Lab Meeting April 20th, 2010 (Jeff): iSAM: Incremental Smoothing and Mapping

Title: iSAM: Incremental Smoothing and Mapping

Authors: Michael Kaess, Ananth Ranganathan, and Frank Dellaert

Abstract:

In this paper, we present incremental smoothing and mapping (iSAM), which is a novel approach to the simultaneous localization and mapping problem that is based on fast incremental matrix factorization. iSAM provides an efficient and exact solution by updating a QR factorization of the naturally sparse smoothing information matrix, thereby recalculating only those matrix entries
that actually change. iSAM is efficient even for robot trajectories with many loops as it avoids unnecessary fill-in in the factor matrix by periodic variable reordering. Also, to enable data association in real time, we provide efficient algorithms to access the estimation uncertainties of interest based on the factored information matrix. We systematically evaluate the different components of iSAM as well as the overall algorithm using various simulated and realworld
datasets for both landmark and pose-only settings.

Link:
IEEE Transactions on Robotics, Vol. 24, No. 6, December 2008:1365-1378
http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=4682731

News: Grand Challenges of Science: Robotics

In January, DISCOVER and the National Science Foundation continued their Grand Challenges event series with a panel discussion at Carnegie Mellon University exploring the dynamic world of robotics.

The panel included four eminent roboticists—Javier Movellan from University of California San Diego, Rodney Brooks from the Massachussetts Institute of Technology, William "Red" Whittaker from Carnegie Mellon University, and Robyn Murphy from Texas A&M University—who discussed some the big questions on the future of their field: How will robots transform industry, health care, and warfare? Will they ever be our equals? The conversation was moderated by DISCOVER editor-in-chief Corey Powell.

See the video

Lab Meeting April 13, 2010 (Alan) - Information-Based Compact Pose SLAM (T-RO 2010)

Title: Information-Based Compact Pose SLAM

Authors: Viorela Ila, Josep M. Porta, and Juan Andrade-Cetto, Member, IEEE

IEEE TRANSACTIONS ON ROBOTICS, VOL. 26, NO. 1, FEBRUARY 2010

Abstract—Pose SLAMis the variant of simultaneous localization and map building (SLAM) is the variant of SLAM, in which only the robot trajectory is estimated and where landmarks are only used to produce relative constraints between robot poses. To reduce the computational cost of the information filter form of PoseSLAM and, at the same time, to delay inconsistency as much as possible, we introduce an approach that takes into account only highly informative loop-closure links and nonredundant poses. This approach includes constant time procedures to compute the distance between poses, the expected information gain for each potential link, and the exact marginal covariances while moving in open loop, as well as a procedure to recover the state after a loop closure that, in practical situations, scales linearly in terms of both time and memory. Using these procedures, the robot operates most of the time in open loop, and the cost of the loop closure is amortized over long trajectories. This way, the computational bottleneck shifts to data association, which is the search over the set of previously visited poses to determine good candidates for sensor registration. To speed up data association, we introduce a method to search for neighboring poses whose complexity ranges from logarithmic in the usual case to linear in degenerate situations. The method is based on organizing the pose information in a balanced tree whose internal levels are defined using interval arithmetic. The proposed Pose-SLAM approach is validated through simulations, real mapping sessions, and experiments using standard SLAM data sets.

Link

Researchers develop a robot that folds towels

More than a household convenience, the project is a breakthrough in the robotic manipulation of non-rigid objects

Check out their ICRA 2010 paper.

Lab Meeting 3/29, 2010 (swem): MonoSLAM: Real-Time Single Camera SLAM

MonoSLAM: Real-Time Single Camera SLAM

Andrew J. Davison, Ian D. Reid, Member, IEEE, Nicholas D. Molton, and Olivier Stasse, Member, IEEE

IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE, VOL. 29, NO. 6, JUNE 2007

[link]

Abstract—We present a real-time algorithm which can recover the 3D trajectory of a monocular camera, moving rapidly through a

previously unknown scene. Our system, which we dub MonoSLAM, is the first successful application of the SLAM methodology from

mobile robotics to the “pure vision” domain of a single uncontrolled camera, achieving real time but drift-free performance inaccessible

to Structure from Motion approaches. The core of the approach is the online creation of a sparse but persistent map of natural

landmarks within a probabilistic framework. Our key novel contributions include an active approach to mapping and measurement, the

use of a general motion model for smooth camera movement, and solutions for monocular feature initialization and feature orientation

estimation. Together, these add up to an extremely efficient and robust algorithm which runs at 30 Hz with standard PC and camera

hardware. This work extends the range of robotic systems in which SLAM can be usefully applied, but also opens up new areas. We

present applications of MonoSLAM to real-time 3D localization and mapping for a high-performance full-size humanoid robot and live

augmented reality with a hand-held camera.

NTU talk: Image-Based Mobile Robot Navigation Design

Title: Image-Based Mobile Robot Navigation Design
Speaker: Prof. Kai-Tai Song, National Chiao Tung University
Time: 2:20pm, April 2 (Fri), 2010
Place: Room 103, CSIE Building


Abstract: Autonomous navigation is a basic function of every mobile robot. It is important for a mobile robot to travel and arrive at a desired location autonomously. In order to accomplish this function, the robot needs to observe its surroundings and locate itself through acquired environmental information. Various sensors have been utilized for acquiring information from environment. Among these perception sensors, image-based systems are most promising for practical application in daily-life scenarios. In this talk, I will first give a general perspective of domestic and service robots today. Then the design issues of mobile robot navigation will be discussed. Two approaches to visual navigation design will be presented. One employs an omni-directional camera, the other uses a normal webcam. In this presentation, the design and implementation of the navigation system will be discussed. Some interesting video clips of autonomous mobile robots will be presented.

Short Biography: Kai-Tai Song received his Ph.D. degree in mechanical engineering from Katholieke Universiteit Leuven, Belgium in 1989. Since 1989 he has been a faculty member and is currently a Professor with National Chiao Tung University (NCTU). From 2007 to 2009, he served as the Associate Dean of the R & D Office of NCTU. He is currently the Director of Institute of Electrical and Control Engineering of NCTU. He has served as the Chairman of IEEE Robotics & Automation Chapter, Taipei Section in the term of 1999. His is a directorate of Robotics Association Taiwan, Taiwan Robotics Society, Taiwan Association of System Science and Engineering, and Chinese Automatic Control Society.
His areas of research interest include mobile robotics, image processing, visual tracking, human-robot interaction, and mechatronics.

Lab Meeting 3/23, 2010 (fish60): Learning to Search: Functional Gradient Techniques for Imitation Learning

I will try to present this one:

Learning to Search: Functional Gradient Techniques for Imitation Learning
Nathan Ratliff, David Silver, J. Andrew Bagnell

Submitted to Autonomous Robotics Special Issue on Robot Learning, 2009

[download draft]

Abstract:
While planning algorithms have shown success in many real-world applications ranging from legged locomotion to outdoor unstructured navigation, such algorithms rely on fully specified cost functions that map sensor readings and environment models to quantifiable costs. Such cost functions are usually manually designed and programmed. Recently, a set of techniques has been developed that explore learning these functions from expert human demonstration. These algorithms apply an inverse optimal control approach to find a cost function for which planned behavior mimics an expert's demonstration.
The work we present extends the Maximum Margin Planning (MMP) frame- work to admit learning of more powerful, non-linear cost functions. These algorithms, known collectively as LEARCH (LEArning to seaRCH ), are simpler to implement than most existing methods, more efficient than previous attempts at non-linearization, more naturally satisfy common constraints on the cost function, and better represent our prior beliefs about the function's form.

NTU talk: 3D Reconstruction from Images

Title: 3D Reconstruction from Images

Shang-Hong Lai
Department of Computer Science
National Tsing Hua University

Time: 2:20pm, March 26 (Fri), 2010
Place: Room 103, CSIE Building

Abstract: The goal of 3D computer vision is to recover real-world three-dimensional information of the scene or objects from 2D images. There have been many different approaches with various image acquisition set-ups to achieve the 3D reconstruction in computer vision. In this talk, I will discuss some 3D reconstruction systems and some researches on 3D reconstruction in my lab. I will present our researches on 3D reconstruction from multi-view images, two-view (stereo) images, and a single image. Some 3D reconstruction results will be shown to demonstrate the performance of our systems.

Bio: Shang-Hong Lai received the BS and MS degrees in electrical engineering from National Tsing Hua University in 1986 and 1988, respectively. He also received the PhD degree from University of Florida, Gainesville, USA, in 1995. He worked for Siemens Corporate Research in Princeton, USA, as a research scientist from 1995 to 2000. Then, Dr. Lai returned to Taiwan to join department of computer science, National Tsing Hua University, as a faculty member. He is currently a professor and associate chair in the same department.

