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11th Asian Conference on computer vision, ACCV 2012

作者： Li, Chi Lu, Le Hager, Gregory D. Tang, Jianyu Wang, Hanzi Cognitive Science Department Xiamen University Xiamen China Imaging and Computer Vision Siemens Corporate Research Princeton NJ United States Computer Science Department Johns Hopkins University Baltimore MD United States Center for Pattern Analysis and Machine Intelligence Xiamen University China

ISBN: (纸本)9783642374302

In this paper, we exploit robust depth information with simple color-shape appearance model on single object tracking in crowd dynamic scenes. Since binocular video streams are captured from a moving camera rig, background subtraction cannot provide a reliable enhancement of region of interest. Our main contribution is a novel tracking strategy to employ explicit stereo depth to track and segment object in crowd dynamic scenes with occlusion handling. Appearance cues including color and shape play a secondary role to further extract the foreground acquired by previous depth-based segmentation. The proposed depth-driven tracking approach can largely alleviate the drifting issue, especially when the object frequently interacts with similar background in long sequence tracking. The problems caused by rapid object appearance change can also be avoided due to the stability of the depth cue. Furthermore, we propose a new, yet simple and effective depth-based scheme to cope with complete occlusion in tracking. From experiments on a large collection of challenging outdoor and indoor sequences, our algorithm demonstrates accurate and reliable tracking performance which outperforms other state-of-the-art competing algorithms. © 2013 Springer-Verlag.

关键词： Image segmentation

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Towards Practical Visual Servoing in robotics

Towards Practical Visual Servoing in Robotics

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Canadian Conference on computer and Robot vision, CRV

作者： Romeo Tatsambon Fomena Camilo Perez Quintero Mona Gridseth Martin Jagersand Computer Vision and Robotics Research Group Department of Computing Science University of Alberta Edmonton AB Canada

ISBN: (纸本)9781467364096

Visual servoing was introduced in robotics nearly 4 decades ago. However until now, there are still only a handful of known examples of application of this technique in addressing real word robotics problems such as disaster response, assistance for elderly or handicapped people, etc. As the world is moving towards the use of robotics to improve quality of life, it is time to assess the challenges involved in applying visual servoing to solve real world problems. This paper presents an overview of these challenges, by asking the question what are the missing components for practical visual servoing? and by providing practical possible solutions for these components. Illustration of these challenges and our current practical solutions are given using our 7-DoFs Barrett WAM Arm.

关键词： Visual servoing Visualization Cameras Target tracking Robot kinematics

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Encoding Local Binary Patterns using the re-parametrization of the second order Gaussian jet

Encoding Local Binary Patterns using the re-parametrization ...

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International Conference on Automatic Face and Gesture Recognition

作者： John A. Ruiz-Hernandez Matti Pietikäinen Center for Machine Vision Research Department of Computer Science FI University of Oulu Finland

In object recognition a robust feature set is considered as an important component in almost all the approaches proposed in the literature. In facial analysis, one of the best known feature set is based in Local Binary Patterns (LBP) which extracts the information contained in the image using comparisons between pixels in a region, finally such comparisons are encoded in form of histogram. We argue that this kind of encoding is statistically non-stable and can lead to errors during the recognition process, specially in noisy and low-resolution images, where the information contained in the image is not enough to generate a statistically robust histogram. In this paper, we propose a new method to encode the Local Binary Patterns using an re-parametrization of the second local order Gaussian Jet which generates more robust and reliable histograms suitable for different facial analysis tasks. We show that our method can be used for recognizing micro-expressions with competitive performances on the Spontaneous Micro-expression Corpus (SMIC) and the YORK Deception Detection Test.

关键词： Encoding Kernel Histograms Vectors Robustness Image coding Feature extraction

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PROGRAMMABLE LOW POWER IMPLEMENTATION OF THE HEVC ADAPTIVE LOOP FILTER

PROGRAMMABLE LOW POWER IMPLEMENTATION OF THE HEVC ADAPTIVE L...

