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IEEE International Conference on Computer Vision systems (ICVS)

作者： Changhan Park Joonki Paik Taewoong Choi Soonhyob Kim Youngouk Kim J. Namkung Image Processing and Intelligent Systems Laboratory Department of Image Engineering Graduate School of Advanced Imaging Science Multimedia and Film Chung-Ang University Seoul South Korea Multimedia Signal Processing Laboratory Kwangwoon University Seoul South Korea Precision Machinery Center Korea Electronics and Technology Institute Puchon Gyeonggi South Korea Intelligent Image Communication Laboratory Department of Computer Engineering Kwangwoon University Seoul South Korea

In this paper, we propose a personal verification method using both face and speech to improve the rate of single biometric verification. False acceptance rate (FAR) and false rejection rate (FRR) have been a fundamental bottleneck of real-time personal verification. The proposed multimodal biometric method is to improve both verification rate and reliability in real-time by overcoming technical limitations of single biometric verification methods. The proposed method uses principal component analysis (PCA) for face recognition and hidden markov model (HMM) for speech recognition. It also uses fuzzy logic for the final decision of personal verification. Based on experimental results, the proposed system can reduce FAR down to 0.0001%, which provides that the proposed method overcomes the limitation of single biometric system and provides stable personal verification in real-time.

关键词： Humans Speech Face detection Face recognition Biometrics Principal component analysis Feature extraction Hidden Markov models Image edge detection Laboratories

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Journal of Information science and Engineering: Editorial notice

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Journal of Information science and Engineering 2005年第5期21卷 i-ii页

作者： Lee, Der-Tsai Amato, Nancy M. Chang, Shih-Fu Chen, Homer H. Chen, Tsuhan Hsu, Tsan-Sheng Hwang, Jenq-Neng Kuo, Sy-Yen Kuo, Tei-Wei Li, Chung-Sheng Tokuyama, Takeshi Wing, Jeannette Wu, Tzong-Chen Yen, John Computer Science at Texas A and M University United States Parasol Laboratory United States IEEE Transactions on Parallel and Distributed Systems Computing Research Association's Committee on the Status of Women in Computing Research CRA-W's Distributed Mentor Program United States Department of Electrical Engineering Columbia University United States Lab. United States College of Electrical Engineering and Computer Science National Taiwan University Taiwan IEEE Transactions on Circuits and Systems for Video Technology IEEE Department of Electrical and Computer Engineering Carnegie Mellon University Pittsburgh PA United States ACM ACM SIGACT IEEE Computer Society IICM Austria Research and Development of the Department Multimedia Signal Processing Technical Committee IEEE Signal Processing Society United States IEEE Transactions on Circuits and systems for Video Technology College of Electrical Engineering and Computer Science National Taiwan Ocean University Keelung Taiwan Department of Electrical Engineering National Taiwan University Taiwan Department of Computer Science and Information Engineering National Taiwan University Taipei Taiwan IEEE Technical Committee on Real-Time Systems Computer Science Division IBM T.J. Watson Research Center United States IBM Research Division Graduate School of Information Sciences Tohoku University Japan ACM IPSJ Mathematical Society of Japan Japan Department of Computer Science Computer Science Department Carnegie Mellon University United States National Academies of Science's Computer Science and Telecommunications Board United States Microsoft's Trustworthy Academic Advisory Board Intel Research Pittsburgh's Advisory Board United States Dartmouth's Institute for Security Technology Studies Advisory Committee Canada Sloan Research Fellowships Program Committee United States ACM Taiwan China Information Sciences and Technology Pennsylvania State University United States Laboratory for Intelligent Agents Penn State's School of Info

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Detection-estimation of distributed Gaussian sources

Detection-estimation of distributed Gaussian sources

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Sensor Array and Multichannel signal processing Workshop