CMU PhD Thesis proposal: Data-driven Scene Parsing With the Visual Memex

Data-driven Scene Parsing With the Visual Memex

Tomasz Malisiewicz

Carnegie Mellon University

February 18, 2010, 4:00 p.m., NSH 3305

Abstract: This proposal is concerned with the problem of image understanding. Given a single static image, the goal is to explain the entire image by recognizing all of the objects depicted in the image. We formulate the problem of image understanding as image parsing -- breaking up the image into semantically meaningful regions and recognizing the objects embedded in each region. In our approach we strive to obtain a dense understanding of the image by not leaving any portion of the image unexplained. While most approaches to scene understanding formulate the problem as that of recognizing abstract object categories (and for object asking “what is this?”), we use a data-driven model of recognition more akin to memory (and ask the question: “what is this like?”). We present an exemplar-based framework for reasoning about objects and their relationships in images dubbed the Visual Memex. The Visual Memex is a non-parametric graph-based model of objects which encodes two types of object relationships: visual similarity between object exemplars, and 2D spatial context between objects in a single image. We use a region-based representation of exemplar objects which has been shown to be superior to the popular rectangular window approach for a wide array of things and stuff found in natural scenes. During training, we learn a set of similarity functions per-exemplar and formulate recognition as association between automatically extracted regions from the input image and exemplar regions in the Visual Memex. We use both bottom-up image segmentation, mid-level reasoning about segment relationships as well as spatial relationships between exemplars in the Visual Memex as complementary sources of object hypotheses. I propose an iterative image parsing framework which builds an interpretation of an input image by iteratively conditioning on a current (partial) interpretation and generating novel segment hypotheses using low-level, mid-level, and high-level cues. An evaluation is proposed which evaluates the system with respect to recognition as well as segmentation on real world scenes from LabelMe.

Thesis Committee
Alexei A. Efros, Chair
Martial Hebert
Takeo Kanade
Pietro Perona, California Institute of Technology

CMU talk: Simon J.D. Prince, Monday, Feb 8, NSH 1507, 3pm-4pm

Title: Modeling Facial Images with Patches
Speaker: Simon J.D. Prince

Abstract:

Faces are one of the most studied object classes in computer vision. Performance is very good for tasks such as identity recognition and gender classification when the pose, lighting and expression are controlled. However, in uncontrolled conditions, these tasks remain challenging. Part of the reason for this limitation is the choice of representation: for example, faces have variously been modeled as subspaces and constellations of features, but these representations have only a limited ability to describe uncontrolled facial images. In this talk, I will present several experiments in which we have investigated representing faces with a regular grid of patches. This type of model can better capture the complex multimodal appearance of uncontrolled faces. I will present models for both gender recognition (or more generally classification of facial characteristic) and pose estimation (regression). I will also show how to extend these patch-based models to allow generation of near photo-realistic images of novel faces.

Bio:

Simon Prince was an undergraduate at UCL where he studied Psychology. His doctoral work was at the University of Oxford, in the Department of Experimental Psychology where he investigated human stereo vision using psychophysics. He subsequently worked in the Laboratory of Physiology in Oxford for two years as a post-doc with Andrew Parker studying stereo vision using single unit electro-physiology. In 2001 he became a post-doctoral research fellow in the Department of Electrical and Computer Engineering in the National University of Singapore working on augmented reality. Following this, he moved to Toronto, Canada, where he worked as a post-doc in computer vision for James Elder in the Centre for Vision Research in York University. Since 2005 he has been a faculty member in the department of computer science at University College London. His current interests include image segmentation, face recognition, optical tomography and object recognition.

Lab Meeting January 19, 2010 (Jimmy): Semi-supervised Learning in Gigantic Image Collections

Title: Semi-supervised Learning in Gigantic Image Collections
Authors: Rob Fergus, Yair Weiss, and Antonio Torralba
In : NIPS2009

Abstract :
With the advent of the Internet it is now possible to collect hundreds of millions of images. These images come with varying degrees of label information. “Clean labels” can be manually obtained on a small fraction, “noisy labels” may be extracted automatically from surrounding text, while for most images there are no labels at all. Semi-supervised learning is a principled framework for combining these different label sources. However, it scales polynomially with the number of images, making it impractical for use on gigantic collections with hundreds of millions of images and thousands of classes. In this paper we show how to utilize recent results in machine learning to obtain highly efficient approximations for semi-supervised learning that are linear in the number of images. Specifically, we use the convergence of the eigenvectors of the normalized graph Laplacian to eigenfunctions of weighted Laplace-Beltrami operators. Our algorithm enables us to apply semi-supervised learning to a database of 80 million images gathered from the Internet.

[paper][slides]

Lab Meeting January 19, 2010(Chung-Han):A collection of outdoor robotic datasets with centimeter-accuracy ground truth

Title : A collection of outdoor robotic datasets with centimeter-accuracy ground truth
Author : Jose-Luis Blanco, Francisco-Angel Moreno, Javier-Gonzalez
In : Autonomous Robots (2009)

Abstract :
The lack of publicly accessible datasets with a reliable ground truth has prevented in the past a fair and coherent comparison of different methods proposed in the mobile robot Simultaneous Localization and Mapping (SLAM) literature. Providing such a ground truth becomes specially challenging in the case of visual SLAM, where the world model is 3-dimensional and the robot path is 6-dimensional. This work addresses both the practical and theoretical issues found while building a collection of six outdoor datasets. It is discussed how to estimate the 6-d vehicle path from readings of a set of three Real Time Kinematics (RTK) GPS receivers, as well as the associated uncertainty bounds that can be employed to evaluate the performance of SLAM methods. The vehicle was also equipped with several laser scanners, from which reference point clouds are built as a testbed for other algorithms such as segmentation or surface fitting. All the datasets, calibration information and associated software tools are available for download http://babel.isa.uma.es/mrpt/papers/dataset2009/.

[Full Text]

CMU PhD Thesis Defense: Mapping Large Urban Environments with GPS-Aided SLAM

CMU RI PhD Thesis Defense

Justin Carlson
Mapping Large Urban Environments with GPS-Aided SLAM
December 03, 2009, 9:00 a.m., GHC 6115

Abstract

Simultaneous Localization and Mapping (SLAM) has been an active area of research for several decades, and has become a foundation of indoor mobile robotics. However, although the scale and quality of results have improved markedly in that time period, no current technique can effectively handle city-sized urban areas.

The Global Positioning System (GPS) is an extraordinarily useful source of localization information. Unfortunately, the noise characteristics of the system are complex, arising from a large number of sources, some of which have large autocorrelation. Incorporation of GPS signals into SLAM algorithms requires using low-level system information and explicit models of the underlying system to make appropriate use of the information. The potential benefits of combining GPS and SLAM include increased robustness, increased scalability, and improved accuracy of localization.

This dissertation presents a theoretical background for GPS-SLAM fusion. The presented model balances ease of implementation with correct handling of the highly colored sources of noise in a GPS system.. This utility of the theory is explored and validated in the framework of a simulated Extended Kalman Filter driven by real-world noise.

The model is then extended to Smoothing and Mapping (SAM), which overcomes the linearization and algorithmic complexity limitations of the EKF formulation. This GPS-SAM model is used to generate a probabilistic landmark-based urban map covering an area an order of magnitude larger than previous work.

[LINK][PDF]

Thesis Committee
Charles Thorpe, Chair
Brett Browning
Martial Hebert
Frank Dellaert, Georgia Institute of Technology

Lab Meeting January 6th, 2010 (Any): Learning General Optical Flow Subspaces for Egomotion Estimation and Detection of Motion Anomalies

Author: Richard Roberts, Christian Potthast and Frank Dellaert

IEEE Computer Society Conference on Computer Vision and Pattern Recognition 2009 (CVPR'09)

Abstract--This paper deals with estimation of dense optical flow and ego-motion in a generalized imaging system by exploiting probabilistic linear subspace constraints on the flow. We deal with the extended motion of the imaging system through an environment that we assume to have some degree of statistical regularity. For example, in autonomous ground vehicles the structure of the environment around the vehicle is far from arbitrary, and the depth at each pixel is often approximately constant. The subspace constraints hold not only for perspective cameras, but in fact for a very general class of imaging systems, including catadioptric and multiple-view systems. Using minimal assumptions about the imaging system, we learn a probabilistic subspace constraint that captures the statistical regularity of the scene geometry relative to an imaging system. We propose an extension to probabilistic PCA (Tipping and Bishop, 1999) as a way to robustly learn this subspace from recorded imagery, and demonstrate its use in conjunction with a sparse optical flow algorithm. To deal with the sparseness of the input flow, we use a generative model to estimate the subspace using only the observed flow measurements. Additionally, to identify and cope with image regions that violate subspace constraints, such as moving objects, objects that violate the depth regularity, or gross flow estimation errors, we employ a per-pixel Gaussian mixture outlier process. We demonstrate results of finding the optical flow subspaces and employing them to estimate dense flow and to recover camera motion for a variety of imaging systems in several different environments.

