MIT Talk: Biomedical Imaging and Analysis Seminar

Biomedical Imaging and Analysis Seminar

Speaker: Arno Klein , Columbia University

Date: Friday, July 31 2009

Time: 2:00PM to 3:00PM

Location: 32-D507

Host: Thomas Yeo, CSAIL

Contact: Thomas Yeo, 617-253-4143, ythomas@csail.mit.edu

Establishing correspondences across brains for the purposes of comparison and group analysis is almost universally done by registering images to one another either directly or via a template. However, there are many registration algorithms to choose from. The first part of this talk will give an overview of a recent evaluation study comparing fully automated nonlinear deformation methods applied to brain image registration (Klein et al. 2009). This study was restricted to volume-based methods, and an ongoing extension of this study is the first known to the authors that directly compares some of the most accurate of these methods with surface-based registration methods, as well as the first study to compare registrations of whole-head and de-skulled brain images. More than 6,000 registrations between 40 manually labeled brain images have been performed so far by the volume-based algorithms ART and SyN and the surface-based algorithms FreeSurfer and Spherical Demons. We used permutation tests and indifference-zone ranking to compare the overlap performance for eight scenarios: ART and SyN on brain images with and without skulls, SyN, FreeSurfer, and Spherical Demons via custom templates, and FreeSurfer via its default atlas.

Link

Phd oral exam : Large Scale Scene Matching for Graphics and Vision

July 29, 2009
James H. Hays
11:00 AM, 7220 Wean Hall
Thesis Oral
Title: Large Scale Scene Matching for Graphics and Vision

Abstract:
Our visual experience is extraordinarily varied and complex. Thediversity of the visual world makes it difficult for computervision to understand images and for computer graphics tosynthesize visual content. But for all its richness, it turns outthat the space of "scenes" might not be astronomically large.With access to imagery on an Internet scale, regularities start toemerge - for most images, there exist numerous examples ofsemantically and structurally similar scenes. Is it possible tosample the space of scenes so densely that one can use similarscenes to "brute force" otherwise difficult image understandingand manipulation tasks? This thesis is focused on exploiting andrefining large scale scene matching to short circuit thetypical computer vision and graphics pipelines for imageunderstanding and manipulation.

First, in "Scene Completion" we patch up holes in images bycopying content from matching scenes. We find scenes so similarthat the manipulations are undetectable to naive viewers and wequantify our success rate with a perceptual study. Second, in"im2gps" we estimate geographic properties and globalgeolocation for photos using scene matching with a database of 6million geo-tagged Internet images. We geolocate sequences ofphotos four times as accurately as the single image case bymodelling the global spatiotemporal statistics of photographers.We introduce a range of features for scene matching and use them,together with lazy SVM learning, to dramatically improve scenematching -- doubling the performance of single image geolocationover our baseline method. Third, we study human photo geolocationto gain insights into the geolocation problem, our algorithms, andhuman scene understanding. This study shows that our algorithmssignificantly exceed human geolocation performance. Finally, weuse our geography estimates, as well as Internet text annotations,to provide context for deeper image understanding, such as objectdetection.

Thesis Committee:Alexei A. Efros, ChairMartial HebertJessica K. HodginsTakeo KanadeRichard Szeliski, Microsoft Research

Lab Meeting August 3, 2009(Shao-Chen): Time-bounded Lattice for Efficient Planning in Dynamic Environments(ICRA09)

Title:Time-bounded Lattice for Efficient Planning in Dynamic Environments(ICRA09)

Authors:Aleksandr Kushleyev, Maxim Likhachev

Abstract

For vehicles navigating initially unknown cluttered environments, current state-of-the-art planning algorithms are able to plan and re-plan dynamically-feasible paths efficiently and robustly. It is still a challenge, however, to deal well with the surroundings that are both cluttered and highly dynamic. Planning under these conditions is more difficult for two reasons. First, tracking and predicting the trajectories of moving objects (i.e., cars, humans) is very noisy. Second, the planning process is computationally more expensive because of the increased dimensionality of the state-space, with time as an additional variable. Moreover, re-planning needs to be invoked more often since the trajectories of moving obstacles need to be constantly re-estimated.

