Some talks at ICRA 2009.

Talks at three forums (Industrial Forum, Science Forum, and Citizen's Forum) and plenary talks of IEEE ICRA 2009 are posted at http://podcasts.icra2009.org/rss.xml.

ICRA2009 Awards

Best Vision Paper
Moving Obstacle Detection in Highly Dynamic Scenes [Local Copy][Attachment]

Best Automation Paper and Best Student Paper
Design and Calibration of a Microfabricated 6-Axis Force-Torque Sensor for Microrobotic Applications [Local Copy]

Best Conference Paper
Towards a Navigation System for Autonomous Indoor Flying [Local Copy]

Lab Meeting June 1st, 2009 (Jeff): Modeling RFID Signal Strength and Tag Detection for Localization and Mapping

Title: Modeling RFID Signal Strength and Tag Detection for Localization and Mapping

Authors: Dominik Joho, Christian Plagemann and Wolfram Burgard

Abstract:

In recent years, there has been an increasing interest within the robotics community in investigating whether Radio Frequency Identification (RFID) technology can be utilized to solve localization and mapping problems in the context of mobile robots. We present a novel sensor model which can be utilized for localizing RFID tags and for tracking a mobile agent moving through an RFID-equipped environment. The proposed probabilistic sensor model characterizes the received signal strength indication (RSSI) information as well as the tag detection events to achieve a higher modeling accuracy compared to state-of-the-art models which deal with one of these aspects only. We furthermore propose a method that is able to bootstrap such a sensor model in a fully unsupervised fashion. Real-world experiments demonstrate the effectiveness of our approach also in comparison to existing techniques.

Link:
ICRA2009
http://www.informatik.uni-freiburg.de/~joho/publications/joho09icra.html

Lab Meeting May 25, 2009 (fish60):

I may talk about what I read in recent weeks based on the following paper.

Brenna Argall, Sonia Chernova and Manuela Veloso. A Survey of Robot Learning from Demonstration. Robotics and Autonomous Systems. Vol. 57, No. 5, pages 469-483, 2009.

Link

abstract:
We present a comprehensive survey of robot Learning from Demonstration (LfD), a technique that develops policies from example state to action mappings. We introduce the LfD design choices in terms of demonstrator, problem space, policy derivation and performance, and contribute the foundations for a structure in which to categorize LfD research. Specifically, we analyze and categorize the multiple ways in which examples are gathered, ranging from teleoperation to imitation, as well as the various techniques for policy derivation, including matching functions, dynamics models and plans. To conclude we discuss LfD limitations and related promising areas for future research.

MIT CSAIL Technical Report: Scene Classification with a Biologically Inspired Method

Title: Scene Classification with a Biologically Inspired Method

(MIT CSAIL Technical Report)

Abstract:

We present a biologically motivated method for scene image classification. The core of the method is to use shape based image property that is provided by a hierarchical feedforward model of the visual cortex [18]. Edge based and color based image properties are additionally used to improve the accuracy. The method consists of two stages of image analysis. In the first stage, each of three paths of classification uses each image property (i.e. shape, edge or color based features) independently. In the second stage, a single classifier assigns the category of an image based on the probability distributions of the first stage classifier outputs. Experiments show that the method boosts the classification accuracy over the shape based model. We demonstrate that this method achieves a high accuracy comparable to other reported methods on publicly available color image dataset.

[download]

VASC Seminar: A Data-Driven Vision Compiler for Automatic Object Pose Recognition

VASC Seminar
Monday, May 11, 2009
3:30p-4:30p
NSH 1507

A Data-Driven Vision Compiler for Automatic Object Pose Recognition

Rosen Diankov
Carnegie Mellon University, Robotics Institute


Abstract:

This presentation focuses on an object-specific vision system that detects and extracts the precise 6D pose of objects in an image. The system builds a data-driven statistical model of the expected features of an object's surface and combines this with a discrete search method
to extract the pose of all object. The training phase of the vision system can be interpreted as a compiler that automatically analyzes the statistics of how the features are distributed on the object and determines a feature set's stability and discriminable power. This compilation phase requires the precise CAD model of an object along with a training set of real-world images. After compilation, a CAD-independent model of how features relate with respect to theobject's pose and inter-relate with each other is created. These relationships allow both point-based features like SIFT and edge-based features to be used simultaneously when computing the 6D pose of an
object. Using this data-driven model, we employ a discrete randomized search with RANSAC to find the poses of all instances of the object in a novel image.