Local copy

Lab Meeting January 6th, 2010 (Kuen-Han): Adaptive Relative Bundle Adjustment(RSS 2009)

Title: Adaptive Relative Bundle Adjustment(RSS 2009)
Authors: Gabe Sibley, Christopher Mei, Ian Reid, Paul Newman

Abstract

It is well known that bundle adjustment is the
optimal non-linear least-squares formulation of the simultaneous
localization and mapping problem, in that its maximum
likelihood form matches the definition of the Cramer Rao
Lower Bound. Unfortunately, computing the ML solution is
often prohibitively expensive – this is especially true during loop
closures, which often necessitate adjusting all parameters in a
loop. In this paper we note that it is precisely the choice of a single
privileged coordinate frame that makes bundle adjustment costly,
and that this expense can be avoided by adopting a completely
relative approach. We derive a new relative bundle adjustment,
which instead of optimizing in a single Euclidean space, works
in a metric-space defined by a connected Riemannian manifold.
Using an adaptive optimization strategy, we show experimentally
that it is possible to solve for the full ML solution incrementally
in constant time – even at loop closure. Our system also operates
online in real-time using stereo data, with fast appearance-based
loop closure detection. We show results for sequences of 23k
frames over 1.08km that indicate the accuracy of the approach.

paper link
technical paper

Lab Meeting December 30th, 2009 (Nicole) : Estimation of Sound Source Number and Directions under a Multi-source Environment (IROS 2009)

Title: Estimation of Sound Source Number and Directions under a Multi-source Environment (IROS 2009)

Authors: Jwu-Sheng Hu, Member IEEE, Chia-Hsing Yang, Student Member IEEE, and Cheng-Kang Wang

Abstract:

Sound source localization is an important featurein robot audition. This work proposes a sound source numberand directions estimation method by using the delayinformation of microphone array. An eigenstructure-basedgeneralized cross correlation method is proposed to estimatetime delay between microphones. Upon obtaining the time delayinformation, the sound source direction and velocity can beestimated by least square method. In multiple sound source case,the time delay combination among microphones is arrangedsuch that the estimated sound speed value falls within anacceptable range. By accumulating the estimation results ofsound source direction and using adaptive K-means++algorithm, the sound source number and directions can beestimated.

[link]

Measuring the Accuracy of Distributed Algorithms on Multi-Robot Systems

Measuring the Accuracy of Distributed Algorithms on Multi-Robot Systems

James McLurkin (UW CSE postdoc, MIT) 
October 9, 2008, 3:30 pm
EE-105

Abstract
Distributed algorithms running on multi-robot systems rely on ad-hoc networks to relay messages throughout the group. The propagation speed of these messages is large, but not infinite, and problems in algorithm execution can arise when the robot speed is a large fraction of the message propagation speed. This implies a robot speed limit, as any robot moving away from a message source faster than the message speed will never receive new information, and no algorithm can function properly on it. In this work, we focus on measuring the accuracy of multi-robot distributed algorithms. We define the Robot Speed Ratio (RSR) as the ratio of robot speed to message speed. We express it in a form that is platform-independent and captures the relationship between communications usage, robot mobility, and algorithm accuracy. We show that trade-offs between these key quantities can be balanced at design time. Finally, we present results from experiments with 50 robots that characterize the accuracy of preexisting distributed algorithms for network communication, navigation, boundary detection, and dynamic task assignment. In all cases, accuracy degrades as speed increases or communication bandwidth is reduced. In our experiments, a RSR of 0.005 allows good accuracy in all algorithms, a RSR of 0.02 allows reasonable accuracy in simple algorithms, and all algorithms tested are essentially useless at a RSR of 0.10 or higher.

[link]

Lab Meeting December 30th, 2009(Gary) : Pose Robust Face Tracking by Combining Active Appearance Models and Cylinder Head Models

Title: Pose Robust Face Tracking by Combining Active Appearance Models and Cylinder Head Models (IJCV 2008)

Author : Jaewon Sung , Takeo Kanade , Daijin Kim

Abstract:

The active appearance models (AAMs) provide
the detailed descriptive parameters that are useful for various
autonomous face analysis problems. However, they are
not suitable for robust face tracking across large pose variation
for the following reasons. First, they are suitable for
tracking the local movements of facial features within a limited
pose variation. Second, they use gradient-based optimization
techniques for model fitting and the fitting performance
is thus very sensitive to initial model parameters.
Third, when their fitting is failed, it is difficult to obtain
appropriate model parameters to re-initialize them. To alleviate
these problems, we propose to combine the active
appearance models and the cylinder head models (CHMs),
where the global head motion parameters obtained from the
CHMs are used as the cues of the AAM parameters for a
good fitting or re-initialization. The good AAM parameters
for robust face tracking are computed in the following manner.
First, we estimate the global motion parameters by the
CHM fitting algorithm. Second, we project the previously
fitted 2D shape points onto the 3D cylinder surface inversely
Third, we transform the inversely projected shape points by
the estimated global motion parameters. Fourth, we project
the transformed 3D points onto the input image and computed
the AAM parameters from them. Finally, we treat the
computed AAM parameters as the initial parameters for the
fitting. Experimental results showed that face tracking combining
AAMs and CHMs is more pose robust than that of
AAMs in terms of 170% higher tracking rate and the 115%
wider pose coverage.



link

NTU talk: Human Action Recognition Using Bag of Video Words

Title: Human Action Recognition Using Bag of Video Words
Speaker: Dr. Mubarak Shah, Agere Chair Professor of Computer Science, University of Central Florida
Time: 4:00pm, Dec 24 (Thu), 2009
Place: Room 210, CSIE building

Abstract:

The traditional approach for video analysis involves detection of objects, followed by tracking of objects from frame to frame and finally analysis of tracks for human action recognition. However, in some videos of complex scenes it is not possible to reliably detect and track objects. Therefore, recently in computer vision there has been lots of interest in the bag of video words approach, which bypasses the object detection and tracking steps. In bag of video words approach an action is described by a distribution of spatiotemporal cuboids (3D interest points).

In this talk, first I will describe a method to automatically discover the optimal number of video words clusters by utilizing the Maximization of Mutual Information (MMI). Unlike the k-means algorithm which is typically used to cluster spatiotemporal cuboids into video words based on their appearance similarity, MMI clustering further groups the video-words, such that the semantically similar video-words, e.g. words corresponding to the same part of the body during an action, are grouped in the same cluster.

The above method for human action recognition uses only one kind of features, spatiotemporal cuboids. However, single feature based representation for human action is not sufficient to capture the imaging variations (view-point, illumination etc.) and attributes of individuals (size, age, gender etc.).

Next I will present a method which uses two types of features: i) a quantized vocabulary of local spatio-temporal (ST) volumes (or cuboids), and ii) a quantized vocabulary of spin-images. To optimally combine these features, we treat different features and videos as nodes in a graph, where weighted edges between the nodes represent the strength of the relationship between entities. The graph is then embedded into a k-dimensional space subject to the criteria that similar nodes have Euclidian coordinates which are closer to each other. This is achieved by converting this constraint into a minimization problem whose solution is the eigenvectors of the graph Laplacian matrix. This procedure is known as Fiedler Embedding.

Short Biography:
Dr. Mubarak Shah, Agere Chair Professor of Computer Science, is the founding director of the Computer Visions Lab at UCF. He is a co-author of three books (Motion-Based Recognition (1997), Video Registration (2003), and Automated Multi-Camera Surveillance: Algorithms and Practice (2008)), all by Springer. He has published ten book chapters, seventy five journal and one hundred seventy conference papers on topics related to visual surveillance, tracking, human activity and action recognition, object detection and categorization, shape from shading, geo registration, photo realistic synthesis, visual crowd analysis, bio medical imaging, etc.

Dr. Shah is a fellow of IEEE, IAPR and SPIE. In 2006, he was awarded a Pegasus Professor award, the highest award at UCF, given to a faculty member who has made a significant impact on the university, has made an extraordinary contribution to the university community, and has demonstrated excellence in teaching, research and service. He is a Distinguished ACM Speaker. He was an IEEE Distinguished Visitor speaker for 1997-2000, and received IEEE Outstanding Engineering Educator Award in 1997. He received the Harris Corporation's Engineering Achievement Award in 1999, the TOKTEN awards from UNDP in 1995, 1997, and 2000; Teaching Incentive Program awards in 1995 and 2003, Research Incentive Award in 2003, Millionaires' Club awards in 2005 and 2006, University Distinguished Researcher award in 2007, SANA award in 2007, an honorable mention for the ICCV 2005 Where Am I? Challenge Problem, and was nominated for the best paper award in ACM Multimedia Conference in 2005. He is an editor of international book series on Video Computing; editor in chief of Machine Vision and Applications journal, and an associate editor of ACM Computing Surveys journal. He was an associate editor of the IEEE Transactions on PAMI, and a guest editor of the special issue of International Journal of Computer Vision on Video Computing. He is the program co-chair of IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2008.