In this paper, we develop a path planning algorithm that addresses these challenges. First, we choose a representation of dynamic obstacles that efficiently models their predicted trajectories and the uncertainty associated with the predictions. Second, to provide real-time guarantees on the performance of planning with dynamic obstacles, we propose to utilize a novel data structure for planning - a time-bounded lattice - that merges together short-term planning in time with longterm planning without time. We demonstrate the effectiveness of the approach in both simulations with up to 30 dynamic obstacles and on real robots.

link

Lab Meeting July 13, 2009 (Alan): An Embodied Cognition Approach to Mindreading Skills for Socially Intelligent Robots (IJRR 2009)

Title: An Embodied Cognition Approach to Mindreading Skills for Socially Intelligent Robots (IJRR 2009)
Authors: Cynthia Breazeal, Jesse Gray, Matt Berlin

Abstract
Future applications for personal robots motivate research into developing robots that are intelligent in their interactions with people. Toward this goal, in this paper we present an integrated socio-cognitive architecture to endow an anthropomorphic robot with the ability to infer mental states such as beliefs, intents, and desires from the observable behavior of its human partner. The design of our architecture is informed by recent findings from neuroscience and embodies cognition that reveals how living systems leverage their physical and cognitive embodiment through simulation-theoretic mechanisms to infer the mental states of others. We assess the robot’s mindreading skills on a suite of benchmark tasks where the robot interacts with a human partner in a cooperative scenario and a learning scenario. In addition, we have conducted human subjects experiments using the same task scenarios to assess human performance on these tasks and to compare the robot’s performance with that of people. In the process, our human subject studies also reveal some interesting insights into human behavior.

Link

Lab Meeting 7/13, 2009(Casey): A Closed-Form Solution to Non-Rigid Shape and Motion Recovery

Title: A Closed-Form Solution to Non-Rigid Shape and Motion Recovery (IJCV 2006)

Authors: J. Xiao, J.Chai, and T.Kanade

Abstract:

Abstract. Recovery ofthree dimensional (3D) shape and motion ofnon-static scenes from a monocular video sequence is important for applications like robot navigation and human computer interaction. Ifev ery point in the scene randomly moves, it is impossible to recover the non-rigid shapes. In practice, many non-rigid objects, e.g. the human face under various expressions, deform with certain structures. Their shapes can be regarded as a weighted combination of certain shape bases. Shape and motion recovery under such situations has attracted much interest. Previous work on this problem [6, 4, 14] utilized only orthonormality constraints on the camera rotations (rotation constraints). This paper proves that using only the rotation constraints results in ambiguous and invalid solutions. The ambiguity arises from the fact that the shape bases are not unique. An arbitrary linear transformation of the bases produces another set ofeligible bases. To eliminate the ambiguity, we propose a set of novel constraints, basis constraints, which uniquely determine the shape bases. We prove that, under the weak-perspective projection model, enforcing both the basis and the rotation constraints leads to a closed-form solution to the problem of non-rigid shape and motion recovery. The accuracy and robustness ofour closed-form solution is evaluated quantitatively on synthetic data and qualitatively on real video sequences.

[Link]

Lab Meeting 7/13, 2009(fish60): Autonomous Robot Navigation in Outdoor Cluttered Pedestrian Walkways

Autonomous Robot Navigation in Outdoor Cluttered Pedestrian Walkways
Yoichi Morales, Alexander Carballo, Eijiro Takeuchi, Atsushi Aburadani, and Takashi Tsubouchi
Journal of Field Robotics 26(8), 609–635 (2009)

Abstract:
This paper describes an implementation of a mobile robot system for autonomous navigationin outdoor concurred walkways. The task was to navigate through nonmodified pedestrian paths with people and bicycles passing by. The proposed approach proved to be robust for outdoor navigationin cluttered and crowded walkways, first on campus paths and then running the challenge course multiple times between trials and the challenge final. The paper reports experimental results and overall performance of the system. Finally the lessons learned are discussed. The main contribution of this work is the report of a system integration approach for autonomous outdoor navigation and its evaluation.

Link

News: Language may be key to theory of mind

Language may be key to theory of mind

• 15:55 23 June 2009 by Anil Ananthaswamy
• For similar stories, visit the The Human Brain Topic Guide

How blind and deaf people approach a cognitive test regarded as a milestone in human development has provided clues to how we deduce what others are thinking.

Understanding another person's perspective, and realising that it can differ from our own, is known as theory of mind. It underpins empathy, communication and the ability to deceive – all of which we take for granted. Although our theory of mind is more developed than it is in other animals, we don't acquire it until around age four, and how it develops is a mystery.

See the full article.

