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SICE Annual Conference

作者： Makoto Shimojo Takuma Arakil Masahiro Teranishi Aigou Ming Masatoshi Ishikawa Mechanical Engineering and Intelligent Systems The University of Electro-Communications Chofu-shi JAPAN Denki Tsushin Daigaku Chofu Tokyo JP Department of Information Physicsand Computing Graduate School of Information Science and Technology University of Tokyo Bunkyo Tokyo Japan Department of Information Physics and Computing The University of Tokyo Bunkyo-ku Tokyo Japan

ISBN: (纸本)9784907764296

A tactile sensor is developed with the aim of covering a robotpsilas entire structure, while reducing wiring requirement and ensuring high-speed response. There are only 4 signal wires from the sensor. The sensor response time is nearly constant (within 1 ms) regardless of the number of detection elements, their placements or sensor areas. In this paper, the principles behind the operation of this sensor and the results of experiments using the sensor are described.

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Identifying Local Ultrametricity of EEG Time Series for Feature Extraction in a Brain-Computer Interface

Identifying Local Ultrametricity of EEG Time Series for Feat...

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Annual International Conference of the IEEE engineering in Medicine and Biology Society (EMBC)

作者： Damien Coyle Thomas M. McGinnity Girijesh Prasad Intelligent Systems Research Center School of Computing and Intelligent Systems Faculty of Engineering University of Ulster Londonderr UK

The accurate discrimination of EEG times-series is a challenging problem and has become a topic of prominent research interest, given the extent of the research activity in the area of brain-computer interface (BCI) technology. Many signal processing algorithms involving preprocessing, feature extraction/selection, and classification have been deployed and yet, the most appropriate and robust solutions are still being sought. This paper presents an analysis of a new methodology for feature extraction in a BCI which is based on identifying the extent of ultrametricity from EEG time-series. This work is inspired by the idea that there are natural, not necessarily unique, tree or hierarchy structures defined by the ultrametric topology of EEG time-series. The objective is to determine if coefficients which reflect the extent of ultrametricity can be used as distinct features of different EEG time series, recorded whilst subjects imagine left/right hand movements (motor imagery(MI)). The results show that MI based EEG time-series can be separated using a local ultrametricity quantifier and a linear discriminant classifier or Bayes classifier. Also, it is shown that neural-time-series-prediction-preprocessing (NTSPP) produces a higher dimensional space in which local ultrametricity is more separable for two classes of EEG signals.

关键词： Electroencephalography Feature extraction Brain computer interfaces Extraterrestrial measurements Time series analysis Robustness Signal processing Speech Handicapped aids Communication standards

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Characterising Range Image Features via Gradient Operators

Characterising Range Image Features via Gradient Operators

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International Conference on Image Analysis and Processing

作者： Sonya Coleman Shanmugalingam Suganthan Bryan Scotney School of Computing and Intelligent Systems University of Ulster Magee UK School of Computing and Information Engineering University of Ulster Coleraine UK

Real-time extraction of features from range images can play an important role in computer vision tasks such as localisation and navigation. Developing operators that can characterise features in a range image, such as jump, crease, or smooth edges, is challenging, due to both the irregular spatial distribution of range image data and the nature of the features themselves. We present adaptive gradient operators that can automatically change shape to accommodate irregular data distribution; through appropriate analysis of the output responses, we show that the operators can also be specialised to characterise particular types of range image features.

关键词： Image edge detection Computer vision Image segmentation Data mining Feature extraction Layout Object recognition Image sensors intelligent systems Real time systems

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Solution to the Generalized Champagne Problem on simultaneous stabilization of linear systems

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Science in China(Series F) 2007年第5期50卷 719-731页

作者： GUAN Qiang WANG Long XIA BiCan YANG Lu YU WenSheng ZENG ZhenBing Laboratory of Complex Systems and Intelligence Science Institute of Automation Chinese Academy of Sciences Beijing 100080 China Center for Systems and Control College of Engineering Peking University Beijing 100871 China LMAM and School of Mathematical Sciences Peking University Beijing 100871 China Shanghai Institute of Theoretical Computing Software Engineering Institute East China Normal University Shanghai 200062 China National Key Laboratory of Intelligent Technology and Systems Tsinghua University Beijing 100084 China

The well-known Generalized Champagne Problem on simultaneous stabilization of linear systems is solved by using complex analysis and Blonders technique. We give a complete answer to the open problem proposed by Patel et al., which automatically includes the solution to the original Champagne Problem. Based on the recent development in automated inequality-type theorem proving, a new stabilizing controller design method is established. Our numerical examples significantly improve the relevant results in the literature.

关键词： linear systems stabilization simultaneous stabilization Champagne Problem Generalized Champagne Problem,complex analysis inequality-type theorem automated theorem proving

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Feature Extraction on Range Images - A New Approach

Feature Extraction on Range Images - A New Approach

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IEEE International Conference on Robotics and Automation (ICRA)

作者： S.A. Coleman B.W. Scotney S. Suganthan School of Computing and Intelligent Systems University of Ulster Londonderry UK School of Computing and Information Engineering University of Ulster Coleraine UK

Range images can provide an almost 3-dimensional description of a scene. Feature driven segmentation of range images has been primarily used for 3D object recognition, and hence the accuracy of the detected features is a prominent issue. Feature extraction on range images has proven to be a more complex problem than on intensity images due to both the irregular distribution of range image data and the nature of the features that are present in range images. Approaches to range image feature extraction are often scan line based approximations that carry a significant computational overhead and hence are not appropriate for real-time processing. This paper presents a design procedure for scalable first order derivative operators that can be used directly on irregularly distributed data. Hence the method is appropriate for direct use on range image data without the requirement of image preprocessing and could form the basis of algorithms of real-time robotic applications.