Lab Meeting Dez. 23rd 09 (Andi): Shape-based Recognition of 3D Point Clouds in Urban Environments

Authors: Aleksey Golovinskiy, Vladimir G. Kim, Thomas Funkhouser

International Conference on Computer Vision (ICCV), September 2009

This paper investigates the design of a system for recognizing objects in 3D point clouds of urban environments. The system is decomposed into four steps: locating, segmenting, characterizing, and classifying clusters of 3D points. Specifically, we first cluster nearby points to form a set of potential object locations (with hierarchical clustering). Then, we segment points near those locations into foreground and background sets (with a graph-cut algorithm). Next, we build a feature vector for each point cluster (based on both its shape and its context). Finally, we label the feature vectors using a classifier trained on a set of manually labeled objects. The paper presents several alternative methods for each step. We quantitatively evaluate the system and tradeoffs of different alternatives in a truthed part of a scan of Ottawa that contains approximately 100 million points and 1000 objects of interest. Then, we use this truth data as a training set to recognize objects amidst approximately 1 billion points of the remainder of the Ottawa scan.

full Paper

also:
Min-Cut Based Segmentation of Point Clouds
Aleksey Golovinskiy and Thomas Funkhouser
IEEE Workshop on Search in 3D and Video (S3DV) at ICCV, September 2009, Kyoto

Lab Meeting December 23rd, 2009 (Shao-Chen): Multi-robot SLAM with Unknown Initial Correspondence: The Robot Rendezvous Case

Title: Multi-robot SLAM with Unknown Initial Correspondence: The Robot Rendezvous Case (IROS 2006)

Authors: Xun S. Zhou and Stergios I. Roumeliotis

Abstract:

This paper presents a new approach to the multi-

robot  map-alignment  problem  that  enables  teams  of  robots  to

build joint maps without initial knowledge of their relative poses.

The  key  contribution  of  this  work  is  an  optimal  algorithm  for

merging  (not  necessarily  overlapping)  maps  that  are  created

by  different  robots  independently.  Relative  pose  measurements

between pairs of robots are processed to compute the coordinate

transformation  between  any  two  maps.  Noise  in  the  robot-

to-robot  observations,  propagated  through  the  map-alignment

process,  increases  the  error  in  the  position  estimates  of  the

transformed  landmarks,  and  reduces  the  overall  accuracy  of

the merged map. When there is overlap between the two maps,

landmarks that appear twice provide additional information, in

the form of constraints, which increases the alignment accuracy.

Landmark  duplicates  are  identified  through  a  fast  nearest-

neighbor  matching  algorithm.  In  order  to  reduce  the  compu-

tational  complexity  of  this  search  process,  a  kd-tree  is  used

to  represent  the  landmarks  in  the  original  map.  The  criterion

employed for matching any two landmarks is the Mahalanobis

distance.  As  a  means  of  validation,  we  present  experimental

results obtained from two robots mapping an area of 4,800 m

2.

[Link]

PhD Thesis Defense: Rhythmic Human-Robot Social Interaction

Marek P. Michalowski
Carnegie Mellon University
December 21, 2009, 10:00 a.m., NSH 3305

Abstract

Social scientists have identified and begun to describe rhythmic and synchronous properties of human social interaction. However, social interactions with robots are often stilted due to temporal mismatch between the behaviors, both verbal and nonverbal, of the interacting partners. This thesis brings the theory of interactional synchrony to bear on the design of social robots with a proposed architecture for rhythmic intelligence. We have developed technology that allows a robot to perceive social rhythms and to behave rhythmically. We have facilitated constrained social interactions, and designed experimental protocols, in which a robot variably synchronizes to human and/or environmental rhythms -- first in a dance-oriented task, and second in a cooperative video game. We have analyzed these interactions to understand the effects of Keepon's rhythmic attention on human performance. This thesis demonstrates that variations in a robot's rhythmic behavior have measurable effect on human rhythmic behavior and on performance in rhythmic tasks. Furthermore, human participants were able to assume and transition between the roles of leader or follower in these tasks.

Thesis Committee

Reid Simmons, Chair
Illah Nourbakhsh
Jodi Forlizzi
Hideki Kozima, Miyagi University, Japan

[link] [thesis draft]

Lab Meeting December 16th, 2009 (Casey): Monocular Vision SLAM for INdoor Aerial Vehicles

Title: Monocular Vision SLAM for INdoor Aerial Vehicles (IROS 2009)

Authors: Koray Celik, Soon-Jo Chung, Matthew Clausman, and Arun K. Somani

Abstract:

This paper presents a novel indoor navigation and ranging strategy by using a monocular camera. The proposed algorithms are integrated with simultaneous localization and mapping(SLAM) with a focus on indoor aerial vehicle applications. We experimentally validate the proposed algorithms by using a fully self-contained micro aerial vehicle (MAV) with on-board image processing and SLAM capabilities. The range measurement strategy is inspired by the key adaptive mechanisms for depth perception and pattern recognition found in humans and intelligent animals. The navigation strategy assumes an unknown, GPS-denied environment, which is representable via corner-like feature points and straight architectural lines. Experimental results show that the system is only limited by the capabilities of the camera and the availability of good corners.

[Link]

Lab Meeting December 16th, 2009 (Jeff): On measuring the accuracy of SLAM algorithms

Title: On measuring the accuracy of SLAM algorithms

Authors: Rainer Kümmerle, Bastian Steder, Christian Dornhege, Michael Ruhnke, Giorgio Grisetti, Cyrill Stachniss and Alexander Kleiner

Abstract:

In this paper, we address the problem of creating an objective benchmark for evaluating SLAM approaches. We propose a framework for analyzing the results of a SLAM approach based on a metric for measuring the error of the corrected trajectory. This metric uses only relative relations between poses and does not rely on a global reference frame. This overcomes serious shortcomings of approaches using a global reference frame to compute the error. Our method furthermore allows us to compare SLAM approaches that use different estimation techniques or different sensor modalities since all computations are made based on the corrected trajectory of the robot.
We provide sets of relative relations needed to compute our metric for an extensive set of datasets frequently used in the robotics community. The relations have been obtained by manually matching laser-range observations to avoid the errors caused by matching algorithms. Our benchmark framework allows the user to easily analyze and objectively compare different SLAM approaches.

Link:
Auton Robot 2009 27:387-407
http://www.springerlink.com/content/5u7458rl080216vr/fulltext.pdf

CMU Talk: Corridor View: Making Indoor Life Easier with Large Image Database

CMU VASC Seminar
Monday, Dec 7, 2009
1:30pm-2:30pm
NSH 1507

Corridor View: Making Indoor Life Easier with Large Image Database
Hongwen "Henry" Kang
Ph.D. Student, Robotics

Abstract:

Indoor environment poses substantial challenges for Computer Vision algorithms, due to the combined patterns that are either highly repetitive (e.g. doors), textureless (e.g. white walls), or temporally changing (e.g. posters, pedestrians). The fundamental challenge we want to tackle is the robust image matching. We proposed two approaches to address this problem, one is an iterative algorithm that combines global/local weighting strategies under bag-of-features model, the other data-mines distinctive feature vectors and uses high dimensional features directly for image matching, without quantization. Both of the approaches demonstrate significant improvements compared to straightforward image retrieval approaches, in highly confusing indoor environment. The proposed image matching techniques have broad applications. We selectively demonstrate two of them for this talk, specifically for vision impaired users living in the office environments. One application is data-driven zoomin; the other application is image composition for object pop-out.

CMU talk: Unsupervised Detection of Regions of Interest Using Iterative Link Analysis

CMU VASC Seminar
Monday, November 30, 2009

Unsupervised Detection of Regions of Interest Using Iterative Link Analysis
Gunhee Kim
Ph.D. Student, Computer Science Department

Abstract:

This work is a joint project with Antonio Torralba during my visit to MIT and will be presented as a poster at the upcoming NIPS 2009 Conference.

This talk will discuss a fast and scalable alternating optimization technique to detect regions of interest (ROIs) in cluttered Web images without labels. The proposed approach discovers highly probable regions of object instances by iteratively repeating the following two functions: (1) choose the exemplar set (i.e. a small number of highly ranked reference ROIs) across the dataset and (2) refine the ROIs of each image with respect to the exemplar set. These two subproblems are formulated as ranking in two different similarity networks of ROI hypotheses by link analysis. The experiments with the PASCAL 06 dataset show that our unsupervised localization performance is better than one of the state-of-the-art techniques and comparable to supervised methods. Also, we test the scalability of our approach with five objects in a Flickr dataset consisting of more than 200K images.

Bio: Gunhee Kim is a Ph.D. student in CMU's Computer Science Department advised by Takeo Kanade. He received his master's degree under the supervision of Martial Hebert in 2008 from the Robotics Institute at CMU. His research interests are computer vision, machine learning, data mining, and biomedical imaging.

