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18th International Conference on Intelligent User Interfaces, IUI 2013

作者： Knox, W. Bradley Stone, Peter Massachusetts Institute of Technology Media Lab. Cambridge MA United States University of Texas at Austin Department of Computer Science Austin TX United States

ISBN: (纸本)9781450320559

Recent research has demonstrated that human-generated reward signals can be effectively used to train agents to perform a range of reinforcement learning tasks. Such tasks are either episodic - i.e., conducted in unconnected episodes of activity that often end in either goal or failure states - or continuing - i.e., indefinitely ongoing. Another point of difference is whether the learning agent highly discounts the value of future reward - a myopic agent - or conversely values future reward appreciably. In recent work, we found that previous approaches to learning from human reward all used myopic valuation [7]. This study additionally provided evidence for the desirability of myopic valuation in task domains that are both goal-based and episodic. In this paper, we conduct three user studies that examine critical assumptions of our previous research: task episodicity, optimal behavior with respect to a Markov Decision Process, and lack of a failure state in the goal-based task. In the first experiment, we show that converting a simple episodic task to non-episodic (i.e., continuing) task resolves some theoretical issues present in episodic tasks with generally positive reward and - relatedly - enables highly successful learning with non-myopic valuation in multiple user studies. The primary learning algorithm in this paper, which we call "VI-TAMER", is the first algorithm to successfully learn non-myopically from human-generated reward;we also empirically show that such non-myopic valuation facilitates higherlevel understanding of the task. Anticipating the complexity of real-world problems, we perform two subsequent user studies - one with a failure state added - that compare (1) learning when states are updated asynchronously with local bias - i.e., states quickly reachable from the agent's current state are updated more often than other states - to (2) learning with the fully synchronous sweeps across each state in the VI-TAMER algorithm. With these locally biase

关键词： Learning algorithms

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Cloud-based evaluation framework for big data

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2013 Annual Future Internet Assembly, FIA 2013

作者： Hanbury, Allan Müller, Henning Langs, Georg Menze, Bjoern H. Institute of Software Technology and Interactive Systems Vienna University of Technology Austria Switzerland CIR Lab. Department of Radiology Medical University of Vienna Austria Computer Vision Laboratory ETH Zürich Switzerland

ISBN: (纸本)9783642380815

The VISCERAL project is building a cloud-based evaluation framework for evaluating machine learning and information retrieval algorithms on large amounts of data. Instead of downloading data and running evaluations locally, the data will be centrally availab.e on the cloud and algorithms to be evaluated will be programmed in computing instances on the cloud, effectively bringing the algorithms to the data. This approach allows evaluations to be performed on Terabytes of data without needing to consider the logistics of moving the data or storing the data on local infrastructure. After discussing the challenges of benchmarking on big data, the design of the VISCERAL system is presented, concentrating on the components for coordinating the participants in the benchmark and managing the ground truth creation. The first two benchmarks run on the VISCERAL framework will be on segmentation and retrieval of 3D medical images. © The Author(s).

关键词： Information retrieval

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A flexible relational feature model for fall detection

A flexible relational feature model for fall detection

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IASTED International Conference on Signal Processing, Pattern Recognition and Applications, SPPRA 2013

作者： Zweng, Andreas Rittler, Thomas Kampel, Martin Computer Vision Lab. Vienna University of Technology Favoritenstr. 9/183-2 Vienna Austria Cogvis GmbH Wiedner Hauptstr. 17/3a Vienna Austria

vision based fall detection solutions support elderly who live alone in their homes. For people falling down and not being able to stand up on their own again, such a fall is a major risk. In this work, we show a person detection approach using a relational feature model using consumer depth cameras. We propose a flexible relational feature model (FRFM) for fall detection in combination with histogram similarity functions such as the bhattacharyya distance, histogram intersection, histogram correlation and the chi-square x2 histogram similarity function. FRFM is an extension of the relational feature model (RFM) with the advantage that it can be used for all rotations of a body. The extension is necessary for fall detection due to the fact, that a lying person is rotated in the image where the standard person detection approach detects upright standing persons only. The relational features are computed for verification situations during fall detection on the basis of the Histograms of Oriented Gradients (HOG) feature descriptor. The experimental results show the best setup parameters for our feature model with different types of images (RGB and depth) and results on the specific field of fall detection.

