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

作者： Yakang Dai Jie Tian Jian Xue Jiangang Liu Medical Image Processing Group Key Laboratory of Complex Systems and Intelligence Science Institute of Automation Chinese Academy of Sciences Beijing China

Conventional freehand 3D ultrasound imaging techniques separate the scanning and reconstruction steps from the visualization step. Several approaches that enable real-time volume reconstruction and visualization during acquisition have been described in the literature in recent years. However, these approaches are not fully interactive, since they do not provide sufficient feedbacks on volume reconstruction. In this paper, we present a qualitative and quantitative interaction technique (QAQIT) which can provide accurate feedbacks on volume reconstruction in real time during acquisition. Two threads are designed for data acquisition: one is to acquire B-scan images from the ultrasound scanner sequentially, and the other is to obtain positions and orientations from the position sensor continuously. A matching thread matches the latest acquired image with its relative position and orientation (PAO), and then reconstructs the image into a predefined volume. After the 3D reconstruction of each image, we calculate the reconstructed ratio (RR) and increased ratio (IR) of the reconstruction volume (RV), then update the display of the RR, and drive the volume rendering of the RV according to the IR. A freehand system based on the QAQIT has been developed on a personal computer (PC). We demonstrate our system on an embryo phantom

关键词： Ultrasonic imaging image reconstruction Visualization Feedback Yarn Data acquisition image sensors Three dimensional displays Computer displays Drives

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Multi-Modal Human Verification Using Face and Speech

Multi-Modal Human Verification Using Face and Speech

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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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APPROXIMATION MULTIDIMENSION FUCTION WITH FUNCTIONAL NETWORK

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Journal of Electronics(China) 2006年第1期23卷 81-84页

作者： Li Weibin Liu Fang Jiao Licheng Zhang Shuling Li Zongling Institute of Graphics and Image Processing Xianyang Normal University Xianyang 712000 China National Key Laboratory for Radar Signal Processing Xidian University Xi＇an 710071 China Insititute of Intelligent Information Processing Xidian University Xi＇an 710071 China

The functional network was introduced by ***, which extended the neural network. Not only can it solve the problems solved, but also it can formulate the ones that cannot be solved by traditional network. This paper applies functional network to approximate the multidimension function under the ridgelet theory. The method performs more stable and faster than the traditional neural network. The numerical examples demonstrate the performance.

关键词： Functional network Ridgelet Approximation

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EEG Signal Analysis Based on Time-variant Coupled Network Lattice Model

EEG Signal Analysis Based on Time-variant Coupled Network La...

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International Conference on Biomedical and Pharmaceutical Engineering, ICBPE

作者： Minfen Shen Guoliang Chang Shuwang Wang Patch J. Beadle Key Laboratory of Digital Signal and Image Processing of Guangdong Shantou University Shantou China School of System Engineering Portsmouth University Portsmouth UK

Brain electrical activity is widely accepted as the typical non-stationary signal. In addition, there exist more evidences that both EEG and ERP signals are chaotic signal produced by the nonlinear dynamics system. To investigate the time-varying nonlinear dynamics of the ERP under specified cognitive tasks, a novel model based on the time-variant coupled map lattice system is proposed for this purpose. The time-variant largest Lyapunov exponent (LLE) is also defined as the quantitative parameters to reveal the global characters of system and discover new information. Several simulations and real ERP signals under different fixed location cue were examined in terms of LLE spectra for studying the signal's dynamic complexity. The experimental results show that the brain chaos changes with time under different attention tasks of the information processing. The influence of the LLE with the scopes size of cues mainly occurs in P2 period. No simple linearly relationship between the scopes of cues and the influence of complexity is found.

关键词： Electroencephalography Signal analysis Lattices Brain modeling Enterprise resource planning Chaos Nonlinear dynamical systems Time varying systems Couplings Information processing

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An Analysis of Visual Speech Information Applied to Voice Activity Detection

An Analysis of Visual Speech Information Applied to Voice Ac...

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International Conference on Acoustics, Speech, and Signal processing (ICASSP)

作者： D. Sodoyer B. Rivet L. Girin J.-L. Schwartz C. Jutten Speech Communication Institute INPG U3 UMR 5009 CNRS Grenoble France Image and Signal Processing Laboratory UMR 5083 CNRS Grenoble France

We present a new approach to the voice activity detection (VAD) problem for speech signals embedded in non-stationary noise. The method is based on automatic lipreading: the objective is to detect voice activity or non-activity by exploiting the coherence between the speech acoustic signal and the speaker's lip movements. From a comprehensive analysis of lip shape parameters during speech and non-speech events, we show that a single appropriate visual parameter, defined to characterize the lip movements, can be used for the detection of sections of voice activity or more precisely, for the detection of silence sections. Detection scores obtained on spontaneous speech confirm the efficiency of the visual voice activity detector (VVAD)

关键词： Speech analysis Information analysis Acoustic signal detection Speech enhancement Speech processing Signal processing Acoustic noise Event detection Detectors Background noise

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image Denoising Algorithm Based on Neighbouring Thresholding Classification in Wavelet Domain

Image Denoising Algorithm Based on Neighbouring Thresholding...

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2006 8th International Conference on Signal processing

作者： Jianhua Hou Chengyi Xiong College of Electronic Information Engineering South-Central University for Nationalities Laboratory for State Key Image Processing & Intelligence Control Huazhong University of Science & Technology

This paper proposes a new image denoising method which exploits spatial correlation among image wavelet coefficients and classification *** extending the neighbouring threshold of wavelet coefficients for 1D signal to 2D image case,each coefficient in a subband is classified as "large" or "small" category,according to its corresponding neighbouring *** strategies are implemented to the classified *** results show that although very simple,the performance of the proposed method can be competitive to the two excellent state of the art denoising algorithms with spatial adaptivity.

