2017 10th International Congress on Image and Signal Processing, BioMedical Engineering and Informatics (CISP-BMEI)最新文献_第8页

Comparison of non-negative matrix factorization and convolution kernel compensation in surface electromyograms of forearm muscles 非负矩阵分解与卷积核补偿在前臂肌表面电图上的比较

2017 10th International Congress on Image and Signal Processing, BioMedical Engineering and Informatics (CISP-BMEI) Pub Date : 2017-10-01 DOI: 10.1109/CISP-BMEI.2017.8302216

M. Šavc, V. Glaser, A. Holobar, I. Cikajlo, Z. Matjačić

{"title":"Comparison of non-negative matrix factorization and convolution kernel compensation in surface electromyograms of forearm muscles","authors":"M. Šavc, V. Glaser, A. Holobar, I. Cikajlo, Z. Matjačić","doi":"10.1109/CISP-BMEI.2017.8302216","DOIUrl":"https://doi.org/10.1109/CISP-BMEI.2017.8302216","url":null,"abstract":"This contribution compares performances of nonnegative matrix factorization and high-density surface electromyogram (EMG) decomposition on EMG signals recoded from forearm muscles of young healthy subjects. During the EMG measurements, subjects performed dynamic wrist extensions and flexions and universal haptic device robot was used to oppose their movements and to measure wrist kinematics and excreted muscle forces. Recoded EMG signals were independently decomposed by Convolution Kernel Compensation technique and by alternating least squares non-negative matrix factorization. The identified motor unit discharge patterns were summed into cumulative spike trains and compared with non-negative components in each measurement. The results demonstrated good match (average correlation coefficient of 0.92 ± 0.06), but several discrepancies between the identified components have also been observed. In particular, when limiting the time support of identified components to active EMG signal portions only, the average correlation coefficient dropped to 0.72 ±0.20.","PeriodicalId":6474,"journal":{"name":"2017 10th International Congress on Image and Signal Processing, BioMedical Engineering and Informatics (CISP-BMEI)","volume":"1 1","pages":"1-5"},"PeriodicalIF":0.0,"publicationDate":"2017-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"76287933","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 1

Object classification using basic-level categories 使用基本级别类别的对象分类

2017 10th International Congress on Image and Signal Processing, BioMedical Engineering and Informatics (CISP-BMEI) Pub Date : 2017-10-01 DOI: 10.1109/CISP-BMEI.2017.8302327

Mariusz Mulka, Wojciech A. Lorkiewicz, R. Katarzyniak

引用次数: 0

Online dynamic magnetic resonance imaging based on pseudo-polar sampling and GPU acceleration 基于伪极坐标采样和GPU加速的在线动态磁共振成像

2017 10th International Congress on Image and Signal Processing, BioMedical Engineering and Informatics (CISP-BMEI) Pub Date : 2017-10-01 DOI: 10.1109/CISP-BMEI.2017.8302180

Qiushi Meng, Zhaoyang Jin

{"title":"Online dynamic magnetic resonance imaging based on pseudo-polar sampling and GPU acceleration","authors":"Qiushi Meng, Zhaoyang Jin","doi":"10.1109/CISP-BMEI.2017.8302180","DOIUrl":"https://doi.org/10.1109/CISP-BMEI.2017.8302180","url":null,"abstract":"Most of the online dynamic magnetic resonance imaging (dMRI) techniques are developed based on Cartesian trajectories. Recently, radial trajectories have been proposed to acquire image data for online dMRI. Compared with Cartesian trajectories, radial trajectories cover densely at k-space center and are more incoherent. When using compressed sensing technique to reconstruct dynamic images with under-sampling radial k-space data, the regridding procedure is employed, however it is usually time consuming and introduces numerical errors. In this study, a novel radial-like pseudo-polar (PP) trajectory was used for online dMRI. PP trajectory can avoid regridding and inverse-regridding operation by using a pseudopolar FFT (PPFFT) operation without interpolation. In the reconstructiongraphics processing unit (GPU) is used to further decrease the reconstruction time and achieve real-time online effect. In this simulation study, cardiac k-space dataset was fully acquired and using as a reference dataset. The PP trajectory was used to retrospectively under-sample k-space data with 12.5% and 25% coverage. The reconstruction results show that, the image quality of online dMRI based on PP under-sampling is higher than that of radial under-sampling based method. The reconstruction time was significantly shorten by using GPU acceleration, for the tested case, it is more than 20 times faster than the CPU computing.","PeriodicalId":6474,"journal":{"name":"2017 10th International Congress on Image and Signal Processing, BioMedical Engineering and Informatics (CISP-BMEI)","volume":"137 1","pages":"1-5"},"PeriodicalIF":0.0,"publicationDate":"2017-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"73229150","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 1