Lab Meeting June 21th, 2009 (swem): Improved Inverse-Depth Parameterization for Monocular Simultaneous Localization and Mapping

Title: Improved Inverse-Depth Parameterization for Monocular
Simultaneous Localization and Mapping
In: ICRA2009

Author: E. Imre, M.-O. Berger, N. Noury

Abstract:

Inverse-depth parameterization can successfully
deal with the feature initialization problem in monocular
simultaneous localization and mapping applications. However,
it is redundant, and when multiple landmarks are initialized
from the same image, it fails to enforce the “common origin”
constraint. The authors propose two new variants that
addresses both of these issues. The experimental results indicate
that the proposed approach achieves a better performance at a
lower computational cost.

[ Link ]

Intelligence Seminar: Action Perception, June 23, 2009

Intelligence Seminar

June 23, 2009 (note special place)
3:30 pm
NSH 1507
Host: Jaime Carbonell
For meetings, contact Michelle Pagnani (pagnani@cs.cmu.edu).

Action Perception

Robert Thibadeau
Seagate Research

Abstract:
The human perception of actions has barely been studied, but this study of action perception promises to provide a wealth of interesting hypotheses regarding cognitive processing. Action perception is distinct from motion perception in that the direct perception of causation is central to the percept. Among the interesting hypotheses is that it can be hypothesized that what we know as thought and reasoning is where we perceive and plan actions. Another hypothesis is that what we know as logic and mathematics derives from our direct perceptions of causation in the actions we perceive and think about.

I will present a study that attempts to estimate the scale of computation needed to implement a system for visually perceiving meaningful actions and non-trivially producing an English narration of what is being visually perceived, as well as answering questions about what is visually perceived. The scale of the computation for learning could easily reach exaflops over distributed datasets (HADOOP or MapReduce style).

This study is partly based on my work (Thibadeau, 1986), and Doug Rohde's 2002 dissertation (http://tedlab.mit.edu:16080/~dr/Thesis/), as well as Simon and Rescher (1966 see summary below). The study includes an explicit proposal for extending Rohde's work to multimodal, multisensory, processing.

(Simon and Rescher 1966 From Wikipedia, Causality)
Derivation theories

The Nobel Prize holder Herbert Simon and Philosopher Nicholas Rescher[20] claim that the asymmetry of the causal relation is unrelated to the asymmetry of any mode of implication that
contraposes. Rather, a causal relation is not a relation between values of variables, but a function of one variable (the cause) on to another (the effect). So, given a system of equations, and a set of
variables appearing in these equations, we can introduce an asymmetricrelation among individual equations and variables that corresponds perfectly to our commonsense notion of a causal ordering. The system of equations must have certain properties, most importantly, if some values are chosen arbitrarily, the remaining values will be determined uniquely through a path of serial discovery that is perfectly causal. They postulate the inherent serialization of such a system of equations may correctly capture causation in all empirical fields, including physics and economics.

RSS 2009 paper: Non-parametric Learning To Aid Path Planning Over Slopes

Title: Non-parametric Learning To Aid Path Planning Over Slopes (RSS 2009)

Sisir Karumanchi, Thomas Allen, Tim Bailey and Steve Scheding
ARC Centre of Excellence For Autonomous Systems (CAS),
Australian Centre For Field Robotics (ACFR),
The University of Sydney,
NSW. 2006, Australia.


Abstract—This paper addresses the problem of closing the loop from perception to action selection for unmanned ground vehicles, with a focus on navigating slopes. A new non-parametric learning technique is presented to generate a mobility representation where maximum feasible speed is used as a criterion to classify the world. The inputs to the algorithm are terrain gradients derived from an elevation map and past observations of wheel slip. It is argued that such a representation can aid in path planning with improved selection of vehicle heading and operating velocity in off-road slopes. Results of mobility map generation and its benefits to path planning are shown.

Link

RSS 2009 Paper: Generalized-ICP

Title: Generalized-ICP (RSS 2009)

Authors: Aleksandr Segal, Dirk Haehnel, Sebastian Thrun

Abstract:

In this paper we combine the Iterative Closest Point (’ICP’) and ‘point-to-plane ICP‘ algorithms into a single probabilistic frame work. We then use this framework to model locally planar surface structure from both scans instead of just the ”model” scan as is typically done with point-to-plane. This can be thought of as ‘plane-to-plane’. The new approach is tested with both simulated and real-world data and is shown to outperform both standard ICP and point-to-lane. Furthermore, the new approach is shown to be more robust to incorrect correspondences, and thus makes it easier to tune the maximum match distance parameter. In addition to the demonstrated performance improvement, the proposed framework allows for more expressive probabilistic models to be incorporated into the ICP framework. While maintaining the speed and simplicity of ICP, Generalized-ICP allows the addition of outlier terms, measurement noise, and other probabilistic techniques to increase robustness.