Bio:

Rosen Diankov graduated from University of California Berkeley in 2006 with Electrical Engineering and Computer Science, and Applied Math degrees. At the moment he is a PhD graduate student at the Robotics Institute at Carnegie Mellon University. Rosen's main research focus is tackling the robotics problem: combining perception, planning, and control into one coherent framework. Up until now he has worked on several vision and planning systems involving autonomous robots in everyday scenarios both in the United States and Japan.

VASC Seminars are sponsored by Tandent Vision Science, Inc.

Lab Meeting May 25, 2009(Chung-Han) COLD : The CoSy Localization Database

Title : COLD : The CoSy Localization Database

Author : A. Pronobis, B. Caputo

Abstract : Two key competencies for mobile robotic systems are localizationand semantic context interpretation. Recently, vision has become themodality of choice for these problems as it provides richer and moredescriptive sensory input. At the same time, designing and testingvision-based algorithms still remains a challenge, as large amounts ofcarefully selected data are required to address the high variability ofvisual information. In this paper we present a freely available databasewhich provides a large-scale, flexible testing environment forvision-based topological localization and semantic knowledge extractionin robotic systems. The database contains 76 image sequencesacquired in three different indoor environments across Europe. Acquisitionwas performed with the same perspective and omnidirectionalcamera setup, in rooms of different functionality and under variousconditions. The database is an ideal testbed for evaluating algorithmsin real-world scenarios with respect to both dynamic and categoricalvariations.


[Full text]

CMU talk: Fast Feature Detection and Stochastic Parameter Estimation of Road Shape using Multiple LIDAR

FRC Seminar

Fast Feature Detection and Stochastic Parameter Estimation of Road Shape using Multiple LIDAR

Kevin Peterson
PhD Student, Robotics Institute, Carnegie Mellon University


Thursday, May 7th, 2009

Developers of autonomous vehicles must overcome significant challenges before these vehicles can operate around human drivers. In urban environments autonomous cars will be required to follow complex traffic rules regarding merging and queuing, navigate in close proximity to other drivers, and safely avoid collision with pedestrians and fixed obstacles near the road. Knowledge of the location and shape of the roadway near then autonomous car is fundamental to these behaviors. While it is tempting to build an /a priori /GPS-registered map of the road network, the possibility for change in the road structure (e.g. new roads, construction, etc.) precludes the use of maps alone. It is therefore necessary to detect and track roads in real-time.

A rich body of work exists in the area of road tracking. Although some early work was performed on unimproved roads, a majority of the available research focuses on paved roads and highways. Additionally, a vast majority of this work focuses on the use of cameras as the single sensing modality. In this talk I will present a framework for road tracking that uses a particle filter to fuse several sources of data including LIDAR and video. The approach enables road tracking on unimproved roads and, because of the flexible nature of the particle filter, can easily be extended to incorporate new forms of data. I will present results from our preparations for the DARPA Urban Challenge.

Speaker Bio: Kevin Peterson’s research focuses on perception techniques for robust autonomous vehicles. Kevin holds a B.S. and M.S. in Electrical and Computer Engineering from Carnegie Mellon University in Pittsburgh, Pennsylvania and is currently pursuing a PhD in Robotics also from Carnegie Mellon University. Kevin has built software systems for many autonomous systems with applications ranging from cave exploration to unexploded ordinance cleanup. Notably, Kevin led Red Team Too, one of CMUs entries in the 2005 DARPA Grand Challenge and was a participant on Tartan Racing, CMUs winning entry in the 2007 Urban Grand Challenge. Since then, he has been applying inverse optimal control techniques to build models of pedestrian motion in structured and unstructured environments.