Power waveforms analysis of GNSS-R delay signals from beidou GEO satellite 北斗GEO卫星GNSS-R延迟信号功率波形分析

2017 10th International Congress on Image and Signal Processing, BioMedical Engineering and Informatics (CISP-BMEI) Pub Date : 2017-10-01 DOI: 10.1109/CISP-BMEI.2017.8302134

Mohammad Shohidul Islam, Dong-Kai Yang, Muhammad Abdul Alim Sikder

{"title":"Power waveforms analysis of GNSS-R delay signals from beidou GEO satellite","authors":"Mohammad Shohidul Islam, Dong-Kai Yang, Muhammad Abdul Alim Sikder","doi":"10.1109/CISP-BMEI.2017.8302134","DOIUrl":"https://doi.org/10.1109/CISP-BMEI.2017.8302134","url":null,"abstract":"GNSS remote sensing can play an important rule for monitoring the earth's surface. For this why, this technique is proposed to retrieve some information from water surface. Specifically, scattered signals from the ocean surface can provide significant information of water surface and its current condition. In this paper, direct and scattered power waveforms of GEO Satellite of Beidou are mainly focused and analyzed. Due to delay of reflected signal, the waveforms pattern & characteristics are clearly different from direct signals. These power waveforms are determined for different coherent integration times, different wind speeds and different satellite elevation angles. Z-V scattering model and Tanos Elfouhaily wave spectrum model are utilized to present the power waveforms of reflected signals and these waveforms are compared with the power waveforms of reflected & direct signals of real data. It is observed that these waveforms are sensitive to integration time, wind speed and satellite elevation angle. It is also found that the waveforms are directly related to these three parameters (time, wind speed & elevation angle). As a result, the model waveforms and real data waveforms have performed a good matching each other.","PeriodicalId":6474,"journal":{"name":"2017 10th International Congress on Image and Signal Processing, BioMedical Engineering and Informatics (CISP-BMEI)","volume":"24 1","pages":"1-6"},"PeriodicalIF":0.0,"publicationDate":"2017-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"74619347","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Single-color image motion deblurring using MTV model 使用MTV模型的单色图像运动去模糊

2017 10th International Congress on Image and Signal Processing, BioMedical Engineering and Informatics (CISP-BMEI) Pub Date : 2017-10-01 DOI: 10.1109/CISP-BMEI.2017.8301961

Hong Zhang, Guodong Wang, Nan Wu, Guojia Hou, Zhimei Zhang

引用次数: 0

A novel method of text representation on hybrid neural networks 一种基于混合神经网络的文本表示方法

2017 10th International Congress on Image and Signal Processing, BioMedical Engineering and Informatics (CISP-BMEI) Pub Date : 2017-10-01 DOI: 10.1109/CISP-BMEI.2017.8302099

Yanbu Guo, Chen Jin, Weihua Li, Chen Ji, Yuanye Fang, Yunhao Duan

{"title":"A novel method of text representation on hybrid neural networks","authors":"Yanbu Guo, Chen Jin, Weihua Li, Chen Ji, Yuanye Fang, Yunhao Duan","doi":"10.1109/CISP-BMEI.2017.8302099","DOIUrl":"https://doi.org/10.1109/CISP-BMEI.2017.8302099","url":null,"abstract":"Text representation is one of the fundamental problems in text analysis tasks. The key of text representation is to extract and express the semantic and syntax feature of texts. The order-sensitive sequence models based on neural networks have achieved great progress in text representation. Bidirectional Long Short-Term Memory (BiLSTM) Neural Networks, as an extension of Recurrent Neural Networks (RNN), not only can deal with variable-length texts, capture the long-term dependencies in texts, but also model the forward and backward sequence contexts. Moreover, typical neural networks, Convolutional Neural Networks (CNN), can extract more semantic and structural information from texts, because of their convolution and pooling operations. The paper proposes a hybrid model, which combines the BiLSTM with 2-dimensial convolution and 1-dimensial pooling operations. In other words, the model firstly captures the abstract representation vector of texts by the BiLSTM, and then extracts text semantic features by 2-dimensial convolutional and 1-dimensial pooling operations. Experiments on text classification tasks show that our method obtains preferable performances compared with the state-of-the-art models when applied on the MR1 sentence polarity dataset.","PeriodicalId":6474,"journal":{"name":"2017 10th International Congress on Image and Signal Processing, BioMedical Engineering and Informatics (CISP-BMEI)","volume":"43 1","pages":"1-6"},"PeriodicalIF":0.0,"publicationDate":"2017-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"74114597","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Collaborative filtering denoising algorithm based on the nonlocal centralized sparse representation model 基于非局部集中稀疏表示模型的协同滤波去噪算法

