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An Embodied Interaction Robots system Based on Speech

Journal of Robotics and Mechatronics 2000年第2期12卷 126-134页

作者： Watanabe, Tomio Okubo, Masashi Ogawa, Hiroki Department of System Engineering Faculty of Computer Science and System Engineering Okayama Prefectural University 111 Kuboki Soja Okayama719-1197 Japan

The concept of an embodied interaction robots system based on speech is proposed for assisting human interaction by generating the motions of robots coherently related to speech. For an essential human interaction model which estimates a listener’s nodding on the basis of a speaker’s burst-pause of speech, the validity of a moving-average (MA) model is demonstrated in the robustness of prediction. Then, the prototype of the proposed system which consists of 2 interaction robots with both functions of speaker and listener is developed in which two models are incorporated: one is a listener’s reaction model in which nodding, blinking and the motions of head, arms and body are estimated by the MA model of the burst-pause of speech to the nodding: the other is a speaker’s bodily reaction model in which the motions of head, arms and body are estimated by its own MA model of the burst-pause of speech to the head motion. By the sensory evaluation and behavioral analysis in human-robot interaction, the effectiveness of the robot’s bodily interaction is demonstrated. The effectiveness of the embodied interaction robots system is also confirmed in remote communication. © 2000, Fuji Technology Press. All rights reserved.

关键词： Sensory analysis

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Modular neural networks for face detection 17

Modular neural networks for face detection

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17th National Radio science Conference, NRSC 2000

作者： El-Bakry, H.M. Abo-Elsoud, M.A. Kanel, M.S. Faculty of Computer Science and Information System Mansoura University Egypt Electronics and Elec. Comm. Dept. Faculty of Engineering Mansoura University Egypt Systems Design Eng. Dept. University of Waterloo Canada

ISBN: (纸本)9775031648

A new concept for detection of human faces is presented. An efficient approach to reduce the computation time taken by neural networks for the searching process is introduced. We combine both Fourier and wavelet transforms with cooperative modular neural networks (MNNs) to enhance the performance of the detection process. Such an approach is applied to identify human faces automatically in cluttered scenes. Here, neural networks are used to test whether a window of 20x20 pixels contains a face or not. The major difficulty in the learning process comes from the large database required for face/nonface images. A simple design for cooperative MNNs is presented to solve this problem by dividing these data into some groups. Such division results in reduction of computational complexity and thus decreasing the time and memory needed during the test of an image. In order to have a faster detection algorithm, a combination of the FFT and the wavelet transform is made in order to reduce the elapsed time during the test phase and enhance the detection performance. Feature measurements of the input faces are made through Fourier descriptors which are insensitive to rotation, translation and scaling. Such a feature is modified to reduce the number of neurons in the hidden layer. The second stage extracts wavelet coefficients that have been shown to provide advantages in terms of better representation for a given data to be compressed. Finally, the resulting vector is fed to one of five neural networks for face detection. Compared to previous work in face detection, the use of this combination reduces the number of neurons required for neural networks. Simulation results for the proposed algorithm show good performance on detecting faces with rotation, occlusion, noise, or change in illumination. © 2000 IEEE.

关键词： Face recognition

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Integrating Fourier descriptors and PCA with neural networks for face recognition 17

Integrating Fourier descriptors and PCA with neural networks...

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17th National Radio science Conference, NRSC 2000

作者： El-Bakry, H.M. Abo-Elsoud, M.A. Kamel, M.S. Faculty of Computer Science and Information System Mansoura University Egypt Electronics and Elec. Comm. Dept. Faculty of Engineering Mansoura University Egypt Systems Design Eng. Dept. University of Waterloo Canada

ISBN: (纸本)9775031648

A new approach to the face recognition problem is presented through combining Fourier descriptors with principal component analysis (PCA) and neural networks. Here the faces are vertically oriented frontal view with scaling, orientation, expression, and illumination changes. There are many research activities on face recognition using the face space which is described by a set of eigenfaces. Each face is efficiently represented by its projection onto the space expanded by the eigenfaces and has a new descriptor. Previous work on eigenface has shown that it performs well only with changes in expression, but results are poor in the case of rotating, or scaling the input face. In order to enhance the performance of the eigenfaces technique to accommodate other variations of the input face, the Fourier vector of each face is projected in the eigenspace. Neural networks are used to recognize the face through learning the correct classification of these new descriptors. A real-time system has been created which combines the face detection and recognition techniques. A recognition rate of 91% has been achieved over real tests. It is also shown that our proposed system behaves accurately in the case of rotated or scaled faces as well as for changes in expression. © 2000 IEEE.