Lab Meeting November 25, 2009 (Alan): Navigating, Recognizing and Describing Urban Spaces With Vision and Lasers (IJRR 2009)

Title: Navigating, Recognizing and Describing Urban Spaces With Vision and Lasers (IJRR 2009)

Authors: Paul Newman, Gabe Sibley, Mike Smith, Mark Cummins, Alastair Harrison, Chris Mei, Ingmar Posner, Robbie Shade, Derik Schroeter, Liz Murphy, Winston Churchill, Dave Cole, Ian Reid

Abstract:

In this paper we describe a body of work aimed at extending the reach of mobile navigation and mapping. We describe how running topological and metric mapping and pose estimation processes concurrently, using vision and laser ranging, has produced a full six degree-of-freedom outdoor navigation system. It is capable of pro-ducing intricate three-dimensional maps over many kilometers and in real time. We consider issues concerning the intrinsic quality of the built maps and describe our progress towards adding semantic labels to maps via scene de-construction and labeling. We show how our choices of representation, inference methods and use of both topological and metric techniques naturally allow us to fuse maps built from multiple sessions with no need for manual frame alignment or data association.

Link: http://ijr.sagepub.com/cgi/content/abstract/28/11-12/1406?etoc

CMU PhD Thesis Proposal: Learning Methods for Thought Recognition

CMU RI PhD Thesis Proposal

Mark Palatucci
Learning Methods for Thought Recognition
November 18, 2009, 3:00 p.m., NSH 3305

Abstract
This thesis proposal considers the problem of training machine learning classifiers in domains where data are very high dimensional and training examples are extremely limited or impossible to collect for all classes of interest. As a case study, we focus on the application of thought recognition, where the objective is to classify a person’s cognitive state from a recorded image of that person’s neural activity. Machine learning and pattern recognition methods have already made a large impact on this field, but most prior work has focused on classification studies with small numbers of classes and moderate amounts of training data. In this thesis, we focus on thought recognition in a limited data setting, where there are few, if any, training examples for the classes we wish to discriminate, and the number of possible classes can be in the thousands.

Despite these constraints, this thesis seeks to demonstrate that it is possible to classify noisy, high dimensional data with extremely few training examples by using spatial and temporal domain knowledge, intelligent feature selection, semantic side information, and large quantities of unlabeled data from related tasks.

In our preliminary work, we showed that it possible that build a binary classifier that can accurately classify between cognitive states with more than 80,000 features, and only two training examples per class. We also showed how classification can be improved using principled feature selection, and derived a significance test using order statistics that is appropriate for very high-dimensional problems with small numbers of training examples.

We have also explored the most extreme case of limited data, the zero-shot learning setting, where we do not have any training examples for classes we wish to discriminate. We showed that by using a knowledge base of semantic side information to create intermediate features, we can build a classifier that can classify words that people are thinking about, even without training data for those words while the classifier is forced to choose between nearly 1,000 different candidate words.

Finally, we showed how multi-task learning can be used to learn useful semantic features directly from data. We formulated the semantic feature learning problem as a Multi-task Lasso and presented an extremely fast and highly scalable algorithm for solving the resulting optimization.

We propose work to extend our zero-shot learning setting by optimizing semantic feature sets and by using an active learning framework to choose the most informative training examples. We also propose to use latent feature models such as components analysis and sparse coding in a self-taught learning framework to improve decoding by leveraging data from additional neural imaging experiments.

[PDF]

Thesis Committee
Tom Mitchell, Chair
Dean Pomerleau
J. Andrew Bagnell
Andrew Ng, Stanford University

CMU talk: Imitation Learning and Purposeful Prediction

Machine Learning Lunch (http://www.cs.cmu.edu/~learning/)
Speaker: Prof. Drew Bagnell
Date: Monday, November 23, 2009

Imitation Learning and Purposeful Prediction

Programming robots is hard. While demonstrating a desired behavior may be easy, designing a system that behaves this way is often difficult, time consuming, and ultimately expensive. Machine learning promises to enable "programming by demonstration" for developing high-performance robotic systems. Unfortunately, many approaches that utilize the classical tools of supervised learning fail to meet the needs of imitation learning. Perhaps foremost, classical statistics and supervised machine learning exist in a vacuum: predictions made by these algorithms are explicitly assumed to not affect the world in which they operate. I'll discuss the problems that result from ignoring the effect of actions influencing the world, and I'll highlight simple "reduction-based" approaches that, both in theory and in practice, mitigate these problems.

Additionally, robotic systems are often built atop sophisticated planning algorithms that efficiently reason far into the future; consequently, ignoring these planning algorithms in lieu of a supervised learning approach often leads to poor and myopic performance. While planners have demonstrated dramatic success in applications ranging from legged locomotion to outdoor unstructured navigation, such algorithms rely on fully specified cost functions that map sensor readings and environment models to a scalar cost. Such cost functions are usually manually designed and programmed. Recently, our group has developed a set of techniques that learn these functions from human demonstration by applying an /Inverse Optimal Control/ (IOC) approach to find a cost function for which planned behavior mimics an expert's demonstration. These approaches shed new light on the intimate connections between probabilistic inference and optimal control. I'll consider case studies in activity forecasting of drivers and pedestrians as well as the imitation learning of robotic locomotion and rough-terrain navigation. These case-studies highlight key challenges in applying the algorithms in practical settings.

Lab Meeting November 25, 2009 (KuoHuei): You’ll NeverWalk Alone: Modeling Social Behavior for Multi-target Tracking (ICCV 2009)

Title: You’ll NeverWalk Alone: Modeling Social Behavior for Multi-target Tracking

The Twelfth IEEE International Conference on Computer Vision (ICCV 2009)

Authors: S. Pellegrini, A. Ess, K. Schindler, and L. van Gool

Abstract:

Object tracking typically relies on a dynamic model to predict the object’s location from its past trajectory. In crowded scenarios a strong dynamic model is particularly important, because more accurate predictions allow for smaller search regions, which greatly simplifies data association.

Traditional dynamic models predict the location for each target solely based on its own history, without taking into account the remaining scene objects. Collisions are resolved only when they happen. Such an approach ignores important aspects of human behavior: people are driven by their future destination, take into account their environment, anticipate collisions, and adjust their trajectories at an early stage in order to avoid them. In this work, we introduce a model of dynamic social behavior, inspired by models developed for crowd simulation. The model is trained with videos recorded from birds-eye view at busy locations, and applied as a motion model for multi-people tracking from a vehicle-mounted camera. Experiments on real sequences show that accounting for social interactions and scene knowledge improves tracking performance, especially during occlusions.

[link]

Lab Meeting November 11, 2009 (swem): Avoiding Negative Depth in Inverse Depth Bearing-Only SLAM (IROS 2008)

Title: Avoiding Negative Depth in Inverse Depth Bearing-Only SLAM

(2008 IEEE/RSJ International Conference on Intelligent Robots and Systems)

Author: Martin P. Parsley and Simon J. Julier

Abstract:

In this paper we consider ways to alleviate negative estimated depth for the inverse depth parameterisation of bearing-only SLAM. This problem, which can arise even if the beacons are far from the platform, can cause catastrophic failure of the filter.We consider three strategies to overcome this difficulty: applying inequality constraints, the use of truncated second order filters, and a reparameterisation using the negative logarithm of depth. We show that both a simple inequality method and the use of truncated second order filters are succesful. However, the most robust peformance is achieved using the negative log parameterisation.

Link: http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=4651118

ICCV'09 Oral Paper: You’ll NeverWalk Alone: Modeling Social Behavior for Multi-target Tracking

You’ll NeverWalk Alone: Modeling Social Behavior for Multi-target Tracking

S. Pellegrini, A. Ess, K. Schindler and L. van Gool
ICCV 2009 (oral)

Abstract:
Object tracking typically relies on a dynamic model to predict the object’s location from its past trajectory. In crowded scenarios a strong dynamic model is particularly important, because more accurate predictions allow for smaller search regions, which greatly simplifies data association. Traditional dynamic models predict the location for each target solely based on its own history, without taking into account the remaining scene objects. Collisions are resolved only when they happen. Such an approach ignores important aspects of human behavior: people are driven by their future destination, take into account their environment, anticipate collisions, and adjust their trajectories at an early stage in order to avoid them. In this work, we introduce a model of dynamic social behavior, inspired by models developed for crowd simulation. The model is trained with videos recorded from birds-eye view at busy locations, and applied as a motion model for multi-people tracking from a vehicle-mounted camera. Experiments on real sequences show that accounting for social interactions and scene knowledge improves tracking performance, especially during occlusions. [PDF]

Lab Meeting November 11, 2009(Jim Yu): Planning-based Prediction for Pedestrians

Title: Planning-based Prediction for Pedestrians
Author: B. D. Ziebart, N. Ratliff, G. Gallagher, C. Mertz, K. Peterson, J. A. Bagnell, M. Hebert, A K. Dey, S. Srinivasa
International Conference on Intelligent Robots and Systems (IROS 2009)


We present a novel approach for determining robot movements that efficiently accomplish the robot’s tasks while not hindering the movements of people within the environment. Our approach models the goal-directed trajectories of pedestrians using maximum entropy inverse optimal control. The advantage of this modeling approach is the generality of its learned cost function to changes in the environment and to entirely different environments. We employ the predictions of this model of pedestrian trajectories in a novel incremental planner and quantitatively show the improvement in hindrance sensitive robot trajectory planning provided by our approach.