关键词： Object detection

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Detecting anomalous behaviors using structural properties of social networks

Detecting anomalous behaviors using structural properties of...

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6th International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction, SBP 2013

作者： Altshuler, Yaniv Fire, Michael Shmueli, Erez Elovici, Yuval Bruckstein, Alfred Pentland, Alex Lazer, David MIT Media Lab. United States Deutsche Telekom Lab. Department of Information Systems Engineering Ben-Gurion University Israel Computer Science Department Technion - Israeli Institute of Technology Israel College of Computer and Information Science Department of Political Science Northeastern University United States

ISBN: (纸本)9783642372094

In this paper we discuss the analysis of mobile networks communication patterns in the presence of some anomalous "real world event". We argue that given limited analysis resources (namely, limited number of network edges we can analyze), it is best to select edges that are located around 'hubs' in the network, resulting in an improved ability to detect such events. We demonstrate this method using a dataset containing the call log data of 3 years from a major mobile carrier in a developed European nation. © 2013 Springer-Verlag.

关键词： Wireless networks

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Fast anatomical structure localization using top-down image patch regression

Fast anatomical structure localization using top-down image ...

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15th International Conference on Medical Image Computing and computer-Assisted Intervention, MICCAI 2012

作者： Donner, René Menze, Bjoern H. Bischof, Horst Langs, Georg Computational Image Analysis and Radiology Lab. Department of Radiology Medical University Vienna Austria Institute for Computer Graphics and Vision Graz University of Technology Austria CSAIL MIT Cambridge MA United States Asclepios Project INRIA Sophia-Antipolis France Computer Vision Laboratory ETH Zurich Switzerland

ISBN: (纸本)9783642366192

Fully automatic localization of anatomical structures in 2D and 3D radiological data sets is important in both computer aided diagnosis, and the rapid automatic processing of large amounts of data. We present a simple, accurate and fast approach with low computational complexity to find anatomical landmarks, based on a multi-scale regression codebook of informative image patches and encoded landmark contexts. From a set of annotated training volumes the method captures the appearance of landmarks over several scales together with relative positions of neighboring landmarks and a spatial distribution model. During multi-scale search in a target volume, starting from the coarsest level, each landmark model predicts all landmark positions it has encoded, with the median of all predictions yielding the final prediction for each scale. We present results on two challenging data sets (hand radiographs and hand CTs), where our method achieves comparable accuracy to the state of the art with substantially improved run-time. © 2013 Springer-Verlag.

关键词： computer aided diagnosis

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Tire Defect Detection Using Local and Global Features

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computer Aided Drafting,Design and Manufacturing 2013年第4期23卷 49-52页

作者： XIANG Yuan-yuan School of Computer Science and Technology Shandong University of Finance and Economics Shandong Key Lab. of Digital Media Technology

In this paper, we present a tire defect detection algorithm based on sparse representation. The dictionary learned from reference images can efficiently represent the test image. As the representation coefficients of normal images have a specific distribution, the local feature can be estimate by comparing representation coefficient distribution. Meanwhile, a coding length is used to measure the global features of representation coefficients. The tire defect is located by both these local and global features. Experimental results demonstrate that the proposed method can accurately detect and locate the tire defects.

关键词： defect detection algorithm local and global features

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A new nonlocal maximum likelihood estimation method for denoising magnetic resonance images

A new nonlocal maximum likelihood estimation method for deno...

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5th International Conference on Pattern Recognition and Machine Intelligence, PReMI 2013

作者： Rajan, Jeny Den Dekker, Arnold J. Juntu, Jaber Sijbers, Jan Department of Computer Science and Engineering National Institute of Technology - Karnataka Surathkal India iMinds Vision Lab. Department of Physics University of Antwerp Belgium Delft Center for Systems and Control Delft University of Technology 2628 CD Delft Netherlands

ISBN: (纸本)9783642450617

Denoising of Magnetic Resonance images is important for proper visual analysis, accurate parameter estimation, and for further preprocessing of these images. Maximum Likelihood (ML) estimation methods were proved to be very effective in denoising Magnetic Resonance (MR) images. Among the ML based methods, the recently proposed Non Local Maximum Likelihood (NLML) approach gained much attention. In the NLML method, the samples for the ML estimation of the true underlying intensity are selected in a non local way based on the intensity similarity of the pixel neighborhoods. This similarity is generally measured using the Euclidean distance. A drawback of this approach is the usage of a fixed sample size for the ML estimation and, as a result, optimal results cannot be achieved because of over- or under-smoothing. In this work, we propose an NLML estimation method for denoising MR images in which the samples are selected in an adaptive way using the Kolmogorov-Smirnov (KS) similarity test. The method has been tested both on simulated and real data, showing its effectiveness. © Springer-Verlag 2013.