作者： Y.L. Abramovich N.K. Spencer A.Y. Gorokhov Intelligence Surveillance and Reconnaissance Division Defence Science and Technology Organisation (DSTO) Edinburgh S.A Australia SPRI Building Cooperative Research Centre for Sensor Signal and Information Processing (CSSIP) Mawson Lakes S.A Australia Ubiquitous Communication Systems Group Philips Research Laboratory Eindhoven AA The Netherlands

The problem of estimating the number of independent homogeneously distributed (spread) Gaussian sources and their parameters impinging upon a uniform linear antenna array is considered for scenarios with a priori known angular spreading. This new method adopts the generalised likelihood ratio test (GLRT) methodology, based on LR maximisation, supported by a statistical nonasymptotic scenario-free lower bound analysis. The introduced LR maximisation yields results that statistically exceed the LR generated by the exact covariance matrix. High LR optimisation efficiency drives very high detection-estimation performance that, nevertheless, breaks down under certain threshold conditions. It is demonstrated that this breakdown phenomenon is not curable within the ML paradigm, since these highly incorrect solutions are still "better" than the actual covariance matrix as measured by the LR.

关键词： Covariance matrix Testing Sensor arrays intelligent sensors Australia Linear antenna arrays Symmetric matrices intelligent structures Surveillance Reconnaissance

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A comparison of part of speech taggers in the task of changing to a new domain

A comparison of part of speech taggers in the task of changi...

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International Conference on Information Intelligence and systems

作者： L. Boggess J.S. Hamaker R. Duncan L. Klimek Yufeng Wu Yu Zeng KUDZU Laboratory for Applied Intelligent Systems Research Department of Computer Science Mississippi State University USA ISIP (Institute for Signal and Information Processing) College of Engineering Mississippi State University USA Laboratory for Applied Intelligent Systems Research Department of Computer Science Mississippi State University USA

Part-of-speech tagging in real-world applications is performed on text in domains which are different from the publicly available large training data sets. The two most successful part-of-speech taggers are trained on the Wall Street Journal corpus, a corpus of millions of words. We compare their performance on a test set from a different domain-astronomy-from documents that are available on the World Wide Web. The Maximum Entropy Part of Speech Tagger (MXPOST) and the Transformation-Based Learning Tagger are well-known and widely used in language research and development systems. The two taggers were tested in several modes: (1) after training on the Wall Street Journal corpus only, (2) after training on only a small body of text from our astronomy domain, (3) with and without an auxiliary lexicon derived from many astronomy-related Web documents, and (4) after incremental training-that is, having been trained on the Wall Street Journal, with additional training from the specific domain. One conclusion from the experiment is that different taggers exhibit different biases when trained on the same data.

关键词： Speech Tagging Laboratories Testing Natural languages Electrical capacitance tomography Entropy Natural language processing Data mining research and development

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Neural Circuit For Matchhnismatch, Familiarity/novelty And Synchronization Detection In Saart Neural Networks

Neural Circuit For Matchhnismatch, Familiarity/novelty And S...

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International Symposium on signal processing and Its Applications (ISSPA)

作者： P. Lozo Weapons Systems Division Aeronautical and Maritime Research Laboratory Defence Science and Technology Organisation Salisbury SA Australia Department of Electrical and Electronic Engineering University of Adelaide Adelaide SA Australia Cooperative Research Centre for Sensor Signal and Information Processing SA Australia

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Neural Circuit For Self-regulated Attentional Learning In Selective Attention Adaptive Resonance Theory (saart) Neural Networks

Neural Circuit For Self-regulated Attentional Learning In Se...

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International Symposium on signal processing and Its Applications (ISSPA)

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Mathematical basis for the design of an artificial retina: visual prosthesis for the retinally blind

Mathematical basis for the design of an artificial retina: v...