Link

(PAMI2009)Head Pose Estimation in Computer Vision: A Survey

Authors:
Murphy-Chutorian, E.; Trivedi, M.M.;

Abstract:
The capacity to estimate the head pose of another person is a common human ability that presents a unique challenge for computer vision systems. Compared to face detection and recognition, which have been the primary foci of face-related vision research, identity-invariant head pose estimation has fewer rigorously evaluated systems or generic solutions. In this paper, we discuss the inherent difficulties in head pose estimation and present an organized survey describing the evolution of the field. Our discussion focuses on the advantages and disadvantages of each approach and spans 90 of the most innovative and characteristic papers that have been published on this topic. We compare these systems by focusing on their ability to estimate coarse and fine head pose, highlighting approaches that are well suited for unconstrained environments.

NTU talk: Video Analysis in Vision-Based Intelligent Systems

Title: Video Analysis in Vision-Based Intelligent Systems
Speaker: Prof. Hsu-Yung Cheng, National Central University
Time: 2:20pm, Nov 6 (Fri), 2009
Place: Room 103, CSIE building

Abstract: Computer vision and video analysis techniques play an important role in modern intelligent systems. Video-based systems can capture a larger variety of desired information and are relatively inexpensive because cameras are easy to install, operate, and maintain. With the huge amount of video cameras installed everywhere nowadays, there is an urgent need for automated video understanding techniques that can replace human operators to monitor the areas under surveillance. In this talk I will breifly introduce several topics and related techniques in intelligent surveillance applications. More discussions will be given on the topic of video object tracking. I will introduce a work on video object tracking which combines the advantages of both the flexibility of particle sampling and mathematical tractability of Kalman filters. Also, for objects that cannot be separated during the tracking proces, possible solutions are discussed.

Short Biography: Hsu-Yung Cheng received the Bachelor’s degree in computer science and information engineering from National Chiao-Tung University in Taiwan in 2000 and the Master’s degree from the same department in 2002. She earned a degree of Doctor of Philosophy from the University of Washington in Electrical Engineering in 2008. Hsu-Yung Cheng joined the Department of Computer Science and Information Engineering in National Central University in 2008 as an assistant professor. Her research interest includes image and video analysis and intelligent systems.

CMU talk: Challenges in the Practical Application of Machine Learning

Intelligence Seminar

November 10, 2009
3:30 pm

Challenges in the Practical Application of Machine Learning
Carla E. Brodley, Tufts University

Abstract:
In this talk I will discuss the factors that impact the successful application of supervised machine learning. Driven by several interdisciplinary collaborations, we are addressing the problem of what to do when your initial accuracy is lower than is acceptable to your domain experts. Low accuracy can be due to three factors: noise in the class labels, insufficient training data, and whether the features describing each training example are able to discriminate the classes. In this talk, I will discuss research efforts at Tufts addressing the second two factors. The first project, introduces a new problem which we have named active class selection (ACS). ACS arises when one can ask the question: given the ability to collect n additional training instances, how should they be distributed with respect to class? The second project examines how one might assess that the class distinctions are not supported by the features and how constraint-based clustering can be used to uncover the true class structure of the data. These two issues and their solutions will be explored in the context of three applications. The first is to create a map of global map of the land cover of the Earth's surface from remotely sensed data (satellite data). The second is to build a classifier based on data collected from an "artificial nose" to discriminate vapors. The "nose" is a collection of sensors that have different reactions to different vapors. The third is to classify HRCT images of the lung.

Bio:
Carla E. Brodley is a professor in the Department of Computer Science at Tufts University. She received her PhD in computer science from the University of Massachusetts, at Amherst in 1994. From 1994-2004, she was on the faculty of the School of Electrical Engineering at Purdue University. Professor Brodley's research interests include machine learning, knowledge discovery in databases, and computer security. She has worked in the areas of anomaly detection, active learning, classifier formation, unsupervised learning, and applications of machine learning to remote sensing, computer security, digital libraries, astrophysics, content-based image retrieval of medical images, computational biology, saliva diagnostics, evidence-based medicine and chemistry. She was a member of the DSSG in 2004-2005. In 2001 she served as program co-chair for the International Conference on Machine Learning (ICML) and in 2004, she served as the general chair for ICML. Currently she is an associate editor of JMLR and Machine Learning, and she is on the editorial board of DKMD. She is a member of the AAAI Council and is co-chair of the Computing Research Association's Committee on the Status of Women in Computing Research (CRA-W).

Lab Meeting November 4, 2009(Chung-Han) : An Active Learning Approach for Segmenting Human Activity Datasets

Title: An Active Learning Approach for Segmenting Human Acticitiy Datasets
Author: Liyue Zhao, Gita Sukthankar
In: MM '09: Proceedings of the seventeen ACM international conference on Multimedia

Abtract:
Human activity datasets collected under natural conditions are an important source of data. Since these contain multiple activities in unscripted sequence, temporal segmentation of multimodal datasets is an important precursor to recognition and analysis. Manual segmentation is prohibitively time consuming and unsupervised approaches for segmentation are unreliable since they fail to exploit the semantic context of the data. Gathering labels for supervised learning places a large workload on the human user since it is relatively easy to gather a mass of unlabeled data but expensive to annotate. This paper proposes an active learning approach for segmenting large motion capture datasets with both small training sets and working sets. Support Vector Machines (SVMs) are learned using an active learning paradigm; after the classifiers are initialized with a small set of labeled data, the users are iteratively queried for labels as needed. We propose a novel method for initializing the classifiers, based on unsupervised segmentation and clustering of the dataset. By identifying and training the SVM with points from pure clusters, we can improve upon a random sampling strategy for creating the query set. Our active learning approach improves upon the initial unsupervised segmentation used to initialize the classifier, while requiring substantially less data than a fully supervised method; the resulting segmentation is comparable to the latter while requiring significantly less effort from the user.

[Full Text]

Lab Meeting November 4, 2009(Jimmy): Learning To Detect Unseen Object Classes by Between-Class Attribute Transfer

Title: Learning To Detect Unseen Object Classes by Between-Class Attribute Transfer
Authors: Christoph H. Lampert, Hannes Nickisch, and Stefan Harmeling
In: CVPR2009

Abstract
We study the problem of object classification when training and test classes are disjoint, i.e. no training examples of the target classes are available. This setup has hardly been studied in computer vision research, but it is the rule rather than the exception, because the world contains tens of thousands of different object classes and for only a very few of them image, collections have been formed and annotated with suitable class labels.

In this paper, we tackle the problem by introducing attribute-based classification. It performs object detection based on a human-specified high-level description of the target objects instead of training images. The description consists of arbitrary semantic attributes, like shape, color or even geographic information. Because such properties transcend the specific learning task at hand, they can be pre-learned, e.g. from image datasets unrelated to the current task. Afterwards, new classes can be detected based on their attribute representation, without the need for a new training phase. In order to evaluate our method and to facilitate research in this area, we have assembled a new largescale dataset, “Animals with Attributes”, of over 30,000 animal images that match the 50 classes in Osherson’s classic table of how strongly humans associate 85 semantic attributes with animal classes. Our experiments show that by using an attribute layer it is indeed possible to build a learning object detection system that does not require any training images of the target classes.

[link]

I will also try to introduce the NIPS2009 paper Zero-Shot Learning with Semantic Output Codes by M. Palatucci, D. Pomerleau, G. Hinton, and T.M. Mitchell, which gives some formalization to the problem.

(IROS2009)Video: RF Vision: RFID Receive Signal Strength Indicator (RSSI) Images for Sensor Fusion and Mobile Manipulation

Video:

http://video.aol.co.uk/video-detail/rfid-receive-signal-strength-indicator-rssi-images/3237866515

Title: RF Vision: RFID Receive Signal Strength Indicator (RSSI) Images for Sensor Fusion and Mobile Manipulation

Abstract:

In this work we present a set of integrated methods that enable an RFID-enabled mobile manipulator to approach and grasp an object to which a self-adhesive passive (battery-free) UHF RFID tag has been affixed.

Link:
IROS2009

I will find the pdf file later.

NTU talk:

Title: Localization and Mapping of Surveillance Cameras in City Map
Speaker: Prof. Leow Wee Kheng, National University of Singapore
Time: 2:20pm, Oct 30 (Fri), 2009
Place: Room 103, CSIE building

Abstract:

Many large cities have installed surveillance cameras to monitor human activities for security purposes. An important surveillance application is to track the motion of an object of interest, e.g., a car or a human, using one or more cameras, and plot the motion path in a city map. To achieve this goal, it is necessary to localize the cameras in the city map and to determine the correspondence mappings between the positions in the city map and the camera views. Since the view of the city map is roughly orthogonal to the camera views, there are very few common features between the two views for a computer vision algorithm to correctly identify corresponding points automatically. We propose a method for camera localization and position mapping that requires minimum user inputs. Given approximate corresponding points between the city map and a camera view identified by a user, the method computes the orientation and position of the camera in the city map, and determines the mapping between the positions in the city map and the camera view. The performance of the method is assessed in both quantitative tests and practical application. Quantitative test results show that the method is accurate and robust in camera localization and position mapping. Application test results are very encouraging, showing the usefulness of the method in real applications.

