to send text messages to the miners. Both technologies operate on a network of wireless radio transmitters installed in the tunnels, and were developed by the Australian firm Mine Site Technologies. Read more: http://www.physorg.com/news10522.html
13. ENGLAND'S WINES PROJECT AGING NICELY
Four groups funded by England's Wired and Wireless Networked Systems (WINES) program -- which studies the creation of massive-scale ubiquitous and pervasive computing environments -- are examined in this month's issue of IEEE Distributed Systems Online. TIME-EACM, a collaboration between the University of London and Birkbeck College, is studying how wired and wireless systems can improve traffic flow and congestion in urban areas. BiosensorNet, comprised of several teams from Imperial College London, hopes to improve the medical industry with state of the art wireless sensors implanted in the body. Cityware, a project including the University of Bath, Imperial College London, and University College London, is studying how new integrated information systems placed in architecture will affect peoples' relationships with their environment. Finally, NEMO, comprised of departments at Lancaster University, is looking at embedding sensors in everyday objects -- called smart artifacts -- in order to enable physical entities to capture and share their "experiences." A new round of WINES funding set to be unleashed next month. Read more: the link
Greater numbers of vehicles equipped for wireless networking present new security challenges due to the short contact times between different mobile nodes and the large size of the networks, according to researchers studying the issue. The German-funded Network on Wheels (NoW) project incorporates security considerations into network development. Researchers say those concerns include continuous system availability (a system is robust even in the presence of malicious or faulty nodes); privacy, including un-traceability of actions to a user and un-linkability of the actions of a node; and secure communication. Current work on NoW includes detecting attacks on the different parts of the system and estimating both their impact and probability, researchers say. Read more: http://www.primidi.com/2006/02/01.html
7. WIRELESS RESCUE SYSTEM TO BE TESTED IN U.S. MINES
Wireless systems that locate trapped miners and send them text messages are being tested by the U.S. Mine Safety and Health Administration (MSHA), including one system which pinpoints the location of individual miners, according to researchers. One of the systems uses a transmitter worn by miners that sends out a signal unique to each individual, researchers say, while another device is a personal receiver that allows rescuers to send text messages to the miners. Both technologies operate on a network of wireless radio transmitters installed in the tunnels, and were developed by the Australian firm Mine Site Technologies. Read more: http://www.physorg.com/news10522.html
13. ENGLAND'S WINES PROJECT AGING NICELY
Four groups funded by England's Wired and Wireless Networked Systems (WINES) program -- which studies the creation of massive-scale ubiquitous and pervasive computing environments -- are examined in this month's issue of IEEE Distributed Systems Online. TIME-EACM, a collaboration between the University of London and Birkbeck College, is studying how wired and wireless systems can improve traffic flow and congestion in urban areas. BiosensorNet, comprised of several teams from Imperial College London, hopes to improve the medical industry with state of the art wireless sensors implanted in the body. Cityware, a project including the University of Bath, Imperial College London, and University College London, is studying how new integrated information systems placed in architecture will affect peoples' relationships with their environment. Finally, NEMO, comprised of departments at Lancaster University, is looking at embedding sensors in everyday objects -- called smart artifacts -- in order to enable physical entities to capture and share their "experiences." A new round of WINES funding set to be unleashed next month. Read more: the link
PASCAL Visual Object Classes Recognition Challenge 2006
Subject: PASCAL Visual Object Classes Recognition Challenge 2006
Date: Fri, 17 Feb 2006 20:32:18 GMT
From: Andrew Zisserman
Dear All,
We are running a second PASCAL Visual Object Classes Recognition Challenge. This time there are more classes (ten), more challenging images, and the possibility of confusion between classes with similar visual appearance (cars/bus, bicycle/motorbike).
As before participants can recognize any or all of the classes, and there is a classficiation and a detection track.
The development kit (Matlab code for evaluation, and baseline algorithms) and training data is now available at:
http://www.pascal-network.org/challenges/VOC/voc2006/index.html
where further details are given. The timetable of the challenge is included below.
It would be great if each of you or your groups could participate.
Best wishes,
Andrew Zisserman
Mark Everingham
Chris Williams
Luc Van Gool
TIMETABLE
* 14 Feb 2006 : Development kit (training and validation data plus evaluation software) made available.