关键词： Magnetic resonance imaging

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Integrating 3D features and virtual visual servoing for hand-eye and humanoid robot pose estimation

Integrating 3D features and virtual visual servoing for hand...

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IEEE-RAS International Conference on Humanoid Robots

作者： Xavi Gratal Christian Smith Mårten Björkman Danica Kragic Computer Vision and Active Perception Lab. Royal Institute of Technology KTH Stockholm Sweden

In this paper, we propose an approach for vision-based pose estimation of a robot hand or full-body pose. The method is based on virtual visual servoing using a CAD model of the robot and it combines 2-D image features with depth features. The method can be applied to estimate either the pose of a robot hand or pose of the whole body given that its joint configuration is known. We present experimental results that show the performance of the approach as demonstrated on both a mobile humanoid robot and a stationary manipulator.

关键词： Robots Solid modeling Feature extraction Image edge detection Estimation Cameras Joints

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Facial landmark localization based on hierarchical pose regression with cascaded random ferns 13

Facial landmark localization based on hierarchical pose regr...

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21st ACM International Conference on Multimedia, MM 2013

作者： Zhang, Zhanpeng Zhang, Wei Liu, Jianzhuang Tang, Xiaoou Shenzhen Key Lab of Computer Vision and Pattern Recognition Chinese Academy of Sciences Shenzhen Institutes of Advanced Technology China Department of Information Engineering Chinese University of Hong Kong Hong Kong Media Lab Huawei Technologies Co. Ltd. China

ISBN: (纸本)9781450324045

The main challenge of facial landmark localization in realworld application is that the large changes of head pose and facial expressions cause substantial image appearance variations. To avoid high dimensional regression in the 3D and 2D facial pose spaces simultaneously, we propose a hierarchical pose regression approach, estimating the head rotation, facial components and landmarks hierarchically. The regression process works in a unified cascaded fern framework. We present generalized gradient boosted ferns (GBFs) for the regression framework, which give better performance than traditional ferns. The framework also achieves real time performance. We verify our method on the latest benchmark datasets. The results show that it outperforms state-of-theart methods in both accuracy and speed. Copyright © 2013 ACM.

关键词： Regression analysis

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Multi-feature canonical correlation analysis for face photo-sketch image retrieval 13

Multi-feature canonical correlation analysis for face photo-...

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21st ACM International Conference on Multimedia, MM 2013

作者： Gong, Dihong Li, Zhifeng Liu, Jianzhuang Qiao, Yu Shenzhen Key Lab of Computer Vision and Pattern Recognition Chinese Academy of Sciences Shenzhen Institutes of Advanced Technology China Media Lab Huawei Technologies Co. Ltd. China Department of Information Engineering Chinese University of Hong Kong Hong Kong

ISBN: (纸本)9781450324045

Automatic face photo-sketch image retrieval has attracted great attention in recent years due to its important applications in real life. The major difficulty in automatic face photo-sketch image retrieval lies in the fact that there exists great discrepancy between the different image modalities (photo and sketch). In order to reduce such discrepancy and improve the performance of automatic face photo-sketch image retrieval, we propose a new framework called multi-feature canonical correlation analysis (MCCA) to effectively address this problem. The MCCA is an extension and improvement of the canonical correlation analysis (CCA) algorithm using multiple features combined with two different random sampling methods in feature space and sample space. In this framework, we first represent each photo or sketch using a patch-based local feature representation scheme, in which histograms of oriented gradients (HOG) and multi-scale local binary pattern (MLBP) serve as the local descriptors. Canonical correlation analysis (CCA) is then performed on a collection of random subspaces to construct an ensemble of classifiers for photo-sketch image retrieval. Extensive experiments on two public-domain face photo-sketch datasets (CUFS and CUFSF) clearly show that the proposed approach obtains a substantial improvement over the state-of-the-art. Copyright © 2013 ACM.

关键词： Image retrieval

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