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Supervised Local Subspace Learning for Continuous Head Pose Estimation

Supervised Local Subspace Learning for Continuous Head Pose ...

引用

IEEE Conference on computer vision and Pattern Recognition

作者： Dong Huang Markus Storer Fernando De la Torre Horst Bischof Robotics Institute Carnegie Mellon University Institute for Computer Graphics and Vision Graz University of Technology

ISBN: (纸本)9781457703942

Head pose estimation from images has recently attracted much attention in computer vision due to its diverse applications in face recognition, driver monitoring and human computer interaction. Most successful approaches to head pose estimation formulate the problem as a nonlinear regression between image features and continuous 3D angles (i.e. yaw, pitch and roll). However, regression-like methods suffer from three main drawbacks: (1) They typically lack generalization and overfit when trained using a few samples. (2) They fail to get reliable estimates over some regions of the output space (angles) when the training set is not uniformly sampled. For instance, if the training data contains under-sampled areas for some angles. (3) They are not robust to image noise or occlusion. To address these problems, this paper presents Supervised Local Subspace Learning (SL~2), a method that learns a local linear model from a sparse and non-uniformly sampled training set. SL~2 learns a mixture of local tangent spaces that is robust to under-sampled regions, and due to its regularization properties it is also robust to over-fitting. Moreover, because SL~2 is a generative model, it can deal with image noise. Experimental results on the CMU Multi-PIE and BU-3DFE database show the effectiveness of our approach in terms of accuracy and computational complexity.

关键词： image noise pose estimation Head images computer human interaction Subspace image feature linear models Nonlinear Regression computer vision complexity classes

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Lidar based off-road negative obstacle detection and analysis

Lidar based off-road negative obstacle detection and analysi...

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International Conference on Intelligent Transportation

作者： Jacoby Larson Mohan Trivedi Computer Vision and Robotics Research Laboratory University of California San Diego La Jolla CA USA

In order for an autonomous unmanned ground vehicle (UGV) to drive in off-road terrain at high speeds, it must analyze and understand its surrounding terrain in realtime: it must know where it intends to go, where are the hazards, and many details of the topography of the terrain. Much research has been done in the way of obstacle avoidance, terrain classification, and path planning, but still so few UGV systems can accurately traverse off-road environments at high speeds autonomously. One of the most dangerous hazards found off-road are negative obstacles, mainly because they are so difficult to detect. We present algorithms that analyze the terrain using a point cloud produced by a 3D laser range finder, then attempt to classify the negative obstacles using both a geometry-based method we call the Negative Obstacle DetectoR (NODR) as well as a support vector machine (SVM) algorithm. The terrain is analyzed with respect to a large UGV with the sensor mounted up high as well as a small UGV with the sensor mounted low to the ground.

关键词： Laser radar Vehicles Support vector machines Robot sensing systems Lasers Three dimensional displays Equations

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Clustering-based Discriminative Locality Alignment for Face Gender Recognition

Clustering-based Discriminative Locality Alignment for Face ...

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IEEE/RSJ International Conference on Intelligent robotics and Systems

作者： Duo Chen Jun Cheng Dacheng Tao College of Communication Engineering Chongqing University Chongqing 400044 China. He is also with the Shenzhen Key Laboratory of Computer Vision and Pattern Recognition Shenzhen Institutes of Advanced Technology Chinese Academy of Sciences. Shenzhen Institutes of Advanced Technology Chinese Academy of Sciences Shenzhen 518055 China. He is also with the Chinese University of Hong Kong and Guangdong Provincial Key Laboratory of Robotics and Intelligent System. Center for Quantum Computation and Intelligent System Faculty of Engineering and Information Technology University of Technology Sydney New South Wales 2007 Australia.

ISBN: (数字)9781467317368

ISBN: (纸本)9781467317375

To facilitate human-robot interactions, human gender information is very important. Motivated by the success of manifold learning for visual recognition, we present a novel clustering-based discriminative locality alignment (CDLA) algorithm to discover the low-dimensional intrinsic submanifold from the embedding high-dimensional ambient space for improving the face gender recognition performance. In particular, CDLA exploits the global geometry through k-means clustering, extracts the discriminative information through margin maximization and explores the local geometry through intra cluster sample concentration. These three properties uniquely characterize CDLA for face gender recognition. The experimental results obtained from the FERET data sets suggest the superiority of the proposed method in terms of recognition speed and accuracy by comparing with several representative methods.

关键词： FERET CDLA comparing Recognition (Psychology) Gender Global geometry Compare Face Face

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Algorithm for efficient 3D reconstruction of outdoor environments using mobile robots

Algorithm for efficient 3D reconstruction of outdoor environ...

