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检索条件"任意字段=International Congress on Image and Signal Processing, BioMedical Engineering and Informatics"

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Different Bands of Sleep EEG analysis Based on the Multiscale Jenson-Shannon Divergence 10

Different Bands of Sleep EEG analysis Based on the Multiscal...

引用

10th international congress on image and signal processing, biomedical engineering and informatics (CISP-BMEI)

作者： Zhang, Zhengxia Wang, Jun Dai, Jiafei Li, Jin Hou, Fengzhen Nanjing Univ Posts & Telecommun Image Proc & Image Commun Key Lab Nanjing Jiangsu Peoples R China Nanjing Mil Command Nanjing Gen Hosp Nanjing Jiangsu Peoples R China Shaanxi Normal Univ Coll Phys & Informat Technol Xian Shaanxi Peoples R China China Pharmaceut Univ Sch Sci Nanjing Jiangsu Peoples R China

ISBN: (纸本)9781538619377

As a necessary process of life, sleep is an important part of the body rehabilitation, integration and consolidating memories, is an integral part of health. Recently, a lot of people are suffering from sleep diseases, such as insomnia, which aroused the interest of a large number of scholars to research on sleep, and this has a certain realistic guiding significance. By using nonlinear dynamics theory knowledge, The Jenson-Shannon Divergence(JSD) algorithm and the multiscale JSD algorithm are proposed to research on the alpha EEG signals and theta EEG signals which in conscious period and Non-rapid Eye Movement(NREM) sleep stage I in this paper. At the same time, we also use the SPSS statistical software to verify the results of two experiments about the EEG in alpha band and theta band in two different states, the results of two groups got a very good effect both in the accuracy and reliability. In addition, we analyzed the alpha EEG in two states with the method of error bar graph, the research results show that both the JSD algorithm and the improved multi-scale JSD algorithm can effectively distinguish the EEG signals both in awaked period and NREM sleep stage I, which is unified with the scientific basis, namely the alpha waves in eeg signals are more than the other waves when people are awaked, however, there are more theta waves in eeg signals when people are in state of NREM sleep stage I (shallow sleep phases), proved that the EEG signals in two states exist significant differences, which also tell me that the algorithm we proposed can be further used in the study of sleep EEG in installment, which can be provided to help all kinds of disease diagnosis and treatment of sleep with effective auxiliary function, the research has an important practical significance.

关键词： multi-scale JSD sleep EEG signals Alpha band Theta band

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Region-Based Euler Video Amplification Algorithm 10

Region-Based Euler Video Amplification Algorithm

引用

international congress on image and signal processing, biomedical engineering and informatics

作者： Haotian Yu Haihong Lin Encai Zhang Jichun Li Guannan Chen Normal University Fuzhou P. R. China Fujian Provincial Engineering Technology Research Center of Photoelectric Sensing Application Fujian Normal University Fuzhou Fujian China Key Laboratory of OptoElectronic Science and Technology for Medicine of Ministry of Education Fujian Provincial Key Laboratory of Photonics Technology Fujian

ISBN: (纸本)9781538619384;9781538619377

Video magnification method plays an important role in medical analysis, modern material analysis and military research. A new algorithm, Region-Based Euler Video Magnification Method which is based on Euler Video Magnification was proposed in this paper. This method performs more efficiency and higher SNR in video magnification. And numerical experiments prove that can increase efficiency and shorten the time limit compared to other video magnification methods.

关键词： Band-pass filters Histograms Optical filters Taylor series signal processing algorithms signal to noise ratio image color analysis

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Blind source separation problem algorithms for audio and biomedical signals

Blind source separation problem algorithms for audio and bio...

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Congreso Internacional de Ingenieria (CONIIN)

作者： César Javier Ortiz-Echeverri Pérez Karla Daniela B. Giovanni Galindo-Burgos Juvenal Rodríguez-Reséndiz Department of Informatics Colegio Tecnico da UFMG Universidade Federal de Minas Gerais Belo Horizonte MG Computer Science Department Centro Federal de Educacao Tecnologica de Minas Gerais Belo Horizonte MG Computer Science Department Universidade Federal de Minas Gerais Belo Horizonte MG

Blind Source Separation (BSS) is an inverse solution problem based on Second Order Statistic (SOS) and Higher Order Statistic (HOS), with significant applications in the area of acoustics, telecommunications, and biomedicine. In this work, one algorithm based on SOS called Second Order Blind Identification (SOBI), and two HOS algorithms, max-kurtosis, and fastICA was compared using mixed of known signals as sinusoidal, and audio waveforms in order to compare the performance of algorithms using one referenced metric between original and recovered signals, as well as five EEG nearby channels located in a specifical regions on the scalp. The results suggest that fastICA algorithm based on negentropy is performed better in most cases, however, in EEG signals, it is not possible to conclude the same due to the lack of ground truth, therefore, the choice of better ICA algorithm to separate EEG signals it is still an open problem in the biomedical applications.