* 31 March 2006: Test set made available
* 21 April 2006: DEADLINE for submission of results
* 7 May 2006: Half-day (afternoon) challenge workshop to be held in conjunction with ECCV06, Graz, Austria.
Date: Fri, 17 Feb 2006 20:32:18 GMT
From: Andrew Zisserman
Dear All,
We are running a second PASCAL Visual Object Classes Recognition Challenge. This time there are more classes (ten), more challenging images, and the possibility of confusion between classes with similar visual appearance (cars/bus, bicycle/motorbike).
As before participants can recognize any or all of the classes, and there is a classficiation and a detection track.
The development kit (Matlab code for evaluation, and baseline algorithms) and training data is now available at:
http://www.pascal-network.org/challenges/VOC/voc2006/index.html
where further details are given. The timetable of the challenge is included below.
It would be great if each of you or your groups could participate.
Best wishes,
Andrew Zisserman
Mark Everingham
Chris Williams
Luc Van Gool
TIMETABLE
* 14 Feb 2006 : Development kit (training and validation data plus evaluation software) made available.
* 31 March 2006: Test set made available
* 21 April 2006: DEADLINE for submission of results
* 7 May 2006: Half-day (afternoon) challenge workshop to be held in conjunction with ECCV06, Graz, Austria.
IEEE Career Alert: Tech Jobs Are Jumping
3. Start Up, Not at the Bottom
The latest trend in entry-level jobs is to avoid them altogether. More and more recent college graduates are heading start-up businesses, writes The Boston Globe. In fact, at high-powered schools like Harvard and Carnegie Mellon, thirty to forty percent of students create their own companies within five years of graduating. For students thinking of leaping to the top of their own corporate ladder, certain skills may come in handy. For one, they may have to learn to market themselves. More advice can be found at:
The latest trend in entry-level jobs is to avoid them altogether. More and more recent college graduates are heading start-up businesses, writes The Boston Globe. In fact, at high-powered schools like Harvard and Carnegie Mellon, thirty to forty percent of students create their own companies within five years of graduating. For students thinking of leaping to the top of their own corporate ladder, certain skills may come in handy. For one, they may have to learn to market themselves. More advice can be found at:
What's New @ IEEE in Signal Processing, February 2006
2. RADAR ON THE SCOPE OF "SIGNAL PROCESSING MAGAZINE" SPECIAL ISSUE
The latest issue of "IEEE Signal Processing Magazine" (v. 23, no. 1) includes a feature section on knowledge-based systems for adaptive radars. Topics covered include Knowledge-based systems for adaptive radar, cognitive radar, space-time adaptive processing as well as several others. The table of contents and abstracts for all articles are available online, where subscribers may also access the full text of all papers: http://ieeexplore.ieee.org/xpl/tocresult.jsp?isnumber=33529
Also now online, the latest issue of "IEEE Signal Processing Letters" (v. 13, no. 3), covering signal modification for ADPCM based on analysis-by-synthesis framework, a new gradient search interpretation of super-exponential algorithms among other topics: http://ieeexplore.ieee.org/xpl/tocresult.jsp?isnumber=33543
7. AIMING FOR MORE ACCURATE FISH POPULATION COUNTS
Many environmentalists and scientists believe the world's fish populations are shrinking, and new developments in signal processing technology seek to arm researchers with techniques that provide more accurate fish population data. Off the coast of Monterey, California, USA, a team of scientists demonstrated a new sonar technique to detect squid egg clusters in the ocean's depths. By towing a sidescan sonar with the California State University Seafloor Mapping Lab's research vessel, the team was able to conduct experiments that tested various ways to tune sound wave frequencies. After signals were drawn out, the sound data was translated into sonar images in the form of seafloor maps which displayed where egg clusters could be found, providing a portrayal of future populations. Meanwhile, researchers at the Massachusetts Institute of Technology have created a remote sensor system that allows scientists to monitor large fish populations over a 10,000-square-kilometer area. While old surveying methods provide a smaller amount of data with high-frequency sonar beams, this new system employs low-frequency sonar beams that can travel farther distances, bringing data back in sharper detail through less intense signals. Read more about these developments:
http://www.eurekalert.org/pub_releases/2006-02/miot-oft013006.php
& http://www.eurekalert.org/pub_releases/2006-02/whoi-nsm020706.php
10. WORLD'S FASTEST CAMERA TO CATCH TRACES OF ELUSIVE PARTICLE