2017 10th International Congress on Image and Signal Processing, BioMedical Engineering and Informatics (CISP-BMEI) Pub Date : 2017-10-01 DOI: 10.1109/CISP-BMEI.2017.8301951

Jing Liu, Ruijiao Liu, Jinlei Chen, Yajie Yang, Douli Ma

引用次数: 1

Efficient deep learning for stereo matching with larger image patches 基于深度学习的大图像块立体匹配

2017 10th International Congress on Image and Signal Processing, BioMedical Engineering and Informatics (CISP-BMEI) Pub Date : 2017-10-01 DOI: 10.1109/CISP-BMEI.2017.8301999

Yiliu Feng, Zhengfa Liang, Hengzhu Liu

引用次数: 14

Three class emotions recognition based on deep learning using staked autoencoder 基于深度学习的三类情绪识别

2017 10th International Congress on Image and Signal Processing, BioMedical Engineering and Informatics (CISP-BMEI) Pub Date : 2017-10-01 DOI: 10.1109/CISP-BMEI.2017.8302098

Banghua Yang, Xu Han, Jianzhen Tang

{"title":"Three class emotions recognition based on deep learning using staked autoencoder","authors":"Banghua Yang, Xu Han, Jianzhen Tang","doi":"10.1109/CISP-BMEI.2017.8302098","DOIUrl":"https://doi.org/10.1109/CISP-BMEI.2017.8302098","url":null,"abstract":"Emotion recognition is a hot spot in advanced humancomputer interaction system, which is of great significance in artificial intelligence, health care, distance education, military field and so on. The paper builds a stacked autoencoder deep learning classification network consist of an input layer, two autoencoder hidden layers and a softmax classifier output layer based on SJTU Emotion EEG Dataset (SEED). Pretrain the first autoencoder employed L-BFGS to optimize the cost function. Then pretrain the second autoencoder with the output of first autoencoder. Finally send to the softmax classifier. Pretrain each autoencoder in forward propagation, then fine-tuning the whole network in back propagation. The well-trained network is used to classify three emotion states including happy, neural and grief. The raw inputs are differential entropy of EEG signal in five rhythmic frequencies band and the differential entropy of whole EEG signal. Fourteen experiments are performed with 5-fold cross validation, the average classification accuracy of three class emotion states is 59.6%, 66.27%, 71.97%, 78.48%, 82.56% and 85.5%. The result shows the higher frequency band differential entropy like Gamma band is more relative to emotion reaction.","PeriodicalId":6474,"journal":{"name":"2017 10th International Congress on Image and Signal Processing, BioMedical Engineering and Informatics (CISP-BMEI)","volume":"21 1","pages":"1-5"},"PeriodicalIF":0.0,"publicationDate":"2017-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"84477602","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 15

Deep feature selection and projection for cross-age face retrieval 跨年龄人脸检索的深度特征选择与投影

2017 10th International Congress on Image and Signal Processing, BioMedical Engineering and Informatics (CISP-BMEI) Pub Date : 2017-10-01 DOI: 10.1109/CISP-BMEI.2017.8301986

Kaihua Tang, Xiao-Nan Hou, Zhiwen Shao, Lizhuang Ma

{"title":"Deep feature selection and projection for cross-age face retrieval","authors":"Kaihua Tang, Xiao-Nan Hou, Zhiwen Shao, Lizhuang Ma","doi":"10.1109/CISP-BMEI.2017.8301986","DOIUrl":"https://doi.org/10.1109/CISP-BMEI.2017.8301986","url":null,"abstract":"While traditional PIE (pose, illumination and expression) face variations have been well settled by latest methods, a new kind of variation, cross-age variation, is drawing attention from researchers. Most of the existing methods fail to maintain the effectiveness in real world applications that contain significant gap of age. Cross-age variation is caused by the shape deformation and texture changing of human faces while people getting old. It will result in tremendous intra-personal changes of face feature that deteriorate the performance of algorithms. This paper proposed a deep feature based framework for face retrieval problem. Our framework uses deep CNNs feature descriptor and two well designed post-processing methods to achieve age-invariance. To the best of our knowledge, this is the first deep feature based method in cross-age face retrieval problem. The deep CNNs model we use is firstly trained on traditional PIE datasets and then fine-tuned by cross-age dataset. The feature selection and projection post-processing we propose is also proved to be very effective in eliminating cross-age variation of deep CNNs feature. The experiments conducted on Cross-Age Celebrity Dataset (CACD), which is the largest public dataset containing cross-age variation, show that our framework outperforms previous state-of-the-art methods.","PeriodicalId":6474,"journal":{"name":"2017 10th International Congress on Image and Signal Processing, BioMedical Engineering and Informatics (CISP-BMEI)","volume":"52 1","pages":"1-7"},"PeriodicalIF":0.0,"publicationDate":"2017-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"84581452","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 3