Pub Date : 2020-10-17DOI: 10.1109/CISP-BMEI51763.2020.9263623
Wen-jing Kang, Changqing Xie, Jin Yao, L. Xuan, Gongliang Liu
Multiple object tracking suffers from many challenges including huge computation work, crowd scenes. In order to solve these problems, we proposed a novel online multiple object tracking algorithm based on recurrent neural networks (RNNs) and appearance model. Compared to traditional algorithms, the RNNs can handle the motion state of the target well because it is trained with a quantity of data extracted from real world scenes. In addition, RNNs is helpful to improve tracking speed because it predicts the trajectories of objects without complex appearance calculations. The appearance feature is significant for tracking, especially in crowed scenes. The appearance model is extracted by convolutional neural networks trained with MARS dataset which is more targeted for the multi object tracking. In order to balance the speed and accuracy of tracking, a novel simple decision method was proposed to decide which features should be used. Otherwise, the cascade matching is integrated into the data association to solve a lot of subproblems in tracking. The experimental evaluation shows our algorithm is fast and accurate.
{"title":"Online Multiple Object Tracking with Recurrent Neural Networks and Appearance Model","authors":"Wen-jing Kang, Changqing Xie, Jin Yao, L. Xuan, Gongliang Liu","doi":"10.1109/CISP-BMEI51763.2020.9263623","DOIUrl":"https://doi.org/10.1109/CISP-BMEI51763.2020.9263623","url":null,"abstract":"Multiple object tracking suffers from many challenges including huge computation work, crowd scenes. In order to solve these problems, we proposed a novel online multiple object tracking algorithm based on recurrent neural networks (RNNs) and appearance model. Compared to traditional algorithms, the RNNs can handle the motion state of the target well because it is trained with a quantity of data extracted from real world scenes. In addition, RNNs is helpful to improve tracking speed because it predicts the trajectories of objects without complex appearance calculations. The appearance feature is significant for tracking, especially in crowed scenes. The appearance model is extracted by convolutional neural networks trained with MARS dataset which is more targeted for the multi object tracking. In order to balance the speed and accuracy of tracking, a novel simple decision method was proposed to decide which features should be used. Otherwise, the cascade matching is integrated into the data association to solve a lot of subproblems in tracking. The experimental evaluation shows our algorithm is fast and accurate.","PeriodicalId":346757,"journal":{"name":"2020 13th International Congress on Image and Signal Processing, BioMedical Engineering and Informatics (CISP-BMEI)","volume":"32 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-10-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126348891","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}
Pub Date : 2020-10-17DOI: 10.1109/CISP-BMEI51763.2020.9263675
Wenbin Zhang, Yushuo Tan, Y. Pu
In this paper, a new fault identification method was proposed based on EEMD permutation entropy and grey relation degree. Firstly, the sampled data was denoised by morphological filter. Secondly, the denoised signal was decomposed into a finite number of stationary intrinsic mode functions (IMF). Thirdly, the permutation entropy were calculated to express some containing the most dominant fault information. Different fault type corresponds with different permutation entropy distribution. Finally, the grey relation degree between the symptom set and standard fault set was calculated as the identification evidence for fault diagnosis. The practical results show that this method is quite effective in gear fault identification. It’s suitable for on-line monitoring and diagnosis of gear system.