Link

RSS 2009 Paper: Large Scale Graph-based SLAM using Aerial Images as Prior Information

Title: Large Scale Graph-based SLAM using Aerial Images as Prior Information (RSS 2009)

Authors: Rainer K¨ummerle Bastian Steder Christian Dornhege Alexander Kleiner Giorgio Grisetti Wolfram Burgard

Abstract:

To effectively navigate in their environments and accurately reach their target locations, mobile robots require a globally consistent map of the environment. The problem of learning a map with a mobile robot has been intensively studied in the past and is usually referred to as the simultaneous localization and mapping (SLAM) problem. However, existing solutions to the SLAM problem typically rely on loop-closures to obtain global consistency and do not exploit prior information even if it is available. In this paper, we present a novel SLAM approach that achieves global consistency by utilizing publicly accessible aerial photographs as prior information. Our approach inserts correspondences found between three-dimensional laser range scans and the aerial image as constraints into a graph-based formulation of the SLAM problem. We evaluate our algorithm based on large real-world datasets acquired in a mixed in- and outdoor environment by comparing the global accuracy with state-of-the-art SLAM approaches and GPS. The experimental results demonstrate that the maps acquired with our method show increased global consistency.

Link

Lab Meeting June 15th, 2009 (Andi): Progress Report

I will talk about my recent progress in 3D Mapping and Localization using one 2D LIDAR.

Using the Distribution Theory to Simultaneously Calibrate the Sensors of a Mobile Robot

Title: Using the Distribution Theory to Simultaneously Calibrate the Sensors of a Mobile Robot

Author: Agostino Martinelli

Abstract:
This paper introduces a simple and very efficient strategy to extrinsically calibrate a bearing sensor (e.g. a camera) mounted on a mobile robot and simultaneously estimate the parameters describing the systematic error of the robot odometry system. The paper provides two contributions. The first one is the analytical computation to derive the part of the system which
is observable when the robot accomplishes circular trejectories. This computation consists in performing a local decomposition of the system, based on the theory of distributions. In this respect, this paper represents the first application of the distribution theory in the frame-work of mobile robotics. Then, starting from this decomposition, a method to efficiently estimate the parameters describing both the extrinsic bearing sensor calibration and the odometry calibration is derived (second contribution). Simulations and experiments with the robot e-Puck equipped
with encoder sensors and a camera validate the approach.

Link:
RSS05
http://www.roboticsproceedings.org/rss05/p11.pdf
http://hal.inria.fr/docs/00/35/30/79/PDF/RR-6796.pdf

News: Willow Garage: Milestone 2 Start to Finish

Lab Meeting June 8th, 2009(ZhenYu):Vertical Line Matching for Omnidirectional Stereovision Images

Title: Vertical Line Matching for Omnidirectional Stereovision Images (ICRA2009)

Authors: Guillaume Caron and El Mustapha Mouaddib

Abstract: We are investigating the mobile robot indoor localization and environment mapping using an omnidirectional stereovision sensor. It uses four parabolic mirrors and an orthographic camera, giving four images of the same scene. At least, only two mirrors are needed. Using four mirrors gives redundancy. We propose to exploit the images of vertical lines. This paper presents a new method in order to match these lines in the four images. Contrary to existing approaches, we took into account the four sub-images existence in the design of this method, in order to exploit redundancy. This brought an original algorithm combining matching and pose estimation of vertical lines from the 3D environment. Experimental results will be presented to validate this approach.

[Link]

Lab Meeting June 8th, 2009 (Any): CRF-Filters

Paper title: CRF-Filters: Discriminative Particle Filters for Sequential State Estimation

Authors: Benson Limketkai, Dieter Fox and Lin Liao

Appears in: ICRA 2007

Abstract: Particle filters have been applied with great success to various state estimation problems in robotics. However, particle filters often require extensive parameter tweaking in order to work well in practice. This is based on two observations. First, particle filters typically rely on independence assumptions such as “the beams in a laser scan are independent given the robot’s location in a map”. Second, even when the noise parameters of the dynamical system are perfectly known, the sample-based approximation can result in poor filter performance. In this paper we introduce CRF-Filters, a novel variant of particle filtering for sequential state estimation. CRF-Filters are based on conditional random fields, which are discriminative models that can handle arbitrary dependencies between observations. We show how to learn the parameters of CRF-Filters based on labeled training data. Experiments using a robot equipped with a laser range-finder demonstrate that our technique is able to learn parameters of the robot’s motion and sensor models that result in good localization performance, without the need of additional parameter tweaking.

Full text: PDF

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