The Regional Calorimeter Trigger, the world's fastest image processor, can analyze a billion proton collisions per second, according to its developers at the University of Wisconsin-Madison, and will be used in the Large Hadron Collider (LHC) in Geneva, Switzerland, to capture traces of the subatomic Higgs-Boson. The US$6 million device is composed of integrated circuits on 300 parallel processing computer cards, researchers say, creating a massive image processor capable of analyzing one trillion bits of data per second. The Higgs-Boson is one of the particles researchers say is necessary to complete the standard model of physics, the evidence for which has been sought for 20 years. When protons crash in a collider the event lasts no more than two-billionths of second, according to researchers. Read more:
http://www.physorg.com/news10589.html
and http://www.primidi.com/2006/02/08.html#a1436
The latest issue of "IEEE Signal Processing Magazine" (v. 23, no. 1) includes a feature section on knowledge-based systems for adaptive radars. Topics covered include Knowledge-based systems for adaptive radar, cognitive radar, space-time adaptive processing as well as several others. The table of contents and abstracts for all articles are available online, where subscribers may also access the full text of all papers: http://ieeexplore.ieee.org/xpl/tocresult.jsp?isnumber=33529
Also now online, the latest issue of "IEEE Signal Processing Letters" (v. 13, no. 3), covering signal modification for ADPCM based on analysis-by-synthesis framework, a new gradient search interpretation of super-exponential algorithms among other topics: http://ieeexplore.ieee.org/xpl/tocresult.jsp?isnumber=33543
7. AIMING FOR MORE ACCURATE FISH POPULATION COUNTS
Many environmentalists and scientists believe the world's fish populations are shrinking, and new developments in signal processing technology seek to arm researchers with techniques that provide more accurate fish population data. Off the coast of Monterey, California, USA, a team of scientists demonstrated a new sonar technique to detect squid egg clusters in the ocean's depths. By towing a sidescan sonar with the California State University Seafloor Mapping Lab's research vessel, the team was able to conduct experiments that tested various ways to tune sound wave frequencies. After signals were drawn out, the sound data was translated into sonar images in the form of seafloor maps which displayed where egg clusters could be found, providing a portrayal of future populations. Meanwhile, researchers at the Massachusetts Institute of Technology have created a remote sensor system that allows scientists to monitor large fish populations over a 10,000-square-kilometer area. While old surveying methods provide a smaller amount of data with high-frequency sonar beams, this new system employs low-frequency sonar beams that can travel farther distances, bringing data back in sharper detail through less intense signals. Read more about these developments:
http://www.eurekalert.org/pub_releases/2006-02/miot-oft013006.php
& http://www.eurekalert.org/pub_releases/2006-02/whoi-nsm020706.php
10. WORLD'S FASTEST CAMERA TO CATCH TRACES OF ELUSIVE PARTICLE
The Regional Calorimeter Trigger, the world's fastest image processor, can analyze a billion proton collisions per second, according to its developers at the University of Wisconsin-Madison, and will be used in the Large Hadron Collider (LHC) in Geneva, Switzerland, to capture traces of the subatomic Higgs-Boson. The US$6 million device is composed of integrated circuits on 300 parallel processing computer cards, researchers say, creating a massive image processor capable of analyzing one trillion bits of data per second. The Higgs-Boson is one of the particles researchers say is necessary to complete the standard model of physics, the evidence for which has been sought for 20 years. When protons crash in a collider the event lasts no more than two-billionths of second, according to researchers. Read more:
http://www.physorg.com/news10589.html
and http://www.primidi.com/2006/02/08.html#a1436
Wednesday, February 22, 2006
Fast Extrinsic Calibration of a Laser Rangefinder to a Camera
{Ranjith Unnikrishnan , Martial Hebert}
Abstract:
External calibration of a camera to a laser rangefinder is a common pre-requisiteon today’s multi-sensor mobile robot platforms. However, the process of doing sois relatively poorly documented and almost always time-consuming. This documentoutlines an easy and portable technique for external calibration of a camera to a laserrangefinder. It describes the usage of the Laser-Camera Calibration Toolbox (LCCT),a MatlabR -based graphical user interface that is meant to accompany this document andfacilitates the calibration procedure. We also summarize the math behind its development.