NTU PhD oral: Mobile Agent Enhanced Service-Oriented Smart Home Architecture and its Human-System Interaction Framework and Algorithm

NTU CSIE PhD oral:

Mobile Agent Enhanced Service-Oriented Smart Home Architecture and its Human-System Interaction Framework and Algorithm

Chao-Lin Wu

Date: May 7, 2009 (Thursday)
Time: 10 am~ 12 noon
Place: CSIE R340

Advisor: Li-Chen Fu

NTU PhD oral: Computer Vision Techniques for Effective Pedestrian Detection

Computer Vision Techniques for Effective Pedestrian Detection

Yu-Ting Chen

Date: May 6, 2009 (Wednesday)
Time: 10 am~12 noon
Place: CSIE R440

Advisors: Chu-Song Chen and Yi-Ping Hung

口試委員:
(1) 校內：洪一平老師、陳祝嵩老師、傅立成老師、王傑智老師
(2) 校外：陳世旺老師、王聖智老師、賴尚宏老師、鍾國亮老師

MIT PhD Thesis: Robust and Efficient Robotic Mapping

MIT PhD Thesis

Title: Robust and Efficient Robotic Mapping
Author: Edwin B. Olson
Date: June 2008

Abstract
Mobile robots are dependent upon a model of the environment for many of their basic functions. Locally accurate maps are critical to collision avoidance, while large-scale maps (accurate both metrically and topologically) are necessary for efficient route planning. Solutions to these problems have immediate and important applications to autonomous vehicles, precision surveying, and domestic robots.
Building accurate maps can be cast as an optimization problem: find the map that is most probable given the set of observations of the environment. However, the problem rapidly becomes difficult when dealing with large maps or large numbers of observations. Sensor noise and non-linearities make the problem even more difficult— especially when using inexpensive (and therefore preferable) sensors.
This thesis describes an optimization algorithm that can rapidly estimate the maximum likelihood map given a set of observations. The algorithm, which iteratively reduces map error by considering a single observation at a time, scales well to large environments with many observations. The approach is particularly robust to noise and non-linearities, quickly escaping local minima that trap current methods. Both batch and online versions of the algorithm are described.
In order to build a map, however, a robot must first be able to recognize places that it has previously seen. Limitations in sensor processing algorithms, coupled with environmental ambiguity, make this difficult. Incorrect place recognitions can rapidly lead to divergence of the map. This thesis describes a place recognition algorithm that can robustly handle ambiguous data.
We evaluate these algorithms on a number of challenging datasets and provide quantitative comparisons to other state-of-the-art methods, illustrating the advantages of our methods.


Link: pdf

CMU talk: Click Chain Model in Web Search

ML Lunch talk:
Speaker: Fan Guo
Date: Monday, May 4, 2009

Title: Click Chain Model in Web Search

Abstract:
Given a terabyte click log, can we build an efficient and effective click model? It is commonly believed that web search click logs are a gold mine for search business, because they reflect users' preference over web documents presented by the search engine. Click models provide a principled approach to inferring user-perceived relevance of web documents, which can be leveraged in numerous applications in search businesses. Due to the huge volume of click data, scalability is a must. I will present the click chain model, which is based on a solid, Bayesian framework. It is both scalable and incremental, perfectly meeting the computational challenges imposed by the voluminous click logs that constantly grow.

Joint work with Chao Liu, Anitha Kannan, Tom Minka, Michael Taylor, Yi-Min Wang and Christos Faloutsos.

NTU talk: AHCBoost: Boosting for Multi-class Classification

Title: AHCBoost: Boosting for Multi-class Classification
Speaker: Prof. Andy Chen-Hai Tsao, National Dong Hwa University
Time: 02:20pm, May 22 (Friday), 2009
Place: Room 102, CSIE building

Abstract:
AdaBoost is one of the important ensemble classifiers developed in the last decade. However, there are difficulties in applying AdaBoost for multi-class classifications. In this study, we introduce the adjustable hyperbolic cosine loss to develop a new boosting algorithm, AHCBoost. Our experiments on benchmark data sets suggest that AHCBoost is very competitive with or even better than the multi-class classifiers SVM and glmBoost. In addition to the fast reduction of training and testing errors, AHCBoost is relatively immune to overfitting and requires little parameter tuning. Some experiments for exploring the potential and limitation of using AHCBoost for ordinal response will also be reported.