{"title":"A New Gear Fault Identification Method Based on EEMD Permutation Entropy and Grey Relation Degree","authors":"Wenbin Zhang, Yushuo Tan, Y. Pu","doi":"10.1109/CISP-BMEI51763.2020.9263675","DOIUrl":"https://doi.org/10.1109/CISP-BMEI51763.2020.9263675","url":null,"abstract":"In this paper, a new fault identification method was proposed based on EEMD permutation entropy and grey relation degree. Firstly, the sampled data was denoised by morphological filter. Secondly, the denoised signal was decomposed into a finite number of stationary intrinsic mode functions (IMF). Thirdly, the permutation entropy were calculated to express some containing the most dominant fault information. Different fault type corresponds with different permutation entropy distribution. Finally, the grey relation degree between the symptom set and standard fault set was calculated as the identification evidence for fault diagnosis. The practical results show that this method is quite effective in gear fault identification. It’s suitable for on-line monitoring and diagnosis of gear system.","PeriodicalId":346757,"journal":{"name":"2020 13th International Congress on Image and Signal Processing, BioMedical Engineering and Informatics (CISP-BMEI)","volume":"30 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-10-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123058193","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}
Pub Date : 2020-10-17DOI: 10.1109/CISP-BMEI51763.2020.9263588
Li Zhao, Zhi-hua Jia, Lin He, Yan Bian, Yong Sun, Hongwei Li
Red light LEDs have shown excellent results from the research of phototherapy for cardiovascular diseases, and the optimal values of light parameters and irradiation time are one of the focuses of their research. Studying the change of LED irradiance and temperature with the irradiation time, and the effect of temperature on irradiance has important reference valued for the phototherapy research of cardiovascular system diseases. Based on the mechanism and application of LED in cardiovascular system diseases, this article tested and analyzed the irradiance and temperature of medical red LED. The experimental results show that as the light time increases, the temperature of the LED increases, and the irradiance decreases. After about 5min, the change rate is small and tends to be stable. Through regression analysis, an approximate relationship curve between the two is obtained.
{"title":"Performance Analysis of LED Light Sources Based on Cardiovascular Disease Treatment","authors":"Li Zhao, Zhi-hua Jia, Lin He, Yan Bian, Yong Sun, Hongwei Li","doi":"10.1109/CISP-BMEI51763.2020.9263588","DOIUrl":"https://doi.org/10.1109/CISP-BMEI51763.2020.9263588","url":null,"abstract":"Red light LEDs have shown excellent results from the research of phototherapy for cardiovascular diseases, and the optimal values of light parameters and irradiation time are one of the focuses of their research. Studying the change of LED irradiance and temperature with the irradiation time, and the effect of temperature on irradiance has important reference valued for the phototherapy research of cardiovascular system diseases. Based on the mechanism and application of LED in cardiovascular system diseases, this article tested and analyzed the irradiance and temperature of medical red LED. The experimental results show that as the light time increases, the temperature of the LED increases, and the irradiance decreases. After about 5min, the change rate is small and tends to be stable. Through regression analysis, an approximate relationship curve between the two is obtained.","PeriodicalId":346757,"journal":{"name":"2020 13th International Congress on Image and Signal Processing, BioMedical Engineering and Informatics (CISP-BMEI)","volume":"3 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-10-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129924961","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}
Pub Date : 2020-10-17DOI: 10.1109/CISP-BMEI51763.2020.9263531
Qun Wang, Yajing Wang, Zhiwen Liu, Yuan-yuan Piao, Tao Yu
A novel algorithm for seizure prediction based on patient specific manner was proposed to improve the accuracy of epilepsy prediction. Time-frequency features and spatial features were extracted from each channel by 4s windows with 2s overlap. A continuous 10-min sample was selected from 1h before seizure onset by preictal period selection, which achieved maximum linear separability compared with inter ictal period. The effective features selected by elastic net and effective channels selected adaptively in greedy manner were input into SVM. The algorithm is tested on MIT scalp EEG database and the database collected in Xuanwu Hospital Capital Medical University. The algorithm can achieve a sensitivity of 94.61% and a false positive rate of 0.1484/h in MIT database, and a sensitivity of 95.14% and a false positive rate of 0.1312/h in Xuanwu Hospital database. The results show that the algorithm in this paper has higher sensitivity and lower false positive rate.