[Link]
Abstract:
External calibration of a camera to a laser rangefinder is a common pre-requisiteon today’s multi-sensor mobile robot platforms. However, the process of doing sois relatively poorly documented and almost always time-consuming. This documentoutlines an easy and portable technique for external calibration of a camera to a laserrangefinder. It describes the usage of the Laser-Camera Calibration Toolbox (LCCT),a MatlabR -based graphical user interface that is meant to accompany this document andfacilitates the calibration procedure. We also summarize the math behind its development.
[Link]
CMU VASC talk: Learning to Transform Time Series with a Few Examples
Ali Rahimi, Intel Lab Seattle
Monday, Feb 27, 2006
Abstract:
I describe a semi-supervised regression algorithm that learns to transform one time series into another time series given examples of the transformation. I apply this algorithm to tracking, where one transforms a time series of observations from sensors to a time series describing the pose of a target. Instead of defining and implementing such transformations for each tracking task separately, I suggest learning a memoryless transformations of time series from a few example input-output mappings. Our algorithm searches for a smooth function that fits the training examples and, when applied to the input time series, produces a time series that evolves according to assumed dynamics. The learning procedure is fast and lends itself to a closed-form solution. I relate this algorithm and its unsupervised extension to nonlinear system identification and manifold learning techniques. I demonstrate it on the tasks of tracking RFID tags from signal strength measurements, recovering the pose of rigid objects, deformable bodies, and articulated bodies from video sequences, and tracking a target in a completely uncalibrated network of sensors.
For these tasks, this algorithm requires significantly fewer examples compared to fully-supervised regression algorithms or semi-supervised learning algorithms that do not take the dynamics of the output time series into account.
Speaker Bio:
Ali Rahimi is interested in developing machine learning tools for solving difficult sensing problems. His focus is on example-based tracking, and efficient approximation methods for estimation. He received a PhD from the MIT Computer Science and AI Lab in 2005, a MS in Media Arts and Science from the MIT Media Lab, and a BS in Electrical Engineering and Computer Science from UC Berkeley.
Monday, Feb 27, 2006
Abstract:
I describe a semi-supervised regression algorithm that learns to transform one time series into another time series given examples of the transformation. I apply this algorithm to tracking, where one transforms a time series of observations from sensors to a time series describing the pose of a target. Instead of defining and implementing such transformations for each tracking task separately, I suggest learning a memoryless transformations of time series from a few example input-output mappings. Our algorithm searches for a smooth function that fits the training examples and, when applied to the input time series, produces a time series that evolves according to assumed dynamics. The learning procedure is fast and lends itself to a closed-form solution. I relate this algorithm and its unsupervised extension to nonlinear system identification and manifold learning techniques. I demonstrate it on the tasks of tracking RFID tags from signal strength measurements, recovering the pose of rigid objects, deformable bodies, and articulated bodies from video sequences, and tracking a target in a completely uncalibrated network of sensors.
For these tasks, this algorithm requires significantly fewer examples compared to fully-supervised regression algorithms or semi-supervised learning algorithms that do not take the dynamics of the output time series into account.
Speaker Bio:
Ali Rahimi is interested in developing machine learning tools for solving difficult sensing problems. His focus is on example-based tracking, and efficient approximation methods for estimation. He received a PhD from the MIT Computer Science and AI Lab in 2005, a MS in Media Arts and Science from the MIT Media Lab, and a BS in Electrical Engineering and Computer Science from UC Berkeley.
Tuesday, February 21, 2006
The Boosting Approach to Machine Learning
The Boosting Approach to Machine Learning
An Overview
Robert E. Schapire
AT&T Labs - Research
Shannon Laboratory
Abstract
Boosting is a general method for improving the accuracy of any given learning algorithm. Focusing primarily on the AdaBoost algorithm, this chapter overviews some of the recent work on boosting including analyses of AdaBoost’s training error and generalization error; boosting’s connection to game theory and linear programming; the relationship between boosting and logistic regression; extensions of AdaBoost for multiclass classification problems; methods of incorporating human knowledge into boosting; and experimental and applied work using boosting.