关键词： Feature extraction Image edge detection Image segmentation Layout Computer vision Object recognition Application software intelligent systems Surface fitting Robotics and automation

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Laplacian Operators for Direct Processing of Range Data

Laplacian Operators for Direct Processing of Range Data

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IEEE International Conference on Image Processing

The use of range data has become prominent in the field of computer vision. Due to the irregular nature of range data that occurs with a number of sensors, feature extraction is a complex and challenging problem. Feature extraction techniques for range images are often based on scan line data approximations and hence do not employ exact data locations. We present a finite element based approach to the development of Laplacian operators that can be applied to both regularly or irregularly distributed range data. We demonstrate that the feature maps generated using our approach on range data are much less susceptible to noise than the traditional use of Laplacian operators on intensity images.

关键词： Laplace equations Shape Layout Distributed computing Image representation Finite element methods Computer vision Feature extraction Image edge detection Pixel

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Concurrent Edge and Corner Detection

Concurrent Edge and Corner Detection

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IEEE International Conference on Image Processing

作者： Sonya Coleman Dermot Kerr Bryan Scotney School of Computing and Intelligent Systems University of Ulster Magee UK School of Computing and Information Engineering University of Ulster Coleraine UK

To enable fast reliable feature matching or tracking in scenes, features need to be discrete and meaningful, and hence corner detection is often used for this purpose. However, to obtain a higher level description of an image, such as identification of objects, additional information such as edges is required, and more recently detectors have been proposed that find both edges and corners. We present a combined operator, enabling edge and corner detection to be achieved concurrently. We demonstrate that accuracy is comparable to well-known existing corner detectors and edge detectors, and, as standard post-smoothing of the corner map is not required, significantly reduced computation time can be achieved.

关键词： Image edge detection Detectors Concurrent computing intelligent systems Product design Layout Object detection Robots Image segmentation Brightness

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Range Image Feature Extraction with Varying Degrees of Data Irregularity

Range Image Feature Extraction with Varying Degrees of Data ...

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International Machine Vision and Image Processing Conference (IMVIP)

作者： Shanmugalingam Suganthan Sonya Coleman Bryan Scotney School of Computing and Intelligent Systems University of Ulster Magee UK School of Computing and Information Engineering University of Ulster Coleraine UK

The use of range images has become prominent in the field of computer vision. Due to the irregular nature of range image data that occurs with a number of sensors, edge detection techniques for range images are often based on scan line data approximations and hence do not employ exact data locations. We present a finite element based approach to the development of gradient operators that can be applied to both regularly and irregularly distributed range images. We have created synthetic irregularly distributed range images for each edge type, and the gradient operators developed are evaluated with respect to their performance in edge detection across varying levels of data irregularity.

关键词： Feature extraction Image edge detection Computer vision Shape Finite element methods Data mining Pixel Layout Machine vision Image processing

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Near-Circular Corner and Edge Detection Operators

Near-Circular Corner and Edge Detection Operators

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International Machine Vision and Image Processing Conference (IMVIP)

作者： Dermot Kerr Sonya Coleman Bryan Scotney School of Computing and Intelligent Systems University of Ulster Magee UK School of Computing and Information Engineering University of Ulster Coleraine UK

To enable fast reliable feature matching or tracking in scenes, features need to be discrete and meaningful, and hence corner detection is often used for this purpose. However, to obtain a higher level description of an image, such as identification of objects, additional information such as edges is required, and more recently detectors have been proposed that find both edges and corners. Recently, finite-element based methods have been used to develop gradient operators for edge detection that have improved angular accuracy over standard techniques. We extend this work to corner detection, enabling edge and corner detection to be integrated. In addition we present a combined operator, enabling edge and corner detection to be achieved concurrently, and we demonstrate that accuracy is comparable to well- known existing corner detectors and edge detectors, and, as standard post-smoothing of the corner map is not required, significantly reduced computation time can be achieved.

关键词： Image edge detection Detectors Computer vision Machine vision Image processing intelligent systems Machine intelligence Reliability engineering Layout Object detection

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Integrated Edge and Corner Detection

Integrated Edge and Corner Detection

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International Conference on Image Analysis and Processing

作者： Sonya Coleman Bryan Scotney Dermot Kerr School of Computing and Intelligent Systems University of Ulster Magee UK School of Computing and Information Engineering University of Ulster Coleraine UK

Corner detection is used in many computer vision applications that require fast and efficient feature matching. For tasks such as robot localisation and navigation, the use of corners for matching is preferred over edges or other, larger, features. In recent years finite-element based methods have been used to develop gradient operators for edge detection that have improved angular accuracy over standard techniques. We extend this work to corner detection, enabling edge and corner detection to be integrated. We demonstrate that accuracy is comparable to well-known existing corner detectors, and that significantly reduced computation time can be achieved, making the approach appropriate for real-time computer vision and robotics.

关键词： Image edge detection Detectors Computer vision Layout Application software Navigation Robot vision systems intelligent systems intelligent structures Anisotropic magnetoresistance

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