{"title":"A Patient Specific Seizure Prediction in Long Term EEG based on Adaptive Channel Selection and Preictal Period Selection","authors":"Qun Wang, Yajing Wang, Zhiwen Liu, Yuan-yuan Piao, Tao Yu","doi":"10.1109/CISP-BMEI51763.2020.9263531","DOIUrl":"https://doi.org/10.1109/CISP-BMEI51763.2020.9263531","url":null,"abstract":"A novel algorithm for seizure prediction based on patient specific manner was proposed to improve the accuracy of epilepsy prediction. Time-frequency features and spatial features were extracted from each channel by 4s windows with 2s overlap. A continuous 10-min sample was selected from 1h before seizure onset by preictal period selection, which achieved maximum linear separability compared with inter ictal period. The effective features selected by elastic net and effective channels selected adaptively in greedy manner were input into SVM. The algorithm is tested on MIT scalp EEG database and the database collected in Xuanwu Hospital Capital Medical University. The algorithm can achieve a sensitivity of 94.61% and a false positive rate of 0.1484/h in MIT database, and a sensitivity of 95.14% and a false positive rate of 0.1312/h in Xuanwu Hospital database. The results show that the algorithm in this paper has higher sensitivity and lower false positive rate.","PeriodicalId":346757,"journal":{"name":"2020 13th International Congress on Image and Signal Processing, BioMedical Engineering and Informatics (CISP-BMEI)","volume":"78 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-10-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126821568","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}
Pub Date : 2020-10-17DOI: 10.1109/CISP-BMEI51763.2020.9263573
Xiaoying Wei, Yanhua Cao, Xiaozhong Yang
Image segmentation is a key technique in the image processing and a classic problem. A Monte Carlo segmentation algorithm based on the Markov Random Field (MRF) image model is proposed to randomly initialize the model parameters, so as to avoid the over-dependence of the algorithm on the initial value and overcome the shortcomings of the local optimal solution of the existing iterative algorithm. Firstly, the MRF model can make full use of the neighborhood relationship of pixel space to obtain the data field information of the image. Then according to the Bayesian theory, the prior knowledge of images is transformed into the prior distribution model. Finally, the Monte Carlo segmentation algorithm is used to iterate until the maximum posterior probability is reached, thus, the distribution of image labels is obtained, that is, the process of image segmentation is completed. The segmentation experiment shows that the Monte Carlo algorithm can overcome the shortcoming of the traditional iterative algorithm, which is trapped in the local optimal, and can segment the image in a more complete and detailed way, effectively realize the accuracy of segmentation, and improve the speed of image segmentation.
图像分割是图像处理中的一项关键技术，也是一个经典问题。提出了一种基于马尔可夫随机场(Markov Random Field, MRF)图像模型的蒙特卡罗分割算法，对模型参数进行随机初始化，避免了算法对初始值的过度依赖，克服了现有迭代算法局部最优解的缺点。首先，MRF模型可以充分利用像素空间的邻域关系获取图像的数据场信息;然后根据贝叶斯理论，将图像的先验知识转化为先验分布模型。最后，使用蒙特卡罗分割算法进行迭代，直到达到最大后验概率，从而得到图像标签的分布，即完成图像分割过程。分割实验表明，蒙特卡罗算法克服了传统迭代算法陷入局部最优的缺点，能够将图像分割得更完整、更细致，有效地实现了分割的准确性，提高了图像分割的速度。
{"title":"The Monte Carlo Algorithm for Image Segmentation Based on the MRF Model","authors":"Xiaoying Wei, Yanhua Cao, Xiaozhong Yang","doi":"10.1109/CISP-BMEI51763.2020.9263573","DOIUrl":"https://doi.org/10.1109/CISP-BMEI51763.2020.9263573","url":null,"abstract":"Image segmentation is a key technique in the image processing and a classic problem. A Monte Carlo segmentation algorithm based on the Markov Random Field (MRF) image model is proposed to randomly initialize the model parameters, so as to avoid the over-dependence of the algorithm on the initial value and overcome the shortcomings of the local optimal solution of the existing iterative algorithm. Firstly, the MRF model can make full use of the neighborhood relationship of pixel space to obtain the data field information of the image. Then according to the Bayesian theory, the prior knowledge of images is transformed into the prior