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IEEE International Conference on systems, Man and Cybernetics

作者： S. Hungenahally Signal Processing and Intelligent Systems Research Laboratory Science and Technology Griffith University Australia

Visual prosthesis for the retinally blind is an area of research which is seeing some advances and progress. Advances in the areas of image processing, neuro-morphology of vision, microelectronic engineering and high speed computing has contributed to significant progress in this area. research using specific patterns of electrical stimulation may enable physicians to restore sight to the blind. Other disciplines provide examples of how electrical stimulation can solve other problems for example the cochlear implants stimulate spinal ganglion cells of the auditory nerves. On the one hand issues of signal and image processing in the biological system are not yet fully understood while on the other hand there is the implant problem which needs to be addressed. In this paper an attempt has been made to point out the various difficulties, unsolved problems and propose solutions to some of them. A prototype of the artificial retina is described. Work under progress in the "signal processing and intelligent systems research laboratory, Faculty of science and technology, Griffith University" is described.

关键词： Retina Image processing Electrical stimulation Visual prosthesis Microelectronics Computer vision Image restoration signal restoration Cochlear implants signal processing

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Fractional discriminant functions: emulation of real-index-order receptive fields for vision systems

Fractional discriminant functions: emulation of real-index-o...

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IEEE International Conference on systems, Man and Cybernetics

作者： S.K. Hungenahally Signal Processing and Intelligent Systems Research Laboratory Science and Technology Griffith University Brisbane QLD Australia

Extraction of transitions and edge information from signals and visual images in the presence of noise is an important class of problems in image processing. Biological visual systems posses some robust neuro-morphological functions for information abstraction. The complex architecture of the neural elements from the retina to the visual cortex are organized into receptive fields. Discriminant functions are further extended into a family of fractional discriminant functions based on the notion of fractional D operators. This paper presents the theory of fractional discriminant functions /spl Theta//sup /spl nu//[/spl xi/] which attempt to emulate the biological receptive field functionality. It employs /spl Theta/[/spl xi/] functions for the enhancement of various attributes of images which require human intervention.

关键词： Emulation Machine vision Data mining Biomedical imaging signal processing Humans Medical diagnostic imaging Remote sensing Space exploration Time domain analysis

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Neural basis for the design of fractional discriminant functions

Neural basis for the design of fractional discriminant funct...

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International Conference on Neural Networks

作者： S. Hungenahally Signal Processing and Intelligent Systems Research Laboratory Faculty of Science and Technology Griffith University Nathan QLD Australia

Fractional discrimination is a process by which an aggregated continuous differentiation of any signal can be carried out. In this paper the author presents the basis for the design of fractional discriminant functions for image processing based on the visual receptive fields. It is hypothesised in this paper that the biological visual system employs some orders of fractional discrimination to carry out visual perception. It is shown that perceptual information of good quality is extracted despite poor PSNR and NMSE in images. It is the author's observation that merely improving the SNR and the statistical measures of an image does not necessarily improve the perceptual quality of the image on the contrary the author has shown by employing fractional discriminant functions that perceptual quality is better in spite of poor statistical measurements.

关键词： signal processing Image processing Visual system Visual perception Data mining PSNR

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Virtual cockpit: fuzzy neural networks in visual perception

Virtual cockpit: fuzzy neural networks in visual perception

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International Conference on Neural Networks

作者： S.K. Hungenahally Signal Processing and Intelligent Systems Research Laboratory Faculty of Science and Technology Griffith University Nathan Campus QLD Australia

research on fuzzy neurons involves the development of computational modules which recognise the physical data in a system and perform logical operations for decision making. The outputs of fuzzy neurons can be connected into a network by employing inference mechanisms to arrive at a judgement. Such networks are very useful in real world systems where a human operator is present in the feedback loop. The complex system of an aircraft cockpit is an ideal example for the application of such a technology. The pilot is an active part of the feedback element of the system. In this paper a formal definition of fuzzy neurons is given and a simple fuzzy neural system is implemented in the design of the AVID panel. AVID is a visual display panel employing fuzzy logic to display the flight instrumentation data and integrated into a processing network for the purposes of real time operations.

关键词： Fuzzy neural networks Visual perception Fuzzy systems Neurons Fuzzy logic Displays Physics computing Decision making Inference mechanisms Humans

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