关键词： Gaussian image image Denoising Algorithm Based on Neighbouring Thresholding Classification in Wavelet Domain Wavelet

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Detection of small target in infrared image sequences using attention mechanism

Detection of small target in infrared image sequences using ...

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Systems and Control in Aerospace and Astronautics, 2006. ISSCAA 2006. 1st International Symposium on

作者： Xun Yao Dehua Li Xianbin Sun Institute of pattern recognition and artificial intelligence Key Laboratory of Education Ministry for Image processing & Intelligence Control Huazhong University of Science and Technology Wuhan China

ISBN: (纸本)0780393953

The requirement of algorithm for detecting small target in infrared image sequences is not only a high rate to identify the true target and a low false alarm rate, but also the computation efficiency. By employing the selective attention mechanism, biological visual system can interpret a complex incoming image in real time with limited hardware resources. Much evidence has suggested that the biological visual system processes information in a serial strategy which rapidly selects a small relevant region in scene for further complex and time consuming analysis. In this paper, a two component computation scheme is proposed to detect dim targets in infrared image sequences. In the first process stage, an efficient method is applied to extract the potential targets which are further identified in second phase, the true targets are detected, and the spurious objects are rejected. The attention-based approach reduces the computation complexity, while the other performance aspects are not traded off. Experimental results indicate that the proposed extraction and recognition modules exhibit excellent performance of detecting small target in infrared image sequences, especially in process speed aspect.

关键词： feature extraction image sequences infrared imaging biological visual system computation complexity computation efficiency false alarm rate feature extraction image recognition modules infrared image sequences selective attention mechanism small target d

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Dim Small Targets Detection Based on Dualband Infrared image Fusion

Dim Small Targets Detection Based on Dualband Infrared Image...

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IEEE International Conference on Industrial Technology (ICIT)

作者： Yu-Qiu Sun Jin-Wen Tian Jian Liu State Education Commission Key Laboratory for Image Processing and Intelligent Control Institute for Pattern Recongnition and Artificial Intelliegence Huazhong University of Science and Technology Wuhan China School of Information and Mathematics Yangtze University Jingzhou China State Education Commission Key Laboratory for Image Processing and Intelligent Control Department of Electronic Information and Enginery Huazhong University of Science and Technology Wuhan China

To infrared images, the contrast of target and background is low, dim small targets have no concrete shapes and their textures cannot be reliable predicted. The paper puts forward a novel algorithm to fuse mid-wave and long-wave infrared images and detect targets. Firstly, the source images are decomposed by wavelet transformation. In usual, targets in infrared images are man-made, and their fractal dimension is different comparing with natural background. In wavelet transformation domain high-frequency part, we calculate local fractal dimension and set up fusion rule to merge corresponding sub-images of two matching source images. In low-frequency, we extract local maximum gray level to fuse them. Then reconstruct image by wavelet inverse transformation and obtain fused result image. In fusion results, the contrast between targets and background has obvious changes. And targets can be detected using contrast thresholding. The experimental results show that the method using fractal dimension to fuse dualband infrared images, and then detect targets is superior to use mid-wave or long -wave infrared images detect targets alone.

关键词： Object detection Dual band Infrared imaging Infrared detectors image fusion Fuses Fractals Concrete Shape Wavelet domain

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Super performance bandgap voltage reference with adjustable output for DC-DC converter

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Journal of China Universities of Posts and Telecommunications 2006年第1期13卷 75-78页

作者： Yu, Hua Zou, Xue-Cheng Chen, Chao-Yang Department of Electronics Science and Technology Huazhong University of Science and Technology Wuhan 430074 China State Key Laboratory for Image Processing and Intelligence Control Huazhong University of Science and Technology Wuhan 430074 China

This paper presents a super performance bandgap voltage reference for DC-DC converter with adjustable output. It generates a wide range of voltage reference ranging from sub-1 V to 1.2217 V and has a low temperature coefficient of 2.3 × 10-5/K over the temperature variation using the current feedback and resistive subdivision. In addition, the power supply rejection ration of the proposed bandgap voltage reference is 78 dB. When supply voltage varies from 2.5 V to 6 V, output VREF is 1.221685 ± 0.055 mV.

关键词： Electric converters

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Contrast-modulated Nonlinear Diffusion for X-ray Angiogram images

Contrast-modulated Nonlinear Diffusion for X-ray Angiogram I...

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

作者： Baopu Li Nong Sang Zhiguo Cao Department of Electronic Engineering Chinese University of Hong Kong Sha Tin Hong Kong China Key Laboratory of Ministry of Education for Image Processing and Intelligent Control Wuhan China

In this paper, we present a new method for X-ray angiogram images enhancement using a contrast-modulated nonlinear diffusion. The original nonlinear diffusion is gradient driven, which leads into much dependence on the accurate estimation of the edge. However, it is very difficult to get the accurate estimation of edges for X-ray angiogram images, which are characterized with complex background. So it is necessary to do some improvements to this model. By designing a new concept of contrast space according to the characteristics of these images, we change the original nonlinear diffusion into contrast-modulated nonlinear diffusion. Compared with the traditional method, this new approach is proved to have a better performance of enhancement

关键词： X-ray imaging Anisotropic magnetoresistance image processing image analysis Control engineering education Intelligent control image enhancement Biomedical imaging Medical diagnostic imaging Chemicals

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