Short Biography: Dr. Leow Wee Kheng obtained his B.Sc. and M.Sc. in Computer Science from National University of Singapore in 1985 and 1989 respectively. He pursued Ph.D. study at The University of Texas at Austin and obtained his Ph.D. in Computer Science in 1994. His curent research interests include computer vision, medical image analysis, and protein docking. He has published more than 80 technical papers in journals, conferences, and books. He has also been awarded two U.S. patents and has published another patent under PCT. He has served in the Program Committees and Organizing Committees of various conferences. He has collaborated widely with a large number of local and overseas institutions. His current local collaborators include I2R of A*STAR, Singapore General Hospital, National University Hospital, and National Skin Centre, and overseas collaborators include CNRS in France and National Taiwan University and National Taiwan University Hospital.

CMU talk: Set estimation for statistical inference in brain imaging and active sensing

Machine Learning Lunch (http://www.cs.cmu.edu/~learning/)
Speaker: Prof. Aarti Singh
Venue: GHC 6115
Date: Monday, October 26, 2009

Set estimation for statistical inference in brain imaging and active sensing

Inferring spatially co-located regions of interest is an important problem in several applications, such as identifying activation regions in the brain or contamination regions in environmental monitoring. In this talk, I will present multi-resolution methods for passive and active learning of sets that aggregate data at appropriate resolutions, to achieve optimal bias and variance tradeoffs for set estimation. In the passive setting, we observe some data such as a noisy fMRI image of the brain and then extract the regions with statistically significant brain activity. Active setting, on the other hand, involves feedback where the location of an observation is decided based on the data observed in the past. This can be used for rapid extraction of set estimates, such as a contamination region in environmental monitoring, by designing data-adaptive spatial survey paths for a mobile sensor. I will describe a method that uses information gleaned from coarse surveys to focus sampling around informative regions (boundaries), thus generating successively refined multi-resolution set estimates.

I will also discuss some current research directions which aim at efficient extraction of spatially distributed sets of interest by exploiting non-local dependencies in the data.

Lab Meeting 10/28, 2009(Kuen-Han): Coupled Object Detection and Tracking from Static Cameras and Moving Vehicles(PAMI 2008)

Title: Coupled Object Detection and Tracking from Static Cameras and Moving Vehicles(PAMI 2008)
Authors: B. Leibe, K. Schindler, N. Cornelis, and L. Van Gool.

Abstract

Abstract—We present a novel approach for multi-object tracking
which considers object detection and spacetime trajectory
estimation as a coupled optimization problem. Our approach
is formulated in a Minimum Description Length hypothesis
selection framework, which allows our system to recover from
mismatches and temporarily lost tracks. Building upon a stateof-
the-art object detector, it performs multi-view/multi-category
object recognition to detect cars and pedestrians in the input
images. The 2D object detections are checked for their consistency
with (automatically estimated) scene geometry and are converted
to 3D observations, which are accumulated in a world coordinate
frame. A subsequent trajectory estimation module analyzes the
resulting 3D observations to find physically plausible spacetime
trajectories. Tracking is achieved by performing model selection
after every frame. At each time instant, our approach searches for
the globally optimal set of spacetime trajectories which provides
the best explanation for the current image and for all evidence
collected so far, while satisfying the constraints that no two
objects may occupy the same physical space, nor explain the
same image pixels at any point in time. Successful trajectory
hypotheses are then fed back to guide object detection in future
frames. The optimization procedure is kept efficient through
incremental computation and conservative hypothesis pruning.
We evaluate our approach on several challenging video sequences
and demonstrate its performance on both a surveillance-type
scenario and a scenario where the input videos are taken from
inside a moving vehicle passing through crowded city areas.

link

webpage

Article: PREDICTING RESEARCH FUTURES

Next-Generation Research and Breakthrough Innovation: Indicators from US Academic Research

by Thomas C. McMail

When searching for breakthrough advantages, the key innovations are those that surpass the present state so significantly that they might well lead to the next generation of technical advances. Over a period of a year and a half, I interviewed more than 100 educators, researchers, and deans in many disciplines with the overall goal of encouraging innovative collaborations between Microsoft Research and academics as part of my responsibilities as a university-relations specialist. The survey reveals connections among a broad range of topics and various recurrent themes that might be of interest to scientists in various fields.

[Full Article]

It is good to see that robotics is one of the Next-Generation Research and Breakthrough Innovations.

-Bob

Lab Meeting 10/28 (Any): GroupSAC

Kai Ni, Hailin Jin, and Frank Dellaert. GroupSAC: Efficient Consensus in the Presence of Groupings. In International Conference on Computer Vision (ICCV), September 2009.

Abstract--We present a novel variant of the RANSAC algorithmthat is much more efficient, in particular when dealing with problems with low inlier ratios. Our algorithm assumes that there exists some grouping in the data, based on which we introduce a new binomial mixture model rather than the simple binomial model as used in RANSAC. We prove that in the new model it is more efficient to sample data from a smaller numbers of groups and groups with more tentative correspondences, which leads to a new sampling procedure that uses progressive numbers of groups. We demonstrate our algorithm on two classical geometric vision problems: wide-baseline matching and camera resectioning. The experiments show that the algorithm servesas a general framework that works well with three possible grouping strategies investigated in this paper, including a novel optical flow based clustering approach. The results show that our algorithm is able to achieve a significant performance gain compared to the standard RANSAC and PROSAC.

Lab Meeting 10/21 (Nicole):Discovery of sound sources by an autonomous mobile robot (Autonomous Robots Vol 27 No.3 Oct.2009)

Title:Discovery of sound sources by an autonomous mobile robot

Authors:Eric Martinson and Alan Schultz

Abstract:

In this work, we describe an autonomous mobile robotic system for finding, investigating, and modeling ambient noise sources in the environment. The system has been fully implemented in two different environments, using two different robotic platforms and a variety of sound source types. Making use of a two-step approach to autonomous exploration of the auditory scene, the robot first quickly moves through the environment to find and roughly localize unknown sound sources using the auditory evidence grid algorithm. Then, using the knowledge gained from the initial exploration, the robot investigates each source in more depth, improving upon the initial localization accuracy, identifying volume and directivity, and, finally, building a classification vector useful for detecting the sound source in the future.

[Link]

CMU talk: Cross-Modal Localization Through Mutual Information

FRC Seminar: Cross-Modal Localization Through Mutual Information

Speaker: Dr. Alen Alempijevic
ARC Centre for Autonomous Systems
Mechatronics and Intelligent Systems Group
University of Technology Sydney, Australia

Friday October 16, 2009
NSH 1109
12:00pm

Abstract: Relating information originating from disparate sensors observing a given scene is a challenging task, particularly when an appropriate model of the environment or the behaviour of any particular object within it is not available. One possible strategy to address this task is to examine whether the sensor outputs contain information which can be attributed to a common cause. I will present an approach to localise this embedded common information through an indirect method of estimating mutual information between all signal sources. Ability of L1 regularization to enforce sparseness of the solution is exploited to identify a subset of signals that are related to each other, from among a large number of sensor outputs. As opposed to the conventional L2 regularization, the proposed method leads to faster convergence with reduced spurious associations.

Speaker Bio: Dr. Alen Alempijevic is a Research Fellow within the ARC Centre for Autonomous Systems (Mechatronics and Intelligent Systems Group) at the University of Technology Sydney. He earned his BE in Computer Systems and PhD degrees in Mechatronics Engineering from the University of Technology Sydney in 2003 and 2009 respectively. He has been a guest researcher at UC Berkeley as part of the 2007 entry into the DARPA Grand Challenge and is currently working on 6DOF localization of an underground miner as part of an ARC Linkage Grant. His research interests are in perception for long term autonomy, distributed sensing in self-reconfiguring modular robots and SLAM for urban search and rescue vehicles.

CMU talk: Applied machine learning in human-computer interaction research

Machine Learning Lunch (http://www.cs.cmu.edu/~learning/)
Speaker: Moira Burke
Venue: GHC 6115
Date: Monday, October 19, 2009

Applied machine learning in human-computer interaction research

Human-computer interaction researchers use diverse methods—from eye-tracking to ethnography—to understand human activity, and machine learning is growing in popularity as a method within the community. This talk will survey projects from HCI researchers at CMU combining machine learning with other techniques to problems such as adapting interfaces to individuals with motor impairments, predicting routines in dual-income families, classifying controversial Wikipedia articles, and identifying rhetorical strategies newcomers use in online support groups that elicit responses. Researchers without strong computational backgrounds can face practical challenges as consumers of machine learning tools; this talk will highlight opportunities for tool design and collaboration across communities.