Short Biography:

Professional Positions
Department of Applied Math, National Dong Hwa University
* Professor: 2005 to present
* Associate Professor: 1997 to 2005
* Assistant Professor : 1995 to 1997

Institute of Math Statistics, National Chung Cheng University
* Visiting Associate Professor: 1994 to 1995

CMU talk: Learning to Search: Structured Prediction Techniques for Imitation Learning

PhD Thesis Defense:

Learning to Search: Structured Prediction Techniques for Imitation Learning

Nathan D. Ratliff
Carnegie Mellon University

May 01, 2009

Abstract: Modern robots successfully manipulate objects, navigate rugged terrain, drive in urban settings, and play world-class chess. Unfortunately, programming these robots is challenging, time-consuming and expensive; the parameters governing their behavior are often unintuitive, even when the desired behavior is clear and easily demonstrated. Inspired by successful end-to-end learning systems such as neural network controlled driving platforms (Pomerleau, 1989), learning-based "programming by demonstration" has gained currency as a method to achieve intelligent robot behavior. Unfortunately, with highly structured algorithms at their core, it is not clear how to effectively and efficiently train modern robotic systems using classical learning techniques. Rather than redefining robot architectures to accommodate existing learning algorithms, in this thesis I develop learning techniques that leverage the performance of modern robotic components.

My presentation begins with a discussion of a novel imitation learning framework we call Maximum Margin Planning which automates finding a cost function for optimal planning and control algorithms such as A*. In the linear setting, this framework has firm theoretical backing in the form of strong generalization and regret bounds. Further, I have developed practical nonlinear generalizations that are effective and efficient for real-world problems. This framework reduces imitation learning to a modern form of machine learning known as Maximum Margin Structured Classification (Taskar et al. 2005); these algorithms, therefore, apply both specifically to training existing state-of-the-art planners, as well as broadly to solving a range of structured prediction problems of importance in learning and robotics.

In difficult high-dimensional planning domains, such as those found in many manipulation problems, high-performance planning technology remains a topic of much research. I will present some recent work which moves toward simultaneously advancing this technology while retaining the learnability developed above.

I'll demonstrate our algorithms on a range of applications including overhead navigation, quadrupedal locomotion, heuristic learning, manipulation planning, grasp prediction, driver prediction, pedestrian prediction, optical character recognition, and LADAR classification.

link

CMU talk: Camera and LIDAR Fusion for Mapping in Dark Environments

FRC Seminar:

Camera and LIDAR Fusion for Mapping in Dark Environments

Uland Wong
PhD Student, Robotics Institute, Carnegie Mellon University

Thursday, April 30th, 2009

Abstract: Unlit diffuse environments like subterranean voids on earth and planetary surfaces elsewhere are of great interest for robotic exploration and exploitation. These environments pose unique obstacles and constraints, including the necessity for active perception and illumination. However, uniformity of albedo, lack of external lighting and known surface reflectance provide additional assumptions which can be used to enhance 3D-mapping and photographic data collected from robots. This talk presents a method for improving the accuracy of super-resolution point clouds by fusing actively illuminated HDR camera imagery with LIDAR data in dark Lambertian environments. The key approach is shape recovery from estimation of the illumination function and integration in a Markov Random Field (MRF) framework. Experimental results collected from a virtual reconstruction of the Bruceton Research Mine in Pittsburgh, PA are also presented.

NTU talk: Community Discovery in Dynamic, Rich Media Social Networks

Title: Community Discovery in Dynamic, Rich Media Social Networks
Speaker: Yu-Ru Lin, PhD Candidate, Arizona State University.
Time: 3:50pm ~ 5:00pm, Wednesday, March 22, 2009.
Place: Room 111, CSIE building

Abstract: With the rapid proliferation of different types of social media, such as instant messaging (e.g., AIM, MSN, Skype), media sharing sites (e.g., Flickr, YouTube), blogs (e.g., Blogger, WordPress, LiveJournal), wikis (e.g., Wikipedia, PBWiki), microblogs (e.g., Twitter, Jaiku), social networks (e.g., MySpace, Facebook), to mention a few, users routinely produce (e.g. blogs) and consume media (e.g. YouTube) as well as interact with each other through a wide array of functionality provided by various social media. These social media depend largely on implicit communities of online users to deliver value. Identifying and analyzing the dynamics of such latent communities can lead to improved functionality of the social media as well as provide insight into the design of future online collaborative services. The problem is particularly important in the enterprise domain where extracting emergent community structure on enterprise social media, can help in forming new collaborative teams, in expertise discovery, and guide long term enterprise reorganization.