distribution model. Finally, the Monte Carlo segmentation algorithm is used to iterate until the maximum posterior probability is reached, thus, the distribution of image labels is obtained, that is, the process of image segmentation is completed. The segmentation experiment shows that the Monte Carlo algorithm can overcome the shortcoming of the traditional iterative algorithm, which is trapped in the local optimal, and can segment the image in a more complete and detailed way, effectively realize the accuracy of segmentation, and improve the speed of image segmentation.","PeriodicalId":346757,"journal":{"name":"2020 13th International Congress on Image and Signal Processing, BioMedical Engineering and Informatics (CISP-BMEI)","volume":"41 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-10-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122684803","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}
Pub Date : 2020-10-17DOI: 10.1109/CISP-BMEI51763.2020.9263545
Shan Liu, Mingxi Li, Shicong Song, Jinge Li, Yan Yan
In the era of media convergence, the communication mode of movie and television dramas has undergone great changes from cross-screen communication to cross-platform communication. It has been a challenge to construct a comprehensive, effective, reasonable analysis of the communication effects. The purpose of this research is to construct a comprehensive cross-platform communication effect evaluation index system for movies and television dramas based on the Analytic Hierarchy Process (AHP) and Technique for Order Preference by Similarity to Ideal Solution (TOPSIS). The simulation results demonstrated that our model can evaluate the comprehensive communication effect and rank movies and TV dramas accurately.
在媒介融合时代，影视剧的传播方式发生了巨大的变化，从跨屏传播到跨平台传播。如何构建全面、有效、合理的传播效果分析已成为一个挑战。本研究的目的是基于层次分析法(AHP)和TOPSIS法(Order Preference Technique by Similarity to Ideal Solution, TOPSIS)，构建一个综合性的影视剧跨平台传播效果评价指标体系。仿真结果表明，该模型能够较准确地评价影视剧的综合传播效果，并对影视剧进行排名。
{"title":"Cross-platform Communication Effect Evaluation Model for Movies and TV Dramas","authors":"Shan Liu, Mingxi Li, Shicong Song, Jinge Li, Yan Yan","doi":"10.1109/CISP-BMEI51763.2020.9263545","DOIUrl":"https://doi.org/10.1109/CISP-BMEI51763.2020.9263545","url":null,"abstract":"In the era of media convergence, the communication mode of movie and television dramas has undergone great changes from cross-screen communication to cross-platform communication. It has been a challenge to construct a comprehensive, effective, reasonable analysis of the communication effects. The purpose of this research is to construct a comprehensive cross-platform communication effect evaluation index system for movies and television dramas based on the Analytic Hierarchy Process (AHP) and Technique for Order Preference by Similarity to Ideal Solution (TOPSIS). The simulation results demonstrated that our model can evaluate the comprehensive communication effect and rank movies and TV dramas accurately.","PeriodicalId":346757,"journal":{"name":"2020 13th International Congress on Image and Signal Processing, BioMedical Engineering and Informatics (CISP-BMEI)","volume":"19 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-10-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122927717","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}
Pub Date : 2020-10-17DOI: 10.1109/CISP-BMEI51763.2020.9263671
Jie-Cyun Huang, Xinrui Zhao, L. Zheng
Micro expression is a facial feature that can reflect the most real emotional state hidden in the human heart. This is a very short process and difficult to capture accurately. Based convolutional network, a new network architecture (SHCFNet) is proposed to extract the spatial-temporal feature of peak frames, the optical flow between onset and apex frame and its derivative (optical strain). The proposed network stacks these features from the outcomes of the previous layer. Then, the stacked feature is merged with the convolution feature of the previous layer, which enhances the learnability of neurons. The performance of the proposed SHCFNet are evaluated on four benchmark datasets: CASME I, CASME II, SAMM and SMIC, and compared with other advanced networks.