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IEEE International Conference on Acoustics, Speech, and Signal Processing

作者： Ilkka Hautala Jani Boutellier Jari Hannuksela Center for Machine Vision Research Department of Computer Science and Engineering FI-90014 University of Oulu

ISBN: (纸本)9781479903573

The Adaptive Loop Filter (ALF) is a subjective and objective image quality improving filter in the High Efficiency Video Coding standard (HEVC). The ALF has shown to be computationally complex and its complexity has been reduced during the HEVC development process. In the HEVC Test Model HM-7.0 ALF is a 9×7 cross + 3×3 square shaped filter. This paper presents a programmable application specific instruction processor for the ALF. The proposed processor processes 1920×1080p luminance frames at 30 frames per second, when operated at a clock frequency of 311 MHz. Low power consumption and a low gate count make the proposed processor suitable for embedded devices. The processor program code is written in pure C-language, which allows versatile use of the circuit and updates to the filter functionality without modifying the processor design. To the authors' best knowledge this is the first programmable solution for ALF on embedded devices.

关键词： Coprocessors Adaptive filters Video signal processing Coprocessors Video signal processing adaptive filters GTF2A1L gene PROGRAMMABLE Embedded device clock frequency PROCESSOR Filters LOW POWER

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Analysis of Scores, Datasets, and Models in Visual Saliency Prediction

Analysis of Scores, Datasets, and Models in Visual Saliency ...

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International Conference on computer vision (ICCV)

作者： Ali Borji Hamed R. Tavakoli Dicky N. Sihite Laurent Itti Department of Computer Science University of Southern California Los Angeles Center for Machine Vision Research University of Oulu Finland

ISBN: (纸本)9781479928415

Significant recent progress has been made in developing high-quality saliency models. However, less effort has been undertaken on fair assessment of these models, over large standardized datasets and correctly addressing confounding factors. In this study, we pursue a critical and quantitative look at challenges (e.g., center-bias, map smoothing) in saliency modeling and the way they affect model accuracy. We quantitatively compare 32 state-of-the-art models (using the shuffled AUC score to discount center-bias) on 4 benchmark eye movement datasets, for prediction of human fixation locations and scan path sequence. We also account for the role of map smoothing. We find that, although model rankings vary, some (e.g., AWS, LG, AIM, and HouNIPS) consistently outperform other models over all datasets. Some models work well for prediction of both fixation locations and scan path sequence (e.g., Judd, GBVS). Our results show low prediction accuracy for models over emotional stimuli from the NUSEF dataset. Our last benchmark, for the first time, gauges the ability of models to decode the stimulus category from statistics of fixations, saccades, and model saliency values at fixated locations. In this test, ITTI and AIM models win over other models. Our benchmark provides a comprehensive high-level picture of the strengths and weaknesses of many popular models, and suggests future research directions in saliency modeling.

关键词： Predictive models Visualization Benchmark testing Smoothing methods Analytical models Face Accuracy

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Automated segmentation of blood cells in Giemsa stained digitized thin blood films

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Diagn Pathol 2013年第SUPPL 1期8卷 S37-S37页

作者： Margarita Walliander Riku Turkki Nina Linder Mikael Lundin Juho Konsti Ville Ojansivu Taru Meri Ville Holmberg Johan Lundin Institute for Molecular Medicine Finland (FiMM) Finland Center for Machine Vision Research Department of Computer Science and Engineering University of Oulu Finland Haartman Institute Finland

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Human-robotic collaborative intelligent control for reaching performance

Human-robotic collaborative intelligent control for reaching...

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7th International Conference on Foundations of Augmented Cognition, AC 2013, Held as Part of 15th International Conference on Human-computer Interaction, HCI International 2013

作者： Gentili, Rodolphe J. Oh, Hyuk Shuggi, Isabelle M. Goodman, Ronald N. Rietschel, Jeremy C. Hatfield, Bradley D. Reggia, James A. Department of Kinesiology Cognitive Motor Neuroscience Laboratory University of Maryland College Park MD 20742 United States Neuroscience and Cognitive Science Program University of Maryland College Park MD 20742 United States Maryland Robotics Center University of Maryland College Park MD 20742 United States Institute for Systems Research University of Maryland College Park MD 20742 United States Veterans Health Administration Maryland Exercise and Robotics Center of Excellence Baltimore MD 21201 United States Department of Computer Science University of Maryland College Park MD 20742 United States Maryland Institute for Advanced Computer Studies University of Maryland College Park MD 20742 United States

