2018 11th International Congress on Image and Signal Processing, BioMedical Engineering and Informatics (CISP-BMEI)最新文献_第6页

Combined Influence of Classifiers, Window Lengths and Number of Channels on EMG Pattern Recognition for Upper Limb Movement Classification 分类器、窗口长度和通道数对上肢运动分类肌电模式识别的综合影响

2018 11th International Congress on Image and Signal Processing, BioMedical Engineering and Informatics (CISP-BMEI) Pub Date : 2018-10-01 DOI: 10.1109/CISP-BMEI.2018.8633114

Anyuan Zhang, Ning Gao, Liang Wang, Qi Li

{"title":"Combined Influence of Classifiers, Window Lengths and Number of Channels on EMG Pattern Recognition for Upper Limb Movement Classification","authors":"Anyuan Zhang, Ning Gao, Liang Wang, Qi Li","doi":"10.1109/CISP-BMEI.2018.8633114","DOIUrl":"https://doi.org/10.1109/CISP-BMEI.2018.8633114","url":null,"abstract":"Electromyogram pattern recognition (EMG-PR) is a very important method for upper limb movement classification. However, there are many factors such as classifiers, window lengths and number of channels which can make an influence on EMG-PR efficiency. Previous studies examined the effects of three different factors on EMG-PR separately. However, the combinations of three different factors (classifiers, window lengths and number of channels) may also affect the classification accuracy of EMG-PR. In present study, we discussed the effects of combinations of three different factors including classifiers, window lengths and number of channels on EMG-PR. We analyzed the different combinations of three factors. Four healthy subjects participated in this study, and they played five motions of hand and wrist in this experiment. We found that these three factors had a significant effect on EMG-PRe The performance of linear discriminant analysis (LDA) of EMG-PR outperformed the performance of back propagation neural network (BPNN) (p < 10−3). The classification accuracy of LDA is higher than support vector machine (SVM) (p < 10−3). In addition, 200 ms window length had enough data to classify the different motions. Furthermore, we also found that five channels has a significant increase when compared to three channels (p < 0.05). The proposed method can increase the performance of EMG-PR.","PeriodicalId":117227,"journal":{"name":"2018 11th International Congress on Image and Signal Processing, BioMedical Engineering and Informatics (CISP-BMEI)","volume":"7 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123485691","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: 5

A Pedestrian Detection Method Based on YOLOv3 Model and Image Enhanced by Retinex 基于YOLOv3模型和Retinex增强图像的行人检测方法

2018 11th International Congress on Image and Signal Processing, BioMedical Engineering and Informatics (CISP-BMEI) Pub Date : 2018-10-01 DOI: 10.1109/CISP-BMEI.2018.8633119

Hongquan Qu, Tongyang Yuan, Zhiyong Sheng, Yuan Zhang

{"title":"A Pedestrian Detection Method Based on YOLOv3 Model and Image Enhanced by Retinex","authors":"Hongquan Qu, Tongyang Yuan, Zhiyong Sheng, Yuan Zhang","doi":"10.1109/CISP-BMEI.2018.8633119","DOIUrl":"https://doi.org/10.1109/CISP-BMEI.2018.8633119","url":null,"abstract":"Pedestrian detection is a basic technology in the field of intelligent traffic video surveillance. It is also help for the optimization design of rail transport. It is known that the deep learning technology can achieve considerable performance on pedestrian detection. However, this kind of methods demand a large number of high-quality samples. In addition, the quality of data sample in the subway station is usually sensitive to the background environment, such as variant illumination or pedestrian density, which can significantly affect performance of the deep neural network. To solve this problem, this paper adopts an image enhancement policy based on the Retinex theory to preprocess training samples to reduce the influence of light changes. Firstly, we use the image enhancement method to enhance the contrast of the image and highlight the color of the object itself. Next, we put the initial sample into darknet frame with YOLOv3 to train the detection model1 and put the enhanced sample into the YOLOv3 to train the detection model 2. Finally, we tested these two models with 200 pedestrian pictures of four different scenarios. The experimental results show that the model trained by Retinex image enhancement has a more accurate detection rate of 94% compared with the model without the enhancement sample trained.","PeriodicalId":117227,"journal":{"name":"2018 11th International Congress on Image and Signal Processing, BioMedical Engineering and Informatics (CISP-BMEI)","volume":"59 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121401321","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: 25

Manchu Word Recognition Based on Convolutional Neural Network with Spatial Pyramid Pooling 基于空间金字塔池化卷积神经网络的满语词识别

2018 11th International Congress on Image and Signal Processing, BioMedical Engineering and Informatics (CISP-BMEI) Pub Date : 2018-10-01 DOI: 10.1109/CISP-BMEI.2018.8633131