关键词： signal processing algorithms Electroencephalography Blind source separation Random variables Entropy

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Music emotion understanding by computer based on BP neural network

Music emotion understanding by computer based on BP neural n...

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international congress on image and signal processing, biomedical engineering and informatics (CISP-BMEI)

作者： Jun Liu Min Huang College of Information Engineering Nanchang Hangkong University Nanchang China College of Music Nanchang Hangkong University Nanchang China

In order to enable the computer to recognize the emotion expressed in the music, a new method was developed in this paper. Firstly, we divided the emotion space into four parts according to Thayer model. Secondly, we extracted eight features from the music and finally a BP neural network was created to classify the music in the emotion space. The result shown the computer recognition is high coincident with the expert recognition and the reliability of the method was proved.

关键词： Emotion recognition Feature extraction Linear regression Neural networks Correlation coefficient Computational modeling Testing

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Salient region detection based on the superpixel and texture

Salient region detection based on the superpixel and texture

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international congress on image and signal processing, biomedical engineering and informatics (CISP-BMEI)

作者： Jichun Li Encai Zhang Haotian Yu Haihong Lin Guannan Chen Fujian Provincial Engineering Technology Research Center of Photoelectric Sensing Application Fujian Normal University Fuzhou Fujian China

ISBN: (纸本)9781538619384;9781538619377

Salient region detection has become one of the hot research topics in pattern recognition in recent years. In this paper, an effective SLIC superpixel-based and global texture contrast salient region detection algorithm is proposed. SLIC algorithm is used to extract the initial superpixels in the image preprocessing stage, and then combined with the textural features of each superpixel to calculate global contrast differences and spatial coherence. Finally, the key areas are enhanced and detected. Experimental results in the public database have been shown that the method performs better than the others in precision, recall and F-measure.

关键词： image color analysis Saliency detection Visualization image segmentation Pattern recognition Computer vision Conferences

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Fast Skewed Object Matching With Adaptive Rotation Step Based on Affine Transformation

Fast Skewed Object Matching With Adaptive Rotation Step Base...

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international congress on image and signal processing, biomedical engineering and informatics

作者： Yao Xiao College of Photonic and Electronic Engineering Fujian Normal University Fuzhou China

ISBN: (纸本)9781538619384;9781538619377

Using affine transformation, object picture can be rotated to the same direction with template picture but it is required to rotate from step by step. This method is very inefficient and time-consuming. To solve this problem, an improved fast skewed object matching algorithm is proposed in this paper. At the beginning, the picture rotate several steps at a given step size to one direction and match a template picture at the same time. The final direction of rotation is determined by the first moment of the similarity. Once the direction is settled, the rotation step size is determined by similarity. If the similarity is lower than threshold then step size increases to approaching the correct angle faster. If the similarity is higher than threshold then step size decreases to achieve the purpose of precise matching the correct angle. It provides a feasible way for the skewed object matching. For example, the skewed Chinese character matching is used to demonstrate this algorithm.

关键词： signal processing algorithms Sorting Noise reduction Cameras signal processing biomedical engineering informatics

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international congress on image and signal processing, biomedical engineering and informatics (CISP-BMEI)

作者： Yi Li Xiaodong Liu Technology of Computer and Information Engineering Heilongjiang University of Science Harbin P. R. China Research Center of Information and Control Dalian University of Technology Dalian P. R. China

In this paper, a maximum similarity criterion is proposed which is adapted to a new fuzzy face recognition method (namely, 2DFMS). The similarity degree between faces is defined by a nonlinear function. Based on this similarity, an improvement fuzzy membership function is obtained by applying k-nearest neighbor. Then, 2DFMS extracts the features from face images directly so that it will not suffer from the SSS problem. Finally, in the projected space, the test image is identified according to a specific classifier, which is based on a maximum similarity criterion. The whole algorithm is implemented on ORL and Yale face database to demonstrate the effectiveness and robustness.

