Monday, June 30, 2008

Sunday, June 29, 2008

[Lab Meeting] June 30th, 2008 (Ekker) :Intelligent Shoes for Abnormal Gait Detection

Intelligent Shoes for Abnormal Gait Detection
Meng Chen, Bufu Huang, and Yangsheng Xu
2008 IEEE International Conference on
Robotics and Automation
Pasadena, CA, USA, May 19-23, 2008

Abstract—In this paper we introduce a shoe-integrated
system for human abnormal gait detection. This intelligent
system focuses on detecting the following patterns: normal gait,
toe in, toe out, oversupination, and heel walking gait abnormalities.
An inertial measurement unit (IMU) consisting of
three-dimensional gyroscopes and accelerometers is employed
to measure angular velocities and accelerations of the foot. Four
force sensing resistors (FSRs) and one bend sensor are installed
on the insole of each foot for force and flexion information
acquisition. The proposed detection method is mainly based
on Principal Component Analysis (PCA) for feature generation
and Support Vector Machine (SVM) for multi-pattern
classification. In the present study, four subjects tested the
shoe-integrated device in outdoor environments. Experimental
results demonstrate that the proposed approach is robust and
efficient in detecting abnormal gait patterns. Our goal is to
provide a cost-effective system for detecting gait abnormalities
in order to assist persons with abnormal gaits in the developing
of a normal walking pattern in their daily life.

Lab Meeting June 30th, 2008 (Hero) : Path and Trajectoty Diversity:Theory and Algorithms

Path and Trajectory Diversity: Theory and Algorithms

Michael S. Branicky Ross A. Knepper† James J. Kuffner†

2008 IEEE International Conference on
Robotics and Automation
Pasadena, CA, USA, May 19-23, 2008

We present heuristic algorithms for pruning large sets of candidate paths or trajectories down to smaller subsets that maintain desirable characteristics in terms of overall reachability and path length. Consider the example of a set of candidate paths in an environment that is the result of a forward search tree built over a set of actions or behaviors. The tree is precomputed and stored in memory to be used online to compute collision-free paths from the root of the tree to a particular goal node. In general, such a set of paths may be quite large, growing exponentially in the depth of the search tree. In practice, however, many of these paths may be close together and could be pruned without a loss to the overall problem of path-finding. The best such pruning for a given resulting tree size is the one that maximizes path diversity, which is quantified as the probability of the survival of paths, averaged over all possible obstacle environments. We formalize this notion and provide formulas for computing it exactly. We also present experimental results for two approximate algorithms for path set reduction that are efficient and yield desirable properties in terms of overall path diversity. The exact formulas and approximate algorithms generalize to the computation and maximization of spatio-temporal diversity for trajectories.

Monday, June 23, 2008

Lab Meeting June 23rd, 2008 (Der-Yeuan Yu)

I will show some progress on matching 3D laser scans based on image features.

Lab Meeting June 23rd, 2008 (KuoHwei Lin)

I will report my progress on SLAMMOT and show some results.

Sunday, June 22, 2008

Lab Meeting June 23rd, 2008 (Stanley)

I will give a (short?) progress report.

Friday, June 20, 2008

News: Google is doing 3D city modeling?

These two pictures are taken in Italy and San Francisco, respectively. As you can see, several SICK laser scanners are mounted. There are two side-facing vertical scanners, and another forward-facing horizontal scanner. So, what is Google doing with 3D laser data? The obvious application is 3D reconstruction for Google Earth.

Educating Silicon - Google Street View - Soon in 3D?
Engadget - Google 工程車被開單

New Algorithms for Feature-Based 2d and 3d Registration

VASC Seminar (Monday, June 23, 3:30pm-4:30pm, NSH 1507)

Charles V. Stewart
Rensselaer Polytechnic Institute and DualAlign, LLC

This talk presents a series of algorithms for feature-based registration. The Dual-Bootstrap approach to registration "grows" inter-image transformations by starting with single-keypoint matching in small image regions. It has been used to develop highly-successful algorithms for 2d-to-2d image registration, 3d-to-3d LiDAR scan registration, and the 3d-to-2d problem of determining the location of a camera with respect to a 3d model. The Dual-Bootstrap is now undergoing commercial development for a wide-variety of applications. More recently, the Location Registration and Recognition (LRR) algorithm has been developed as an aid to longitudinal diagnosis and treatment monitoring, particularly for lung cancer. Rather than applying deformable registration, clinical regions of interest in one CT scan (such as small volumes surrounding nodules) are automatically recognized and aligned in a second CT scan. Like the Dual-Bootstrap, LRR uses a combination of keypoint indexing, (local) feature-based refinement, and learned decision criteria. LRR works at near interactive speeds and is (slightly) more accurate than the best current deformable registration technique.