Here is the link
An Overview
Robert E. Schapire
AT&T Labs - Research
Shannon Laboratory
Abstract
Boosting is a general method for improving the accuracy of any given learning algorithm. Focusing primarily on the AdaBoost algorithm, this chapter overviews some of the recent work on boosting including analyses of AdaBoost’s training error and generalization error; boosting’s connection to game theory and linear programming; the relationship between boosting and logistic regression; extensions of AdaBoost for multiclass classification problems; methods of incorporating human knowledge into boosting; and experimental and applied work using boosting.
Here is the link
Monday, February 20, 2006
My talk this week (Casey)
My talk has below parts:
1.The related work: Robust Real-time Object Detection.(Author: Viola & Jones)
2.Detection approach of HandVu System
3.Tracking Approach of HandVu System
4.Recognition
The information of this paper:
It is in IEEE Intl. Conference on Automatic Face and Gesture Recognition, May 2004.
Robust Hand Detection
Mathias K¨olsch and Matthew Turk
Department of Computer Science, University of California, Santa Barbara, CA
Abstract
Vision-based hand gesture interfaces require fast and extremely
robust hand detection. Here, we study view-specic
hand posture detection with an object recognition method
recently proposed by Viola and Jones. Training with this
method is computationally very expensive, prohibiting the
evaluation of many hand appearances for their suitability
to detection. As one contribution of this paper, we present a
frequency analysis-based method for instantaneous estimation
of class separability, without the need for any training.
We built detectors for the most promising candidates, their
receiver operating characteristics conrming the estimates.
Next, we found that classication accuracy increases with
a more expressive feature type. As a third contribution, we
show that further optimization of training parameters yields
additional detection rate improvements. In summary, we
present a systematic approach to building an extremely robust
hand appearance detector, providing an important step
towards easily deployable and reliable vision-based hand
gesture interfaces.
And Below is the autor's Ph.D thesis, "Vision Based Hand Gesture Interfaces for Wearable Computing and Virtual Environments"
You can download these two paper and get the author's information from this link: http://www.movesinstitute.org/~kolsch/publications.html
1.The related work: Robust Real-time Object Detection.(Author: Viola & Jones)
2.Detection approach of HandVu System
3.Tracking Approach of HandVu System
4.Recognition
The information of this paper:
It is in IEEE Intl. Conference on Automatic Face and Gesture Recognition, May 2004.
Robust Hand Detection
Mathias K¨olsch and Matthew Turk
Department of Computer Science, University of California, Santa Barbara, CA
Abstract
Vision-based hand gesture interfaces require fast and extremely
robust hand detection. Here, we study view-specic
hand posture detection with an object recognition method
recently proposed by Viola and Jones. Training with this
method is computationally very expensive, prohibiting the
evaluation of many hand appearances for their suitability
to detection. As one contribution of this paper, we present a
frequency analysis-based method for instantaneous estimation
of class separability, without the need for any training.
We built detectors for the most promising candidates, their
receiver operating characteristics conrming the estimates.
Next, we found that classication accuracy increases with
a more expressive feature type. As a third contribution, we
show that further optimization of training parameters yields
additional detection rate improvements. In summary, we
present a systematic approach to building an extremely robust
hand appearance detector, providing an important step
towards easily deployable and reliable vision-based hand
gesture interfaces.