{"title":"SHCFNet on Micro-expression Recognition System","authors":"Jie-Cyun Huang, Xinrui Zhao, L. Zheng","doi":"10.1109/CISP-BMEI51763.2020.9263671","DOIUrl":"https://doi.org/10.1109/CISP-BMEI51763.2020.9263671","url":null,"abstract":"Micro expression is a facial feature that can reflect the most real emotional state hidden in the human heart. This is a very short process and difficult to capture accurately. Based convolutional network, a new network architecture (SHCFNet) is proposed to extract the spatial-temporal feature of peak frames, the optical flow between onset and apex frame and its derivative (optical strain). The proposed network stacks these features from the outcomes of the previous layer. Then, the stacked feature is merged with the convolution feature of the previous layer, which enhances the learnability of neurons. The performance of the proposed SHCFNet are evaluated on four benchmark datasets: CASME I, CASME II, SAMM and SMIC, and compared with other advanced networks.","PeriodicalId":346757,"journal":{"name":"2020 13th International Congress on Image and Signal Processing, BioMedical Engineering and Informatics (CISP-BMEI)","volume":"17 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-10-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127908939","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}
Pub Date : 2020-10-17DOI: 10.1109/CISP-BMEI51763.2020.9263510
Fan Meng, Xue Ni, Guang Yang, Qianqian Jia
Low-speed dim small targets are not easily detected by radar in a clutter environment. In this paper, we propose a novel approach to improve the detection probability of low-speed dim small targets, which is to convert radar data into two-dimensional images to achieve background noise suppression. Firstly, we extract the data of the target and its surroundings by setting the detection domain and make the radar data map into the data of 256 gray grades for image processing. In order to suppress clutter, we develop the improved Bilateral filter (IBF) and apply the Doppler velocity as a weight term of the Gaussian function. Combined with the weight term of spatial distance, the detection domain can be significantly enhanced. Then, the target region contour is extracted by the adaptive threshold segmentation method from the background, and the target focused is accumulated, combining with Doppler velocity. The results show that the proposed method can effectively keep the edge of the target domain and weaken the noise background, thereby improving the detection probability of the target.
{"title":"A Novel Method for Low-Speed Dim Small Target Detection","authors":"Fan Meng, Xue Ni, Guang Yang, Qianqian Jia","doi":"10.1109/CISP-BMEI51763.2020.9263510","DOIUrl":"https://doi.org/10.1109/CISP-BMEI51763.2020.9263510","url":null,"abstract":"Low-speed dim small targets are not easily detected by radar in a clutter environment. In this paper, we propose a novel approach to improve the detection probability of low-speed dim small targets, which is to convert radar data into two-dimensional images to achieve background noise suppression. Firstly, we extract the data of the target and its surroundings by setting the detection domain and make the radar data map into the data of 256 gray grades for image processing. In order to suppress clutter, we develop the improved Bilateral filter (IBF) and apply the Doppler velocity as a weight term of the Gaussian function. Combined with the weight term of spatial distance, the detection domain can be significantly enhanced. Then, the target region contour is extracted by the adaptive threshold segmentation method from the background, and the target focused is accumulated, combining with Doppler velocity. The results show that the proposed method can effectively keep the edge of the target domain and weaken the noise background, thereby improving the detection probability of the target.","PeriodicalId":346757,"journal":{"name":"2020 13th International Congress on Image and Signal Processing, BioMedical Engineering and Informatics (CISP-BMEI)","volume":"44 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-10-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128842371","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}
Pub Date : 2020-10-17DOI: 10.1109/CISP-BMEI51763.2020.9263601
Lecheng Weng, Xiaoqiang Li, Wenfeng Wang
In the process of a finger vein image acquisition, finger vein images are susceptible to external factors like finger posture and light source conditions, which will result in poor recognition accuracy. Therefore, a finger vein recognition method based on improved convolution neural net work is proposed to improve the accuracy and robustness of the image recognition. Firstly, the collected finger vein image is preprocessed by image segmentation, finger root key point location and image extraction in the region of interest (ROI). Secondly, according to the application context of finger vein recognition, the convolution neural network structure is adjusted appropriately, and the output of convolution layer is standardized in batches. The optimized neural network is used to automatically extract, classify and identify the features of the preprocessed images. A large number of experiments were performed on public finger print data sets of Shandong University. The optimal recognition rates are 90% respectively. The experiments verify the effectiveness of this method.