Charles V. Stewart is a professor in the Department of Computer Science, Rensselaer Polytechnic Institute, Troy, New York. He has done sabbaticals at the GE Center for Research and Development in Niskayuna, New York, and at the Johns Hopkins University. In 1999, together with Ali Can and Badrinath Roysam, he received the Best Paper Award at the IEEE Conference on Computer Vision and Pattern Recognition (CVPR). In 2007, he founded DualAlign LLC, where he is currently working as chief scientist while on leave from Rensselaer.

RPIs Computer Vision group

News: Audio processing is on the move

There is a quiet revolution underway in audio processing - although it's quiet only in that the developments are mainly aimed at eliminating troublesome noise and echos. To find out more, we talked to a few of the companies leading the charge.

It may seem counter-intuitive, but as audio headsets get more and more clever, their users are unlikely to hear the improvements. That's because the work is mostly aimed at improving the outbound sound quality via techniques such as noise cancellation - although headset users should see benefits in areas such as voice dialling or speech recognition.

To make those improvements, modern headsets increasingly use multiple microphones, plus powerful digital signal processors (DSPs) to re-work the sounds thus collected.

For example, Blue Ant's Z9 Bluetooth headset has two microphones, plus a DSP which uses the signals to measure the distance to the sound source and thereby triangulate on the mouth, says Taisen Maddern, the company's CEO.

Maddern notes that as devices such as this are essentially software-driven, they can also be upgraded as new and better algorithms come along. He points for example to the emerging wideband speech profile for Bluetooth, which will allow headsets to support 3G's broader audio spectrum.

He adds that as developers look to make Bluetooth easier to use, voice control is an obvious possibility - but it's one that relies upon a clear audio signal.

"We have a headset coming that will be the first voice-command headset that talks you through the process of pairing," he says. "You can use voice commands to request help, check the battery level, make calls."

It's not just audio that can be collected and made use of, adds Alex Affely, the CTO of Aliph, the company behind the Jawbone headset.

As its name suggests, the Jawbone not only picks up exterior and spoken sound, it also has a third microphone which "taps into your jaw vibration," says Affely.

Wireless has really given audio technology a big boost, he reckons. He says that while some of the work behind Jawbone dates back to the early 1990s and the First Gulf War, when it was realised that the wired headsets used by soldiers needed better noise cancellation, it wasn't until Bluetooth came into focus a few years ago that it really took off.

As well as noise and echo cancellation, developers are also taking advantage of research into areas such as pattern recognition, says Jennifer Stagnaro, the marketing VP at Audience, which develops voice processing technology for integration into mobile phones.

"We have reverse-engineered the human auditory system, using an optimised DSP chip and software. Most past technologies could cancel stationary noise, we can cancel non-stationary noise," she claims.

Audience's voice processor includes algorithms based on research into auditory scene analysis - a complex technique for taking a mixture of sounds and sorting them into packages, each of which probably has the same source. Other useful techniques for signal selectivity include beamforming and blind signal separation, she says.

One advantage of the DSP-and-algorithm approach is that it can be position-neutral, so it can be used in handsets as well as headsets, she argues.

"The challenge in the handset is bigger," Stagnaro says. "With a headset you can do [bone] conduction pick-up. Ours still uses two microphones, but is position-neutral so it works in speakerphone mode, for example."

But whether the technology's in the headset or the handset, it still only cleans up the outgoing audio, notes Alex Affely.

He adds, provocatively: "What about incoming sound? That's one possibility for the future, it's one of the interesting things out there."


Thursday, June 12, 2008

IEEE news: Intelligent Computers See Your Human Traits

Intelligent Computers See Your Human Traits
In order to make human-computer interaction more natural and friendly, computer engineers are currently working on a way to give computers a more personal touch. By combining audio and visual data, Yongjin Wang, University of Toronto, Ontario, Canada and Ling Guan, Ryerson University, Toronto, Ontario, Canada, have developed a system that recognizes six human emotional states: happiness, sadness, anger, fear, surprise and disgust. "Human-centered computing focuses on understanding humans, including recognition of face, emotions, gestures, speech, body movements, etc.," said Wang. "Emotion recognition systems help the computer to understand the affective state of the user, and hence the computer can respond accordingly based on that perception." Their system can recognize emotions in people from different cultures and who speak different languages with a success rate of 82%. Read more Learn more about human-computer interaction in IEEE Xplore®