Lab Meeting 10/21 (Gary): 3D Alignment of Face in a Single Image (CVPR 06)

Authors: Lie Gu, Takeo Kanade

Abstract:

We present an approach for aligning a 3D deformable model to a single face image. The model consists of a set of sparse 3D points and the view-based patches associated with every point. Assuming a weak perspective projection model, our algorithm iteratively deforms the model and adjusts the 3D pose to fit the image. As opposed to previous approaches, our algorithm starts the fitting without resorting to manual labeling of key facial points. And it makes no assumptions about global illumination or surface properties, so it can be applied to a wide range of imaging conditions. Experiments demonstrate that our approach can effectively handle unseen faces with a variety of pose and illumination variations.

link

NTU talk: Bridging the Gap between Signal Processing and Machine Learning

Title: Bridging the Gap between Signal Processing and Machine Learning
Speaker: Dr. Y.-C. Frank Wang, Academia Sinica
Time: 2:20pm, Oct 16 (Fri), 2009
Place: Room 103, CSIE building

Abstract:
The advancements of signal processing and machine learning techniques have afforded many applications which benefit people in different areas. The first part of this talk starts with some interesting examples, and explains how “signal processing” and “machine learning” people might interpret the same thing in different points of view. I will discuss why it is vital to bridge the gap between these two areas, and what can be achieved by close collaboration between people from these two communities.

Many of real-world machine learning applications involve recognition of multiple classes. However, standard classification methods may not perform well and efficiently on large-scale problems. Designing a classifier with good generalization and scalability is an ongoing research topic for machine learning researchers. Another important issue, which is typically not addressed in prior work, is the rejection of unseen false classes (not of interest). Since we cannot design a classifier by training the data from “unseen” classes, the rejection problem becomes very challenging. In the second part of this talk, I will present my proposed work, soft-decision hierarchical SVRDM (support vector representation and discrimination machine) classifier, which is to address aforementioned multiclass classification and rejection problems.

Short Biography:
Yu-Chiang Frank Wang received his B.S. in Electrical Engineering from National Taiwan University in 2001. Before pursuing his graduate study, he worked as a research assistant in the Division of Medical Engineering Research at National Health Research Institutes in Taiwan from 2001 to 2002. He received his M.S. and Ph.D. degrees in Electrical and Computer Engineering from Carnegie Mellon University in 2004 and 2009, respectively. His research projects at CMU included the development of a military automated target recognition system, the design of a multi-modal biometric fusion system, and algorithms for data clustering and multi-class classification problems. His research and graduate study were funded by the US Army Research Office through Carnegie Mellon.

Dr. Wang is currently an assistant research fellow in the Research Center for Information Technology Innovation (CITI) at Academia Sinica, where he also holds the position as an adjunct assistant research fellow in the Institute of Information Science. His research interests span the areas of pattern recognition, machine learning, computer vision, multimedia signal processing and content analysis.

NTU talk: Data-Aware Search: Web Scale Integration, Web Scale Inspiration

Title: Data-Aware Search: Web Scale Integration, Web Scale Inspiration
Speaker: Prof. Kevin C. Chang, UIUC
Time: 2:10pm, Oct 15 (Thu), 2009
Place: Room 210, CSIE building

Abstract:
What have you been searching lately? With so much data on the web, we often look for various "stuff" across many sites-- but current search engines can only find web pages and then only one page at a time.

Towards "data-aware" search over the web as a massive database, we face the challenges of integrating data from everywhere. The barrier boils down to the classic "impedance mismatch" between structured queries over unstructured data-- but now at the Internet scale! I will discuss our lessons learned in the MetaQuerier and WISDM projects at Illinois, in which we develop large-scale data integration by two approaches with duality-- bringing data to match queries, and vice versa. I will demo prototypes and their productization at Cazoodle.

Short Biography:
Kevin C. Chang is an Associate Professor in Computer Science, University of Illinois at Urbana-Champaign. He received a BS from National Taiwan University and PhD from Stanford University, in Electrical Engineering. He likes large scale information access and, with his students, co-founded Cazoodle, a startup from the University of Illinois, for developing "data-aware" search over the web. URL: http://www-faculty.cs.uiuc.edu/~kcchang

Lab Meeting 14/10 (Andi): MMM-classification of 3D Range Data (ICRA 09)

Authors: Anuraag Agrawal, Atsushi Nakazawa, and Haruo Takemura (Osaka University)

Abstract: This paper presents a method for accurately segmenting and classifying 3D range data into particular object classes. Object classification of input images is necessary for applications including robot navigation and automation, in particular with respect to path planning. To achieve robust object classification, we propose the idea of an object feature which represents a distribution of neighboring points around a target point. In addition, rather than processing raw points, we reconstruct polygons from the point data, introducing connectivity to the points. With these ideas, we can refine the Markov Random Field (MRF) calculation with more relevant information with regards to determining “related points”. The algorithm was tested against five outdoor scenes and provided accurate classification even in the presence of many classes of interest.


local copy

Lab Meeting 10/14, 2009 (Shao-Chen): Distributed Multirobot Localization(IEEE Transactions on Robotics and Automation Oct. 2002)

Title: Distributed Multirobot Localization(IEEE Transactions on Robotics and Automation Oct. 2002)

Authors: Stergios I. Roumeliotis,  George A. Bekey

Abstract:

In this paper, we present a new approach to the
problem of simultaneously localizing a group of mobile robots
capable of sensing one another. Each of the robots collects sensor
data regarding its own motion and shares this information with
the rest of the team during the update cycles. A single estimator,
in the form of a Kalman filter, processes the available positioning
information  from  all  the  members  of  the  team  and  produces
a pose estimate for every one of them. The equations for this
centralized  estimator  can  be  written  in  a  decentralized  form,
therefore  allowing this  single  Kalman  filter  to  be  decomposed
into a number of smaller communicating filters. Each of these
filters processes sensor data collected by its host robot. Exchange
of information between the individual filters is necessary only
when two robots detect each other and measure their relative
pose. The resulting decentralized estimation schema, which we
call collective localization, constitutes a unique means for fusing
measurements collected from a variety of sensors with minimal
communication  and  processing  requirements.  The  distributed
localization algorithm is applied to a group of three robots and
the improvement in localization accuracy is presented. Finally, a
comparison to the equivalent decentralized information filter is
provided.

Link

CMU talk: The Inner Workings of face Processing: From human to computer perception and back

CMU VASC Seminar
Monday, October 12
2pm-3pm

The Inner Workings of face Processing: From human to computer perception and back
Aleix Martinez
Ohio State University

Abstract:
Faces and emotions shape our daily life in many different ways. We are so intrigued about such effects that writers, poets and painters have been depicting and portraying them for centuries. Why does the male character in Wood's "American Gothic" seem sad? Why do kids elongate their faces when they are upset? Why do some people always seem angry? Why do we recognize identity from faces so easily? Why is it so hard to learn non-manuals (i.e., facial expressions of grammar) in sign languages, when native signers do this effortlessly? In short, what are the dimensions of our computational (cognitive) space responsible for these face processing tasks? If we are to understand why things appear as they do and how cognitive disorders in Autism, schizophrenia and Huntington’s disease develop, we need to define how the brain codes and interpreters faces, emotions and grammar. This is also important for the design of technology – as devices need to interact with us. In this talk, I will outline the research framework I use to study face perception and the related topics of emotion and grammar. This consists of a multidisciplinary approach in cognitive science, including psychophysical studies and the design of computer algorithms for the analysis of face images. We will review the big questions about this computational space. In doing so, we will see that the ability of human observers to process face images is truly remarkable, suggesting that some abstract, yet simple representation that is unaffected by a large number of image transformations is at work. We will summarize our current understanding of this representation.

Bio:
Aleix M. Martinez is an Associate Professor in the Department of Electrical and Computer Engineering at The Ohio State University (OSU), where he is the founder and director of the Computational Biology and Cognitive Science Lab. He is also affiliated with the Department of Biomedical Engineering and to the Center for Cognitive Science. Prior to joining OSU, he was affiliated with the Electrical and Computer Engineering Department at Purdue University and with the Sony Computer Science Lab. He serves as an associate editor of IEEE Transactions on Pattern Analysis and Machine Intelligence and of Image and Vision Computing and has been an area chair of CVPR. Aleix has spent his time wondering why he is such a bad face recognizer and why people attribute social labels to faces of unknown individuals. His other areas of interest are learning, vision and linguistics.

Lab Meeting 10/6, 2009 (Casey): Towards Visual Localization, Mapping and Moving Objects Tracking by a Mobile Robot: a Geometric and Probabilistic ...

Title: Towards Visual Localization, Mapping and Moving Objects Tracking by a Mobile Robot: a Geometric and Probabilistic Approach

Author: Joan Solà

(PhD Thesis, 2007/02)

(LAAS-CNRS laboratory in Toulouse, Occitania, France )

Outline:

1. Undelayed initialization in monocular SLAM

2. Binocular SLAM

3. Moving object detection and tracking

[PhD Thesis pdf file]

[Joan Solà's website]