And Below is the autor's Ph.D thesis, "Vision Based Hand Gesture Interfaces for Wearable Computing and Virtual Environments"
You can download these two paper and get the author's information from this link: http://www.movesinstitute.org/~kolsch/publications.html
Thursday, February 16, 2006
What's New @ IEEE in Computing, February 2006
4. ALGORITHMS TO AID DEVELOPMENT OF PATTERN RECOGNITION SOFTWARE DEVELOPED
Departing from traditional approaches to promoting the development of pattern recognition software, researchers at Ohio State University have created a new method that tests machine vision algorithms to evaluate which algorithm is most successful for a given application. Using two databases, one consisting of objects such as apples and pears and another of faces with various expressions, the researchers found the tasks of sorting objects and identifying expressions to be distinct in such a way that an algorithm could be good at doing one but not the other. The end result allows for a faster, more efficient way to gather data from pattern recognition software, according to Aleix Martinez, assistant professor of electrical and computer engineering at Ohio State. This work may have an affect on research in areas as varied as neuroscience, genetics, and economics. Read more:
http://www.eurekalert.org/pub_releases/2006-01/osu-anw012406.php
5. INTELLIGENT TRANSPORTATION PAPERS NEEDED
The IEEE Pervasive Computing Magazine has announced a call for papers for a special issue on intelligent transportation. Authors are asked to submit articles describing the application of pervasive computing technologies, systems, and applications to vehicles, roads, and other transportation systems. Also encouraged are articles that discuss the security, privacy, social, and human-related issues of intelligent transportation, and case studies of experiences with existing pervasive technologies in use in transportation. Deadline for submission is 31 May 2006. For details, visit:
http://www.computer.org/portal/pages/pervasive/content/cfp4.html
Departing from traditional approaches to promoting the development of pattern recognition software, researchers at Ohio State University have created a new method that tests machine vision algorithms to evaluate which algorithm is most successful for a given application. Using two databases, one consisting of objects such as apples and pears and another of faces with various expressions, the researchers found the tasks of sorting objects and identifying expressions to be distinct in such a way that an algorithm could be good at doing one but not the other. The end result allows for a faster, more efficient way to gather data from pattern recognition software, according to Aleix Martinez, assistant professor of electrical and computer engineering at Ohio State. This work may have an affect on research in areas as varied as neuroscience, genetics, and economics. Read more:
http://www.eurekalert.org/pub_releases/2006-01/osu-anw012406.php
5. INTELLIGENT TRANSPORTATION PAPERS NEEDED
The IEEE Pervasive Computing Magazine has announced a call for papers for a special issue on intelligent transportation. Authors are asked to submit articles describing the application of pervasive computing technologies, systems, and applications to vehicles, roads, and other transportation systems. Also encouraged are articles that discuss the security, privacy, social, and human-related issues of intelligent transportation, and case studies of experiences with existing pervasive technologies in use in transportation. Deadline for submission is 31 May 2006. For details, visit:
http://www.computer.org/portal/pages/pervasive/content/cfp4.html
Tuesday, February 14, 2006
MIT Report : Learning Semantic Scene Models by Trajectory Analysis
Authors
Xiaogang Wang, Kinh Tieu, Eric Grimson
Abstract
In this paper, we describe an unsupervised learning framework to segment a scene into semantic regions and to build semantic scene models from long-term observations of moving objects in the scene. First, we introduce two novel similarity measures for comparing trajectories in far-field visual surveillance. The measures simultaneously compare the spatial distribution of trajectories and other attributes, such as velocity and object size, along the trajectories. They also provide a comparison confidence measure which indicates how well the measured image-based similarity approximates true physical similarity. We also introduce novel clustering algorithms which use both similarity and comparison confidence. Based on the proposed similarity measures and clustering methods, a framework to learn semantic scene models by trajectory analysis is developed. Trajectories are first clustered into vehicles and pedestrians, and then further grouped based on spatial and velocity distributions. Different trajectory clusters represent different activities. The geometric and statistical models of structures in the scene, such as roads, walk paths, source and sinks, are automatically learned from the trajectory clusters. Abnormal activities are detected using the semantic scene models. The system is robust to low-level tracking errors.
Link
Xiaogang Wang, Kinh Tieu, Eric Grimson
Abstract
In this paper, we describe an unsupervised learning framework to segment a scene into semantic regions and to build semantic scene models from long-term observations of moving objects in the scene. First, we introduce two novel similarity measures for comparing trajectories in far-field visual surveillance. The measures simultaneously compare the spatial distribution of trajectories and other attributes, such as velocity and object size, along the trajectories. They also provide a comparison confidence measure which indicates how well the measured image-based similarity approximates true physical similarity. We also introduce novel clustering algorithms which use both similarity and comparison confidence. Based on the proposed similarity measures and clustering methods, a framework to learn semantic scene models by trajectory analysis is developed. Trajectories are first clustered into vehicles and pedestrians, and then further grouped based on spatial and velocity distributions. Different trajectory clusters represent different activities. The geometric and statistical models of structures in the scene, such as roads, walk paths, source and sinks, are automatically learned from the trajectory clusters. Abnormal activities are detected using the semantic scene models. The system is robust to low-level tracking errors.
