Lab Meeting April 27th (Andi): Error Modeling and Calibration of Exteroceptive Sensors for Accurate Mapping Applications

Authors: James P. Underwood, Andrew Hill, Thierry Peynot, and Steven J. Scheding
ARC Centre of Excellence for Autonomous Systems, Australian Centre for Field Robotics

Abstract: Reliable robotic perception and planning are critical to performing autonomous actions in uncertain, unstructured environments. In field robotic systems, automation is achieved by interpreting exteroceptive sensor information to infer something about the world. This is then mapped to provide a consistent spatial context, so that actions can be planned around the predicted future interaction of the robot and the world. The whole system is as reliable as the weakest link in this chain. In this paper, the term mapping is used broadly to describe the transformation of range-based exteroceptive sensor data (such as LIDAR or stereo vision) to a fixed navigation frame, so that it can be used to form an internal representation of the environment. The coordinate transformation from the sensor frame to the navigation frame is analyzed to produce a spatial error model that
captures the dominant geometric and temporal sources of mapping error. This allows the mapping accuracy to be calculated at run time. A generic extrinsic calibration method for exteroceptive range-based sensors is then presented to determine the sensor location and orientation. This allows systematic errors in individual sensors to be minimized, and when multiple sensors are used, it minimizes the systematic contradiction between them to enable reliable multisensor data fusion. The mathematical derivations at the core of this model are not particularly novel or complicated, but the rigorous analysis and application to field robotics seems to be largely absent from the literature to date. The techniques in this paper are simple to implement, and they offer a significant improvement to the accuracy, precision, and integrity of mapped information. Consequently, they should
be employed whenever maps are formed from range-based exteroceptive sensor data.

full paper

Lab Meeting April 20th, 2010 (KuoHuei): Directing Crowd Simulations Using Navigation Fields (TVCG 2010)

Title: Directing Crowd Simulations Using Navigation Fields

(to appear in IEEE Transactions on Visualization and Computer Graphics)

Authors: Sachin Patil,Jur van den Berg,Sean Curtis,Ming Lin,Dinesh Manocha

Abstract: We present a novel approach to direct and control virtual crowds using navigation fields. Our method guides one or more agents towards desired goals based on guidance fields. The system allows the user to specify these fields by either sketching paths directly in the scene via an intuitive authoring interface or by importing motion flow fields extracted from crowd video footage. We propose a novel formulation to blend input guidance fields to create singularity-free, goal-directed navigation fields. Our method can be easily combined with most current local collision-avoidance methods and we use two such methods as examples to highlight the potential of our approach. We illustrate its performance on several simulation scenarios.

Link: [web] [pdf]

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