In this talk, I will cover three aspects of community analysis in dynamic, rich media social networks: (1) Community evolution – How do we identify communities in large scale, dynamic social networks, and analyze their structures and evolutions? I will introduce a robust unified approach that discovers communities and captures their evolution with temporal smoothness given by historic community structure. (2) Community summarization – How do we summarize community activities, in order to trace community interests or retrieve community generated content? I will present a summarization framework that characterizes the time-evolving patterns of social activities with associated media objects in a community. (3) Multi-relational communities – How do we discover communities when the social networks exist in a highly connected web of contexts (e.g., social groups, geographic locations, time, etc.)? I will discuss a novel multi-relational non-negative tensor decomposition algorithm that aims to solve this problem. I will also show the effectiveness of these techniques in real world datasets collected from the blogosphere, an enterprise, Flickr, Digg, etc.

Short Biography: Yu-Ru Lin is currently a Ph.D. student in the School of Computing and Informatics at Arizona State University, with a concentration in Arts, Media and Engineering. Her advisor is Dr. Hari Sundaram. Her research interests include problems relating to dynamic multi-relational social network analysis – in particular, community dynamics, social information summarization and representation. Her research focuses on extracting human communities that collaborate around certain topics or media sharing activities. She has proposed non-negative matrix/tensor factorization techniques for analyzing community structures and evolutions in online social networks, as well as time-varying social relational data. Her work has been published in leading international conferences and journals. (Her publication can be found at http://www.public.asu.edu/~ylin56/pub.html.)
She has worked at NEC Labs America and IBM TJ Watson Research Center as a summer intern in 2006, 2007 and 2008. She has received awards including AME Student Excellence Award (2007 and 2008) and IBM PhD Fellowship Award (2009). She holds an M.S. and B.S. degree in Computer Science from National Chiao Tung University, Taiwan.

NTU talk: Mining Geotagged Photos for Semantic Understanding

Title: Mining Geotagged Photos for Semantic Understanding
Speaker: Dr. Jiebo Luo, IEEE Fellow, Senior Principal Scientist with the Kodak Research Laboratories.
Time: 2:30pm ~ 3:40pm, Wednesday, March 22, 2009.
Place: Room 111, CSIE building

Abstract:
Semantic understanding based only on vision cues has been a challenging problem. This problem is particularly acute when the application domain is unconstrained photos available on the Internet or in personal repositories. In recent years, it has been shown that metadata captured with pictures can provide valuable contextual cues complementary to the image content and can be used to improve classification performance. With the recent geotagging phenomenon, an important piece of metadata available with many geotagged pictures is GPS information. We will describe a number of novel ways to mine GPS information in a powerful contextual inference framework that boosts the accuracy of semantic understanding. With integrated GPS-capable cameras on the horizon and geotagging on the rise, this line of research will revolutionize event recognition and media annotation.

Short Biography: Jiebo Luo is a Senior Principal Scientist with the Kodak Research Laboratories in Rochester, NY. He received a B.S. degree and M.S. degree in Electrical Engineering from the University of Science and Technology of China (USTC) in 1989 and 1992, respectively, and a Ph.D. degree in Electrical Engineering from the University of Rochester in 1995. His research interests include signal and image processing, pattern recognition, computer vision, and the related multi-disciplines such as multimedia data mining, biomedical informatics, computational photography, and human-computer interaction. Dr. Luo has authored over 130 technical papers and holds 50 granted US patents. Dr. Luo actively participates in numerous technical conferences, including serving as the chair of the 2008 ACM International Conference on Content-based Image and Video Retrieval (CIVR), an area chair of the 2008 IEEE International Conference on Computer Vision and Pattern Recognition (CVPR), a program co-chair of the 2007 SPIE International Symposium on Visual Communication and Image Processing (VCIP), a member of the Organizing Committee of the 2008 ACM Multimedia Conference, 2006 & 2008 IEEE International Conference on Multimedia and Expo (ICME) and 2002 IEEE International Conference on Image Processing (ICIP), and the chair of the IEEE CVPR Workshop on Semantic Learning Application in Multimedia (SLAM) since its inception in 2006. He is the Editor-in-Chief for the Journal of Multimedia (Academy Publisher). Currently, he is also on the editorial boards of the IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI), the IEEE Transactions on Multimedia (TMM), Pattern Recognition (PR), and Journal of Electronic Imaging (JEI). He is a guest editor for a number of influential special issues, including “Image Understanding for Digital Photos” (PR, 2005), “Real-World Image Annotation and Retrieval” (TPAMI, 2008), “Event Analysis in Video” (IEEE Transactions on Circuits and Systems for Video Technology, 2008), “Integration of Content and Context for Multimedia Management” (TMM, 2009), and “Probabilistic Graphic Models in Computer Vision” (TPAMI, 2009). Dr. Luo is an adjunct professor at Rochester Institute of Technology, as well as the co-dvisor or thesis committee member of many PhD and MS graduate students in various US universities. He is a Kodak Distinguished, a Fellow of SPIE for achievements in electronic imaging and visual communication, and a Fellow of IEEE for contributions to semantic image understanding and intelligent image processing.