ISBN: (纸本)9783642394539

In most human-robot interfaces, the user completely controls the robot that operates as a passive tool without adaptation capabilities. However, a synergetic human-robot interface where both agents collaborate could improve the user's performance while reducing the cognitive and physical workload. Specifically, when considering this framework applied to rehabilitation, we examined a shared collaborative control between a human user and an adaptive biologically inspired neurocontroller in order to perform reaching movements with a simulated prosthetic arm. When this neurocontroller was enabled, it progressively learned from the user to control the prosthetic arm, increasing its role in the shared performance and facilitating the user's reaching movements. This resulted in the user's performance enhancement and in a reduction of his/her cognitive workload. The long term goal of this work is to contribute to the development of the next generation of intelligent human-robotic interfaces for rehabilitation. © 2013 Springer-Verlag Berlin Heidelberg.

关键词： Adaptive control systems

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Trajectory generator for autonomous vehicles in urban environments

Trajectory generator for autonomous vehicles in urban enviro...

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IEEE International Conference on robotics and Automation (ICRA)

作者： Joshué Pérez Jorge Godoy Jorge Villagrá Enrique Onieva INRIA research center Paris ROCQUENCOURT France Industrial Computer Science Department Centre of Automation and Robotics (CAR) Consejo Superiorde Investigaciones Cientficas Universidad Politecnica de Madrid Madrid Spain Industrial Computer Science Department UPM-CSIC Madrid Spain Institute of Technology (DeustoTech) University of Deusto Bilbao Spain

Nowadays, some developments in the vehicle industry permit a safe and comfortable driving. However, several manufactures and research groups are still working in the improvement of the control strategies and path smoothing algorithms. In this paper, a new trajectory generation approach for autonomous vehicles in urban scenarios, considering parametric equations, is proposed. An algorithm that considers Bezier curves and circumference parametric equations for a real vehicle, specifically in roundabout and urban intersections is presented. This approach is generated in real time and can be adapted to dynamic changes in the route. A smooth trajectory generator computationally efficient and easily implementable is proposed. Moreover, this new trajectory generator reduces the control actions, generated with to a fuzzy controller. Some trials have been performed in an urban circuit with promising performance.

关键词： Mobile robots Wheels

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面向盲人避障的场景自适应分割及障碍物检测

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计算机辅助设计与图形学学报 2013年第12期25卷 1818-1825页

作者：刘宏王喆王向东赵国英钱跃良中国科学院计算技术研究所普适计算研究中心北京100190 移动计算与新型终端北京市重点实验室北京100190 中国科学院大学北京100049 Center for Machine Vision Research Department of Computer Science and EngineeringUniversity of Oulu

针对盲人避障系统的实时性和鲁棒性要求,提出了基于单帧RGB-D图像的场景自适应分割和障碍物检测方法.针对室内场景存在较多的平面结构的特点,首先利用深度信息进行由粗到细的两阶段平面快速提取;然后提出了基于图论的RGB-D场景自适应分... 详细信息

针对盲人避障系统的实时性和鲁棒性要求,提出了基于单帧RGB-D图像的场景自适应分割和障碍物检测方法.针对室内场景存在较多的平面结构的特点,首先利用深度信息进行由粗到细的两阶段平面快速提取;然后提出了基于图论的RGB-D场景自适应分割算法,将基于深度信息的平面分割结果和基于RGB的图像分割进行自适应的权重融合;最后提出了多层次的障碍物检测识别策略,将场景区域识别为地面、桌面、墙面以及其他障碍物,并给出了场景分析结果的简化模型.实验结果表明,文中方法结合深度数据的优势以及场景的几何结构特点,有效地提高了场景分割和障碍物检测的实时性和鲁棒性,能够满足盲人室内避障应用需求.

关键词：深度传感器平面提取场景分割障碍物检测盲人避障

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An open-source, MATLAB based annotation tool for virtual slides

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Diagnostic Pathology 2013年第1期8卷 1-4页

作者： Riku Turkki Margarita Walliander Ville Ojansivu Nina Linder Mikael Lundin Johan Lundin Institute for Molecular Medicine Finland (FIMM) University of Helsinki P.O. Box 20 FI-00014 Helsinki Finland Center for Machine Vision Research Department of Computer Science and Engineering University of Oulu P.O Box 4500 FI-90014 Finland Division of Global Health Department of Public Sciences Karolinska Institutet Stockholm Sweden

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