{"title":"Finger vein recognition based on Deep Convolutional Neural Networks","authors":"Lecheng Weng, Xiaoqiang Li, Wenfeng Wang","doi":"10.1109/CISP-BMEI51763.2020.9263601","DOIUrl":"https://doi.org/10.1109/CISP-BMEI51763.2020.9263601","url":null,"abstract":"In the process of a finger vein image acquisition, finger vein images are susceptible to external factors like finger posture and light source conditions, which will result in poor recognition accuracy. Therefore, a finger vein recognition method based on improved convolution neural net work is proposed to improve the accuracy and robustness of the image recognition. Firstly, the collected finger vein image is preprocessed by image segmentation, finger root key point location and image extraction in the region of interest (ROI). Secondly, according to the application context of finger vein recognition, the convolution neural network structure is adjusted appropriately, and the output of convolution layer is standardized in batches. The optimized neural network is used to automatically extract, classify and identify the features of the preprocessed images. A large number of experiments were performed on public finger print data sets of Shandong University. The optimal recognition rates are 90% respectively. The experiments verify the effectiveness of this method.","PeriodicalId":346757,"journal":{"name":"2020 13th International Congress on Image and Signal Processing, BioMedical Engineering and Informatics (CISP-BMEI)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-10-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129111850","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}
Pub Date : 2020-10-17DOI: 10.1109/CISP-BMEI51763.2020.9263542
Liying Zhou, Xiaomin Li, Yi Liu, W. Zuo
This paper is based on literature dataset about Artificial Intelligence from SCI and EL A series of indices, such as Documents, Times Cited, CNCI, Highly Cited Papers, Hot Papers and EI Controlled Terms are used to analyze the research status and trends in the field of artificial intelligence in 2018-2020. Based on Documents, Times Cited and CNCI, high-yield countries, high-yield institutions, high-impact countries and high-impact institutions are identified. Based on Highly Cited Papers, Hot Papers and EI Controlled Terms, the most productive topics and the most influential topics in AI subject are identified. The results show that: AI is the third most productive sub-field in the Computer Science, and it produces the most highly cited papers and hot papers; the three countries with most total paper output are China mainland, USA, and Japan, while the top three countries with highest average paper impact are USA, England and United Kingdom; China mainland has the most high-yield institutions, among which Tsinghua University ranks first; the most influential topics discussed in highly cited papers are Decision Making, Neural Networks, Convolution, Fuzzy Sets, Deep Learning, Learning Algorithms, etc.
{"title":"Data Analytics for Artificial Intelligence Research from 2018 to 2020","authors":"Liying Zhou, Xiaomin Li, Yi Liu, W. Zuo","doi":"10.1109/CISP-BMEI51763.2020.9263542","DOIUrl":"https://doi.org/10.1109/CISP-BMEI51763.2020.9263542","url":null,"abstract":"This paper is based on literature dataset about Artificial Intelligence from SCI and EL A series of indices, such as Documents, Times Cited, CNCI, Highly Cited Papers, Hot Papers and EI Controlled Terms are used to analyze the research status and trends in the field of artificial intelligence in 2018-2020. Based on Documents, Times Cited and CNCI, high-yield countries, high-yield institutions, high-impact countries and high-impact institutions are identified. Based on Highly Cited Papers, Hot Papers and EI Controlled Terms, the most productive topics and the most influential topics in AI subject are identified. The results show that: AI is the third most productive sub-field in the Computer Science, and it produces the most highly cited papers and hot papers; the three countries with most total paper output are China mainland, USA, and Japan, while the top three countries with highest average paper impact are USA, England and United Kingdom; China mainland has the most high-yield institutions, among which Tsinghua University ranks first; the most influential topics discussed in highly cited papers are Decision Making, Neural Networks, Convolution, Fuzzy Sets, Deep Learning, Learning Algorithms, etc.","PeriodicalId":346757,"journal":{"name":"2020 13th International Congress on Image and Signal Processing, BioMedical Engineering and Informatics (CISP-BMEI)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-10-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116957258","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}
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