Link
MIT talk: Object Class and Subclass Recognition Using Relational Object Models
Speaker: Aharon Bar-Hillel , Hebrew University
Date: Wednesday, February 15 2006
Host: Prof. Tomaso Poggio, M.I.T., McGovern Institute, BCS & CSAIL
Abstract: In the first part of the talk I will present a new learning method for object class recognition, combining a generative constellation model with a discriminative optimization technique. Specifically we use a 'star'-like Bayesian network model, but learn its parameters using an extended boosting technique which iterates between inference and part learning. Learning complexity is linear in the number of model parts and image features, compared to an exponential learning complexity for similar models in a generative framework. This allows the construction of rich models with many distinctive parts, leading to improved classification accuracy.
In the second part of the talk I will address the problem of sub-ordinate class recognition (like the distinction between cross and sport motorcycles), relying on the above-mentioned learning technique. Our approach to this problem is motivated by observations from cognitive psychology, which identify parts as the defining component of basic level categories, while sub-ordinate categories are more often defined by modified parts. Accordingly, we suggest a two-stage algorithm: First a model of the inclusive class is learned (e.g., motorcycles in general) using the technique introduced earlier, and then subclass classification is made based on the part correspondence implied by the model. The two-stage algorithm typically outperforms a competing one-step algorithm, which builds distinct constellation models for each subclass. This performance advantage critically relies on modeling of the spatial relations between parts, and on having models with a large number of parts.
The talk is based on a joint work with Tomer Hertz and Prof. Daphna Weinshall.
Date: Wednesday, February 15 2006
Host: Prof. Tomaso Poggio, M.I.T., McGovern Institute, BCS & CSAIL
Abstract: In the first part of the talk I will present a new learning method for object class recognition, combining a generative constellation model with a discriminative optimization technique. Specifically we use a 'star'-like Bayesian network model, but learn its parameters using an extended boosting technique which iterates between inference and part learning. Learning complexity is linear in the number of model parts and image features, compared to an exponential learning complexity for similar models in a generative framework. This allows the construction of rich models with many distinctive parts, leading to improved classification accuracy.
In the second part of the talk I will address the problem of sub-ordinate class recognition (like the distinction between cross and sport motorcycles), relying on the above-mentioned learning technique. Our approach to this problem is motivated by observations from cognitive psychology, which identify parts as the defining component of basic level categories, while sub-ordinate categories are more often defined by modified parts. Accordingly, we suggest a two-stage algorithm: First a model of the inclusive class is learned (e.g., motorcycles in general) using the technique introduced earlier, and then subclass classification is made based on the part correspondence implied by the model. The two-stage algorithm typically outperforms a competing one-step algorithm, which builds distinct constellation models for each subclass. This performance advantage critically relies on modeling of the spatial relations between parts, and on having models with a large number of parts.
The talk is based on a joint work with Tomer Hertz and Prof. Daphna Weinshall.
MIT Thesis Defense: Learning a Dictionary of Shape-Components in Visual Cortex: Comparison with Neurons, Humans and Machine
Speaker: Thomas Serre , Dept. of Brain & Cognitive Sciences and McGovern Institute for Brain Research
Date: Wednesday, February 15 2006
Host: Prof. Tomaso Poggio, McGovern Institute for Brain Research
Relevant URL: http://web.mit.edu/serre/www/
In this talk I will describe a quantitative model that accounts for the circuits and computations of the feedforward path of the ventral stream of visual cortex. This model is consistent with a general theory of visual processing that extends the hierarchical model of Hubel & Wiesel from primary to extrastriate visual areas and attempts to explain the first few hundred milliseconds of visual processing. One of the key elements in the approach I will describe is the learning of a generic dictionary of shape-components from V2 to IT, which provides an invariant representation to task-specific categorization circuits in higher brain areas. This vocabulary of shape-tuned units is learned in an unsupervised manner from natural images, and constitutes a large and redundant set of image features with different complexities and invariances. This theory significantly extends an earlier approach by Riesenhuber & Poggio (1999) and builds upon several existing neurobiological models and conceptual proposals.
I will present evidence to show that not only can the model duplicate the tuning properties of neurons in various brain areas when probed with artificial stimuli (like the ones typically used in physiology), but it can also handle the recognition of objects in the real-world, to the extent of competing with the best computer vision systems. Following this, I will present a comparison between the performance of the model and the performance of human observers in a rapid animal vs. non-animal recognition task for which recognition is fast and cortical back-projections are likely to be inactive. Results indicate that the model predicts human performance extremely well when the delay between the stimulus and the mask is about 50 ms. These results suggest that cortical back-projections may not play a significant role when the time interval is in this range, and the model may therefore provide a satisfactory description of the feedforward path.