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2011 IEEE International Conference on robotics and Automation, ICRA 2011

作者： Fentanes, Jaime Pulido Zalama, Eduardo Gómez-García-Bermejo, Jaime CARTIF Foundation Spain Universidad de Valladolid Spain CARTIF Centro Tecnológico Dep. Of Robotics and Computer Vision Boecillo Valladolid Spain Dep. Of Systems Engineering and Automatic Control Industrial Engineering School Spain

ISBN: (纸本)9781612843865

In this paper, an algorithm for the reconstruction of an outdoor environment using a mobile robot is presented. The focus of this algorithm is making the mapping process efficient by capturing the greatest amount of information on every scan, ensuring at the same time that the overall quality of the resulting 3D model of the environment complies with the specified standards. With respect to existing approaches, the proposed approach is an innovation since there are very few information based methods for outdoor reconstruction that use resulting model quality and trajectory cost estimation as criteria for view planning. © 2011 IEEE.

关键词： Mobile robots

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A non-linear diffeomorphic framework for prostate multimodal registration

A non-linear diffeomorphic framework for prostate multimodal...

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2011 International Conference on Digital Image Computing: Techniques and Applications, DICTA 2011

作者： Mitra, Jhimli Kato, Zoltan Martí, Robert Oliver, Arnau Lladó, Xavier Ghose, Soumya Vilanova, Joan C. Meriaudeau, Fabrice Le2i-UMR CNRS 5158 Université de Bourgogne Le Creusot France Computer Vision and Robotics Group Universitat de Girona Girona Spain Department of Image Processing and Computer Graphics University of Szeged Szeged Hungary Girona Magnetic Resonance Center Girona Spain

ISBN: (纸本)9780769545882

This paper presents a novel method for non-rigid registration of prostate multimodal images based on a nonlinear framework. The parametric estimation of the non-linear diffeomorphism between the 2D fixed and moving images has its basis in solving a set of non-linear equations of thin-plate splines. The regularized bending energy of the thin-plate splines along with the localization error of established correspondences is jointly minimized with the fixed and transformed image difference, where, the transformed image is represented by the set of non-linear equations defined over the moving image. The traditional thin-plate splines with established correspondences may provide good registration of the anatomical targets inside the prostate but may fail to provide improved contour registration. On the contrary, the proposed framework maintains the accuracy of registration in terms of overlap due to the non-linear thinplate spline functions while also producing smooth deformations of the anatomical structures inside the prostate as a result of established corrspondences. The registration accuracies of the proposed method are evaluated in 20 pairs of prostate midgland ultrasound and magnetic resonance images in terms of Dice similarity coefficient with an average of 0.982 ± 0.004, average 95% Hausdorff distance of 1.54 ± 0.46 mm and mean target registration and target localization errors of 1.90±1.27 mm and 0.15 ± 0.12 mm respectively. © 2011 IEEE.

关键词： Ultrasonics

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An adaptable vision system for the automatic inspection of surface defects in automotive headlamp lenses

An adaptable vision system for the automatic inspection of s...

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IEEE International Conference on Automation Science and Engineering (CASE)

作者： S. Satorres Martínez J. Gómez Ortega A. Sánchez García J. Gámez García Corresponding author is with the Group of Robotics Automation and Computer Vision University of Jaen Spain Universidad de Jaen Jaen JaÃ©n ES

As a great part of the industrial inspection problems require specific, expensive, and highly complex solutions, there is the need to build flexible systems capable to be easily adapted to different models of a part. In this context, this paper proposes the use of a component-based methodology for designing and building flexible inspection systems for automotive headlamp lenses. Through computer vision, and taking into account the different elements that can be utilized in an industrial environment, we propose the use of a component based architecture for easily designing and building industrial inspection systems for this kind of parts. Within this architecture, each software, or hardware element, is encapsulated in a component with a predefined input/ouput layout and, later, it can be used considering the task inspection requirements. The main advantages of the proposed working philosophy are its flexibility, that the resulting machine vision can be easily adapted to any kind of headlamp lens, and that it is fully open to any new component (lighting system, process algorithm, industrial conveyor, etc.). This proposition has been successfully validated building an industrial prototype that was tested in both, the lab, and in a production line.

关键词： Inspection Lenses Lighting Cameras Machine vision Software Hardware

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Statistical shape and probability prior model for automatic prostate segmentation

Statistical shape and probability prior model for automatic ...