Min Li, Rui-rui Zheng, Shuang Xu, Yu Fu, Di Huang

{"title":"Manchu Word Recognition Based on Convolutional Neural Network with Spatial Pyramid Pooling","authors":"Min Li, Rui-rui Zheng, Shuang Xu, Yu Fu, Di Huang","doi":"10.1109/CISP-BMEI.2018.8633131","DOIUrl":"https://doi.org/10.1109/CISP-BMEI.2018.8633131","url":null,"abstract":"Manchu character recognition is important in protecting and researching Manchu culture and history. Previous methods of Manchu character recognition are mainly based on conventional machine learning using shallow artificial selection features, thus recognition results are unsatisfactory. The method with convolutional neural networks achieves high accuracy on optical character recognition as the convolution operators can automatically extract deep structure features. The convolutional neural network needs input images with the fixed size, but as a kind of phonemic language, the Manchu word has an arbitrary length. So it is needed to normalize the size of images if applying conventional convolutional neural network directly on Manchu word recognition. This normalization process will restrain the promotion of Manchu character recognition accuracy. This paper utilizes the spatial pyramid pooling layer instead of the last max-pooling layer in a convolutional neural network, and proposes a classifier for recognizing the arbitrary size Manchu word without segmenting the word. Without need of normalizing image sizes, the proposed model obtains the better recognition accuracy. The experiments indicate that the proposed Manchu word recognition models achieve the highest accuracy of 0.9768, higher than the conventional convolutional neural network. Furthermore there is no normalization on input images with arbitrary sizes in recognizing process. The proposed Manchu word recognition models outperform conventional counterparts in both accuracy and flexibility.","PeriodicalId":117227,"journal":{"name":"2018 11th International Congress on Image and Signal Processing, BioMedical Engineering and Informatics (CISP-BMEI)","volume":"37 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125331063","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

A Reduced Memory AVS Decoder for Compressed HD Video 压缩高清视频的减少内存AVS解码器

2018 11th International Congress on Image and Signal Processing, BioMedical Engineering and Informatics (CISP-BMEI) Pub Date : 2018-10-01 DOI: 10.1109/CISP-BMEI.2018.8633039

Z. Nie

引用次数: 0

A Novel Speckle Noise Reduction Algorithm for Old Movies Recovery 一种新的旧电影复原散斑降噪算法

2018 11th International Congress on Image and Signal Processing, BioMedical Engineering and Informatics (CISP-BMEI) Pub Date : 2018-10-01 DOI: 10.1109/CISP-BMEI.2018.8633183

Chen Liu, Xiaoguang Li, L. Zhuo, Jiafeng Li, Qingfeng Zhou

引用次数: 2

An Gaussian-Mixture Hidden Markov Models for Action Recognition Based on Key Frame 基于关键帧的高斯混合隐马尔可夫模型动作识别

2018 11th International Congress on Image and Signal Processing, BioMedical Engineering and Informatics (CISP-BMEI) Pub Date : 2018-10-01 DOI: 10.1109/CISP-BMEI.2018.8633176

Jinhong Li, T. Lei, Fengquan Zhang

引用次数: 3

Research on Digitally Reconstructed Radiograph Algorithm Improvement Based on Computed Tomography Image 基于计算机断层图像的数字重建x线图像算法改进研究

2018 11th International Congress on Image and Signal Processing, BioMedical Engineering and Informatics (CISP-BMEI) Pub Date : 2018-10-01 DOI: 10.1109/CISP-BMEI.2018.8633268

Tongying Li, Hongbo Zhu

引用次数: 1

Recognition of ErrP in P300 Speller Based on Time Series Pattern 基于时间序列模式的P300拼写错误识别

2018 11th International Congress on Image and Signal Processing, BioMedical Engineering and Informatics (CISP-BMEI) Pub Date : 2018-10-01 DOI: 10.1109/CISP-BMEI.2018.8633092

Zhifeng Lin, Zhihua Huang

引用次数: 0

A Crop-Based Multi-Branch Network for Matching Cost Computation 一种基于作物的匹配成本计算多分支网络

2018 11th International Congress on Image and Signal Processing, BioMedical Engineering and Informatics (CISP-BMEI) Pub Date : 2018-10-01 DOI: 10.1109/CISP-BMEI.2018.8633267

Yu Chen, Youshen Xia, Chenwang Wu

引用次数: 2

A Light Intensity Reverse Algorithm for Improving Dark Channel Prior Dehazing 一种改进暗通道先验去雾的光强反演算法

2018 11th International Congress on Image and Signal Processing, BioMedical Engineering and Informatics (CISP-BMEI) Pub Date : 2018-10-01 DOI: 10.1109/CISP-BMEI.2018.8633085

Qi Liu

引用次数: 1