Taken together, the evidence I will present shows that we may have the skeleton of a successful theory of visual cortex. In addition, this may be the first time that a neurobiological model, faithful to the physiology and the anatomy of visual cortex, not only competes with some of the best computer vision systems thus providing a realistic alternative to engineered artificial vision systems, but also achieves performance close to that of humans in a categorization task involving complex natural images.
Date: Wednesday, February 15 2006
Host: Prof. Tomaso Poggio, McGovern Institute for Brain Research
Relevant URL: http://web.mit.edu/serre/www/
In this talk I will describe a quantitative model that accounts for the circuits and computations of the feedforward path of the ventral stream of visual cortex. This model is consistent with a general theory of visual processing that extends the hierarchical model of Hubel & Wiesel from primary to extrastriate visual areas and attempts to explain the first few hundred milliseconds of visual processing. One of the key elements in the approach I will describe is the learning of a generic dictionary of shape-components from V2 to IT, which provides an invariant representation to task-specific categorization circuits in higher brain areas. This vocabulary of shape-tuned units is learned in an unsupervised manner from natural images, and constitutes a large and redundant set of image features with different complexities and invariances. This theory significantly extends an earlier approach by Riesenhuber & Poggio (1999) and builds upon several existing neurobiological models and conceptual proposals.
I will present evidence to show that not only can the model duplicate the tuning properties of neurons in various brain areas when probed with artificial stimuli (like the ones typically used in physiology), but it can also handle the recognition of objects in the real-world, to the extent of competing with the best computer vision systems. Following this, I will present a comparison between the performance of the model and the performance of human observers in a rapid animal vs. non-animal recognition task for which recognition is fast and cortical back-projections are likely to be inactive. Results indicate that the model predicts human performance extremely well when the delay between the stimulus and the mask is about 50 ms. These results suggest that cortical back-projections may not play a significant role when the time interval is in this range, and the model may therefore provide a satisfactory description of the feedforward path.
Taken together, the evidence I will present shows that we may have the skeleton of a successful theory of visual cortex. In addition, this may be the first time that a neurobiological model, faithful to the physiology and the anatomy of visual cortex, not only competes with some of the best computer vision systems thus providing a realistic alternative to engineered artificial vision systems, but also achieves performance close to that of humans in a categorization task involving complex natural images.
CMU VASC talk: Video visualization - Beyond pixels and frames
Yaron Capsi, Tel Aviv University
Monday, Feb 20, 2006
Abstract: Video data is represented by pixels and frames. This restricts the way it is captured, accessed and visualized. On one hand, visual information is distributed across all frames, and therefore, in order to depict the visual information, the entire video sequence must be viewed sequentially, frame by frame. On the other hand, important visual information is lost by the limited frame rate. Similarly in the spatial domain, sensor and optics limit the capturing process, while huge redundancy prevents an efficient visualization of information. In this talk I will show how to exceed both limitations of capturing devices and of visual displays. In particular, how fusion of information from multiple sources allows to exceed temporal and spatial limitations, and how visualization of video data can benefit from importance ranking. I will describe a process that depicts the essence of video or animation, by embedding high dimensional data in low dimensional Euclidean space. I will also show how super-pixels (in contrast to pixels) contribute to the exploitation of temporal redundancy for the task of spatial segmentation of regions with high importance.
Monday, Feb 20, 2006
Abstract: Video data is represented by pixels and frames. This restricts the way it is captured, accessed and visualized. On one hand, visual information is distributed across all frames, and therefore, in order to depict the visual information, the entire video sequence must be viewed sequentially, frame by frame. On the other hand, important visual information is lost by the limited frame rate. Similarly in the spatial domain, sensor and optics limit the capturing process, while huge redundancy prevents an efficient visualization of information. In this talk I will show how to exceed both limitations of capturing devices and of visual displays. In particular, how fusion of information from multiple sources allows to exceed temporal and spatial limitations, and how visualization of video data can benefit from importance ranking. I will describe a process that depicts the essence of video or animation, by embedding high dimensional data in low dimensional Euclidean space. I will also show how super-pixels (in contrast to pixels) contribute to the exploitation of temporal redundancy for the task of spatial segmentation of regions with high importance.
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