关键词： Image reconstruction

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Failure diagnosis method for machinery in unsteady operating condition by instantaneous power spectrum and genetic programming

Failure diagnosis method for machinery in unsteady operating...

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International Conference on Knowledge-Based Intelligent Information engineering systems (KES)

作者： Peng Chen T. Toyota M. Taniguchi Fang Feng T. Hiho Faculty of Computer Science and System Engineering Kyushu Institute of Technology Fukuoka Japan

The paper proposes a failure diagnosis method for machinery in unsteady operating condition using instantaneous power spectrum (IPS) and genetic programming (GP). The IPS is used to extract feature frequency of each machine state from measured vibration signal for distinguishing failures by the Relative Crossing Information (RCI). Excellent symptom parameters for detecting failures are automatically generated by GP. The method proposed in the paper is verified by applying it to the failure diagnosis of rolling bearing.

关键词： Machinery Genetic programming Vibration measurement Data mining Feature extraction Time measurement systems engineering and theory Frequency measurement Frequency domain analysis computer science

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InterRobot: a speech driven embodied interaction robot

InterRobot: a speech driven embodied interaction robot

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IEEE International Workshop on Robot and Human Communication (ROMAN)

作者： H. Ogawa T. Watanabe Faculty of Computer Science and System Engineering Okayama Prefectural University Soja Okayama Japan Japan Science and Technology Corporation Japan

The concept of a speech driven embodied interaction robots system is introduced for supporting human interaction by generating the expressive actions or motions of robots coherently related to speech input. Then, the prototype of the system which consists of 2 interaction robots called InterRobots with both functions of speaker and listener is developed in which two models are incorporated: one is a listener's action model in which nodding, blinking and the motions of head, arms and body are estimated by the hierarchical moving average (MA) model of the burst-pause (ON-OFF) of speech to the nodding; the other is a speaker's action model in which the motions of head, arms and body are estimated by its own MA model of the ON-OFF of speech to the head motion. By the sensory evaluation and behavioral analysis in human-robot interaction, the effectiveness of InterRobot's interaction is demonstrated. The effectiveness of the embodied interaction robots system is also confirmed in remote communication.

关键词： Human robot interaction Biological system modeling Robot sensing systems Motion estimation Face computer graphics computer science systems engineering and theory Speech analysis Computational modeling

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A speech driven embodied interaction robots system for human communication support

A speech driven embodied interaction robots system for human...

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IEEE International Conference on systems, Man and Cybernetics

作者： T. Watanabe M. Okubo H. Ogawa Faculty of Computer Science and System Engineering Okayama Prefectural University Okayama Japan CREST Japan Science and Technology Corporation Japan

The concept of a speech driven embodied interaction robots system is introduced for supporting human interaction by generating the expressive actions or motions of robots coherently related to speech input. The prototype of the system which consists of 2 interaction robots called InterRobots with both functions of speaker and listener is developed in which two models are incorporated: one is a listener's action model where nodding, blinking and the motions of head, arms and body are estimated by the hierarchical MA (moving-average) model of the burst-pause (ON-OFF) of speech to the nodding; the other is a speaker's action model in which the motions of head, arms and body are estimated by its own MA model of the ON-OFF of speech to the head motion. By sensory evaluation and behavioral analysis in human-robot interaction, the effectiveness of InterRobot's interaction is demonstrated. The effectiveness of the embodied interaction robot system is also confirmed in remote communication.

关键词： Speech Human robot interaction Biological system modeling Robot sensing systems Arm Motion estimation Prototypes computer graphics computer science systems engineering and theory

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Evaluation of an embodied virtual communication system for human interaction analysis by synthesis

Evaluation of an embodied virtual communication system for h...