IEEE Spectrum Special Issue: The Singularity

Hi Folks, you have to check out this issue of IEEE Spectrum. -Bob

Spectrum Special Issue: The Singularity
Be sure to read the special June issue of IEEE Spectrum, which focuses on the singularity and how continuous technological innovation in artificial intelligence could one day outstrip human brain power, changing life as we know it. The special report touches on a wide variety of singularity arguments, including how to create consciousness if we do not know what it really is; thoughts on reversing the human brain; and whether or not it is possible to escape death by uploading our minds into machines. The issue also includes a "Who's Who in The Singularity" feature, outlining all the inventors, researchers, academics and authors who have had something to say about the controversial topic. Read more

Monday, June 09, 2008

The meeting tomorrow afternoon

As Bob has a meeting at noon, all meetings (afternoon) will be postponed one hour.

Lab Meeting June 9th, 2008 (Yu-chun): GUMSAWS: A Generic User Modeling Server for Adaptive Web Systems

Communication Networks and Services Research, 2007

Author: Jie Zhang and Ali A. Ghorbani

In this paper we focus on the architecture, design and implementation of a generic user modeling server for adaptive web systems (GUMSAWS), reaching the goals of generality, extendability and replaceability. GUMSAWS acts as a centralized user modeling server to assist several adaptive web systems (possibly in different domains) concurrently. It incrementally builds up user models, provides functions of storing, updating and deleting entries in user profiles, and maintains consistency of user models. Our system is also able to infer missing entries in user profiles from different information sources, including direct information, groups information, association rules and general facts. We further evaluate its inference performance within the context of e-commerce. Experimental results show that the average accuracy of inferring user missing property values from different information resources is found to be almost 70%. We also use a personalized electronic news system to demonstrate the example of our system in use.


Sunday, June 08, 2008

News:Scientists Figure Out Human Mobility Patterns from Cell phone Signals

Researchers in Boston tracked 100,000 people by their cellphone signals for six months to determine characteristics of how we get around to determine what underlies our travel patterns. The results show that we are a very predictable bunch, and that should be good news to urban planners, epidemiologists, and traffic analysts.


Lab Meeting June 9th, 2008 (Yu-Hsiang) : Trajectory Analysis and Semantic Region Modeling Using A Nonparametric Bayesian Model

Computer Vision and Pattern Recognition 2008


Xiaogang Wang, Keng Teck Ma, Gee-Wah Ng, Eric Grimson


We propose a novel nonparametric Bayesian model, Dual Hierarchical Dirichlet Processes (Dual-HDP), for trajectory analysis and semantic region modeling in surveillance settings, in an unsupervised way. In our approach, trajectories are treated as documents and observations of an object on a trajectory are treated as words in a document. Trajectories are clustered into different activities. Abnormal trajectories are detected as samples with low likelihoods. The semantic regions,which are intersections of paths commonly taken by objects, related to activities in the scene are also modeled. Dual-HDP advances the existing Hierarchical Dirichlet Processes (HDP) language model. HDP only clusters co-occurring words fromdocuments into topics and automatically decides the number of topics. Dual-HDP co-clusters both words and documents.It learns both the numbers of word topics and document clusters from data. Under our problem settings, HDP only clusters observations of objects, while Dual-HDP clusters both observations and trajectories. Experiments are evaluated on two datasets, radar tracks collected from a maritime port and visual tracks collected from a parking lot.


Lab Meeting June 9th, 2008 (Jeff):Progress report

I will show the dataset I get from PAL3 for achieving SLAM.

And try to describe PAL3 motion model.

Monday, June 02, 2008

[Lab Meeting] June 2nd, 2008 (Leo): Tracking Interacting Targets with Laser Scanner via On-line Supervised Learning

Authors: Xuan Song, Jinshi Cui, Xulei Wang, Huijing Zhao and Hongbin Zha

From: ICRA 2008

Abstract: Successful multi-target tracking requires
locating the targets and labeling their identities. For the laser
based tracking system, the latter becomes significantly more
challenging when the targets frequently interact with each
other. This paper presents a novel on-line supervised learning
based method for tracking interacting targets with laser
scanner. When the targets do not interact with each other, we
collect samples and train a classifier for each target. When the
targets are in close proximity, we use these classifiers to assist
in tracking. Different evaluations demonstrate that this
method has a better tracking performance than previous
methods when interactions occur, and can maintain correct
tracking under various complex tracking situations.

The pdf version can be found on our FTP server.