关键词： Face Face recognition Training Feature extraction Databases Classification algorithms Two dimensional displays

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A denosing method of frequency spectrum for recognition of dashboard sounds

A denosing method of frequency spectrum for recognition of d...

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international congress on image and signal processing, biomedical engineering and informatics (CISP-BMEI)

作者： Pengfei Li Qingxiang Wu Caiyun Wu Caiyou Yuan College of Photonic and Electronic Engineering Fujian Normal University Fuzhou Fujian China

ISBN: (纸本)9781538619384;9781538619377

In this article a frequency spectrum-based approach is proposed to extract features and identify warning tones from dashboard. The purpose of this paper is to identify the warning tones emitted from the dashboard, such as turn signal lights, unmanned seat belts, position lights, etc. After analysis of the spectral characteristics of sound signals, the information is extracted directly from the spectral characteristics as the basis of classification and identification. Firstly, the standard voices of different instruments are collected as templates. The test sound is compared with the template sounds when testing, and similarity is calculated. When the similarity is higher than a threshold, test sound and template sound are regarded as the same voice. Otherwise that is not qualified sound and the meter is considered to be defective. The usual way to remove noise was in the time domain. A new approach has been proposed to denoise in the spectral space and obtain the efficiently spectral characteristics of the standard sounds. Finally this method is applied to dashboard voice recognition and has achieved good results.

关键词： Standards Discrete Fourier transforms signal processing algorithms Speech recognition Speech Feature extraction Instruments

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Multimeter digital recognition based on feature coding detection

Multimeter digital recognition based on feature coding detec...

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international congress on image and signal processing, biomedical engineering and informatics (CISP-BMEI)

作者： Caiyun Wu Qingxiang Wu Caiyou Yuan Pengfei Li Yanan Zhang Yao Xiao Key Laboratory of OptoElectronic Science and Technology for Medicine of Ministry of Education Fujian Normal University Fujian Fuzhou China Fujian Provincial Key Laboratory for Photonics Technology Fujian Normal University Fujian Fuzhou China

ISBN: (纸本)9781538619384;9781538619377

In many industrial applications, digits on instrument display need to be automatically identified. In this paper, we propose a new feature extraction method based on non-luminous digital tubes for recognition of digits on instrument display. The advantage of this method is that it is required fewer digital tubes to represent a digit on multimeters. And based on feature coding detection, a corresponding recognition algorithm is proposed to recognize digits on multimeters. The algorithm can identify digits well only using a few steps to detect features so that the algorithm is very efficient. The experimental results show that the recognition rate can reach 99.36% and recognition time is not more than 0.2 milliseconds using a PC with 2.0GHz CPU. They demonstrate that the proposed method is effective and fast in digital recognition, which can meet the requirements for the automatic identification of digits on multimeters. It is significant for application in real time systems.

关键词： Electron tubes Feature extraction image recognition Digital images image segmentation Instruments image color analysis

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An efficient algorithm for recognition of pointer scale value on dashboard

An efficient algorithm for recognition of pointer scale valu...

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international congress on image and signal processing, biomedical engineering and informatics (CISP-BMEI)

作者： Pengfei Li Qingxiang Wu Yao Xiao Yanan Zhang Key Laboratory of OptoElectronic Science and Technology for Medicine of Ministry of Education Fujian Normal University Fujian Fuzhou China Fujian Provincial Key Laboratory for Photonics Technology Fujian Normal University Fujian Fuzhou China

ISBN: (纸本)9781538619384;9781538619377

In this article an efficient pointer recognition algorithm is proposed to identify the pointer and it is used in real-time testing the scale of the dashboard pointer. The algorithm can be used to identify any type of dashboard pointer such as red color pointer, yellow color pointer and white color pointer etc. Pointer recognition is divided into two parts; configuration and testing. The parameters are required in testing of the dashboard such as the minimum, maximum and nonuniform scale of the dashboard. These parameters are collected in the configuration interface. These parameters are loaded when testing. The scale value of pointer is calculated according to the parameters after pointer position is detected. Configurations are setup for different types of dashboard so that any types of dashboard pointer can be detected. If all the scale values of the test points are correct values within an allowable error and the instrument is considered qualified during the test. Otherwise that the instrument is considered to be defective. Finally pointer recognition algorithm is applied to automobile instrument quality detection and has achieved good results.

关键词： Testing image color analysis Instruments Standards image recognition signal processing algorithms Photonics

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