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IEEE International Workshop on Robot and Human Communication (ROMAN)

作者： Y. Ishii T. Watanabe Faculty of Computer Science and System Engineering Okayama Prefectural University Soja Okayama Japan CREST Japan Science and Technology Agency Japan

An embodied virtual face-to-face communication system with two types of avatars for human interaction analysis by synthesis is developed: one is a VirtualActor (VA) as a human avatar which represents interactive behavior such as the motions of head, arms and body, the other is a VirtualWave (VW) as an abstract avatar in which human behavior is simplified as the motion of wave to clarify an essential role of interaction. The system provides networked virtual communication environment in which two remote talkers can share embodied interaction through their VA or VW including themselves in the same virtual space. The effectiveness of the system is demonstrated by the sensory evaluation and behavioral analysis of the communication experiment in 13 pairs of 26 talkers under various conditions such as the time lag of motion and voice. The importance of mutual embodied sharing in communication is also clarified The system would be expected to form the foundation of information media and communication technologies as well as the methodology for the analysis and understanding of various human interactions.

关键词： Humans Avatars Face Motion analysis Control system synthesis computer science systems engineering and theory Arm Speech analysis Information analysis

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Integrating Fourier descriptors and PCA with neural networks for face recognition

Integrating Fourier descriptors and PCA with neural networks...

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National Conference on Radio science (NSRC)

作者： H.M. El-Bakry M.A. Abo-Elsoud M.S. Kamel Faculty of Computer Science &Information System Mansoura University Egypt Systems Design Engineering Department University of Waterloo Canada

关键词： Principal component analysis Neural networks Face recognition Face detection Lighting Pattern recognition Humans Computational modeling Image reconstruction Information systems

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A review of real-time software engineering methodologies for developing a wall-climbing robot control firmware

A review of real-time software engineering methodologies for...

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IEEE Region 10 International Conference TENCON

作者： D.N.A. Jawawi S. Deris R. Mamat Faculty of Electrical Engineering Universiti Teknologi Malaysia Johor Malaysia Faculty of Computer Science and Information System Universiti Teknologi Malaysia Johor Malaysia

Designing and developing software for autonomous robot control system is a challenging task. Issues related to real-time control, embedded system and artificial intelligence are involved in the software development process. This type of software must be developed with proper software methodology or well-defined development process in order to increase the productivity and quality of the software design and software products. This paper presents a review of two real-time software development methodologies and compares their suitability for developing real-time control software for a wall-climbing robot under development at Universiti Teknologi Malaysia (UTM).

关键词： Software engineering Software quality Software design Programming Robot control Real time systems Control systems Embedded system Artificial intelligence Productivity

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Modular neural networks for face detection

Modular neural networks for face detection

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National Conference on Radio science (NSRC)

作者： H.M. El-Bakry M.A. Abo-Elsoud M.S. Kanel Faculty of Computer Science&Information System Mansoura University Egypt Electronics & Electrical Comm.Department Faculty of Engineering Mansoura University Egypt

A new concept for detection of human faces is presented. An efficient approach to reduce the computation time taken by neural networks for the searching process is introduced. We combine both Fourier and wavelet transforms with cooperative modular neural networks (MNNs) to enhance the performance of the detection process. Such an approach is applied to identify human faces automatically in cluttered scenes. Here, neural networks are used to test whether a window of 20/spl times/20 pixels contains a face or not. The major difficulty in the learning process comes from the large database required for face/nonface images. A simple design for cooperative MNNs is presented to solve this problem by dividing these data into some groups. Such division results in reduction of computational complexity and thus decreasing the time and memory needed during the test of an image. In order to have a faster detection algorithm, a combination of the FFT and the wavelet transform is made in order to reduce the elapsed time during the test phase and enhance the detection performance. Feature measurements of the input faces are made through Fourier descriptors which are insensitive to rotation, translation and scaling. Such a feature is modified to reduce the number of neurons in the hidden layer. The second stage extracts wavelet coefficients that have been shown to provide advantages in terms of better representation for a given data to be compressed. Finally, the resulting vector is fed to one of five neural networks for face detection. Compared to previous work in face detection, the use of this combination reduces the number of neurons required for neural networks. Simulation results for the proposed algorithm show good performance on detecting faces with rotation, occlusion, noise, or change in illumination.

关键词： Neural networks Face detection Testing Humans Wavelet transforms Neurons computer networks Fourier transforms Layout Image databases

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