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2011 International Conference on Digital Image Computing: Techniques and Applications, DICTA 2011

作者： Ghose, Soumya Oliver, Arnau Martí, Robert Lladó, Xavier Freixenet, Jordi Mitra, Jhimli Vilanova, Joan C. Comet, Josep Meriaudeau, Fabrice Le2i-UMR CNRS 5158 Université de Bourgogne Le Creusot France Computer Vision and Robotics Group Universitat de Girona Girona Spain Girona Magnetic Resonance Center Girona Spain University Hospital Dr. Josep Trueta Girona Spain

ISBN: (纸本)9780769545882

Accurate prostate segmentation in Trans Rectal Ultra Sound (TRUS) images is an important step in different clinical applications. However, the development of computer aided automatic prostate segmentation in TRUS images is a challenging task due to low contrast, heterogeneous intensity distribution inside the prostate region, imaging artifacts like shadow, and speckle. Significant variations in prostate shape, size and contrast between the datasets pose further challenges to achieve an accurate segmentation. In this paper we propose to use graph cuts in a Bayesian framework for automatic initialization and propagate multiple mean parametric models derived from principal component analysis of shape and posterior probability information of the prostate region to segment the prostate. The proposed framework achieves a mean Dice similarity coefficient value of 0.974±0.006, mean mean absolute distance value of 0.49±0.20 mm and mean Hausdorff distance of 1.24±0.56 mm when validated with 23 datasets in a leave-one-patient-out validation framework. © 2011 IEEE.

关键词： Principal component analysis

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Boosting based object detection using a geometric model

Boosting based object detection using a geometric model

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IEEE International Conference on Image Processing

作者： Katharina Quast Christoph Seeger Mohan Trivedi André Kaup Multimedia Communications and Signal Processing University of Erlangen-Nuremberg Erlangen Germany Computer Vision and Robotics Research University of California San Diego La Jolla CA USA

In this paper we present a new method for automatic object detection in images and video sequences. As a classifier the popular Ad aBoost algorithm is used, that combines several weak classifiers into one strong classifier. To create a detector based on this classifier, the weak classifiers are set into relation during boosting by using a geometric model. All votes of the weak detectors are evaluated in a voting space. The voting space allows a detection with combinations of different object features. We trained and tested the proposed method with SIFT and kAS features and combinations of these. The learned detector is then used to localize objects in images and video sequences. The performance of the algorithm is examined based on selected image data.

关键词： Detectors Feature extraction Vectors Boosting Training Kernel Object detection

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Nonverbal communication with a multimodal agent via facial expression recognition

Nonverbal communication with a multimodal agent via facial e...

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IEEE International Conference on robotics and Automation (ICRA)

作者： Samuel Marcos Jaime Gómez-García-Bermejo Eduardo Zalama Joaquín López Robotics and Computer Vision Division CARTIF Centro Tecnológico Valladolid Spain Department of Systems Engineering and Automatic Control Industrial Engineering School University of Valladolid Spain

Based on our previous work in the development of a multimodal animated avatar oriented to human-machine interaction and social robotics, a facial expression recognition system is presented in this paper. Both the avatar's architecture and facial recognition system have been oriented to FACS Action Units recognition and generation, so they can be easily integrated. The resulting animated system is able to maintain nonverbal and verbal bidirectional communication, enhancing the interaction with the avatar and making it more natural for the users.

关键词： Gabor filters Face Mouth Shape Eyebrows Face recognition Feature extraction

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An accurate 3D feature tracking system with wide-baseline stereo smart cameras

An accurate 3D feature tracking system with wide-baseline st...

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ACM/IEEE International Conference on Distributed Smart Cameras, ICDSC

作者： Dominik Rueß Kristian Manthey Ralf Reulke German AeroSpace Center Optical Information Systems Institute of Robotics and Mechatronics Berlin Germany Department of Computer Science Research and Lecture Group Computer Vision Humboldt-Universität zu Berlin Berlin Germany

A typical video surveillance system consists of at least one camera, controlled by an operator. To decrease the human error rate and to generally lessen the burden of operators, many object tracking systems have been implemented, most of which work in 2D image space. If used centralized, this is a very expensive task. Furthermore, if several views are to be fused, large inaccuracies arise due to ground plane assumptions, for instance. Lastly, in outdoor setups, quite often there is a need for slower channels like Wireless LAN which cannot cope with the full resolution data stream. We provide a smart camera system which performs the intensive tasks like background estimation or feature extraction. A central unit only has to process the received data in feature space, increasing scalability. Additionally, the object tracking problem is converted to an accurate 3D feature tracking, avoiding difficulties such as proper object segmentation and adding increased trajectory accuracy. The feature regions are computed within the smart camera. A wide-baseline feature matching approach has been employed to allow more freedom in the placement of the single smart cameras.

关键词： Trajectory Three dimensional displays Cameras Smart cameras Estimation Image reconstruction Feature extraction

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