Lab Meeting April 20, 2009 (Casey): Avoiding moving outliers in visual SLAM by tracking moving objects

Title: Avoiding moving outliers in visual SLAM by tracking moving objects
Authors: Somkiat Wangsiripitak and David W Murray

In: ICRA 2009

Abstract:

To work at video rate, the maps that monocular SLAM builds are bound to be sparse, making them sensitive to the erroneous inclusion of moving points and to the deletion of valid points through temporary occlusion. This paper describes the parallel implementation of monoSLAM with a 3D object tracker, allowing reasoning about moving objects and occlusion. The SLAMprocess provides the object tracker with information to register objects to the map’s frame, and the object tracker allows the marking of features, either those on objects, or those created by their occluding edges, or those occluded by objects. Experiments are presented to verify the recovered geometry and to indicate the impact on camera pose in monoSLAM of including and avoiding moving features.

[Download]

Lab Meeting April 20, 2009 (Yuchun): An Affective Guide Robot in a Shopping Mall

Title: An Affective Guide Robot in a Shopping Mall
Authors: Takayuki Kanda, Masahiro Shiomi, Zenta Miyashita, Hiroshi Ishiguro and Norihiro Hagita HRI 2009: 173-180

Abstract:
To explore possible robot tasks in daily life, we developed a guide robot for a shopping mall and conducted a field trial with it. The robot was designed to interact naturally with customers and to affectively provide shopping information. It was also designed to repeatedly interact with people to build a rapport; since a shopping mall is a place people repeatedly visit, it provides the chance to explicitly design a robot for multiple interactions. For this capability, we used RFID tags for person identification. The robot was semi- autonomous, partially controlled by a human operator, to cope with the difficulty of speech recognition in a real environment and to handle unexpected situations.

A field trial was conducted at a shopping mall for 25 days to observe how the robot performed this task and how people interacted with it. The robot interacted with approximately 100 groups of customers each day. We invited customers to sign up for RFID tags and those who participated answered questionnaires. The results revealed that 63 out of 235 people in fact went shopping based on the information provided by the robot. The experimental results suggest promising potential for robots working in shopping malls.

[pdf]

CMU talk: Looking without Seeing is in fact Seeing without Knowing -- Insights from Gaze-tracked Change Blindness Studies

Center for the Neural Basis of Cognition Seminar:

Looking without Seeing is in fact Seeing without Knowing
-- Insights from Gaze-tracked Change Blindness Studies

Stella X. Yu
Clare Boothe Luce Assistant Professor
Computer Science @ Boston College
http://www.cs.bc.edu/~syu

Tuesday, April 28

Abstract: Change blindness experiments demonstrate that human vision often neglects certain aspects of the visual scene while attending to others. Using a gaze-tracked flicker paradigm and synthetic images equally rendered in three fundamental features, we explore whether and how innate feature processing might be responsible for the blindness to a change. Our analysis of detection accuracy, detection time, and gaze patterns in this active visual search task reveals distinctive feature extraction, discrimination, and selectivity of size, color, and orientation that underly different behaviours of change blindness. With an array of two-feature stimuli where a single element could change in either feature dimension, we discover that what is changing is sensed long before the subject consciously detects the change, and the change detection task is not accomplished in a single thread of searching for a nonspecific change, but in three separate threads: sensing what the change is, localizing where it might be, and discerning how it is actualized.


Bio: Stella X. Yu got her Ph.D. from the School of Computer Science at Carnegie Mellon University, where she studied robotics at the Robotics Institute and vision science at the Center for the Neural Basis of Cognition. She then went to the University of California, Berkeley to continue her research on computer vision. Her research interests are visual perception and computer vision. Since she joined Boston College, Dr. Yu has been developing an interdisciplinary curriculum and research agenda around art and vision. Her 5-year NSF CAREER proposal, entitled Art and Vision: Scene Layout from Pictorial Cues, was awarded in 2007. Her recent works include image segmentation, object matching, spatial layout categorization and inference, change blindness, and brightness perception.