... International Workshop on Pattern Recognition in NeuroImaging. International Workshop on Pattern Recognition in NeuroImaging最新文献_第10页

A fast CUM-m-Capon algorithm for DOA estimation based on fourth-order cumulant 基于四阶累积量的快速com -m- capon DOA估计算法

... International Workshop on Pattern Recognition in NeuroImaging. International Workshop on Pattern Recognition in NeuroImaging Pub Date : 2018-07-26 DOI: 10.1117/12.2501766

K. Chao, Xinyu Zhang, K. Huo, Weidong Jiang

引用次数: 0

Recognizing emotions in chinese text using dictionary and ensemble of classifiers 基于词典和分类器集成的汉语文本情感识别

... International Workshop on Pattern Recognition in NeuroImaging. International Workshop on Pattern Recognition in NeuroImaging Pub Date : 2018-07-26 DOI: 10.1117/12.2501916

Yanyong Ai, Zhenxiang Chen, Shanshan Wang, Ying Pang

{"title":"Recognizing emotions in chinese text using dictionary and ensemble of classifiers","authors":"Yanyong Ai, Zhenxiang Chen, Shanshan Wang, Ying Pang","doi":"10.1117/12.2501916","DOIUrl":"https://doi.org/10.1117/12.2501916","url":null,"abstract":"In recent years, subjective texts have shown great application value. As a hot research issues in the field of natural language processing, analysis of emotions in text, has attracted attentions from many scholars and also greatly develops research on the emotional polarity of Chinese texts. This paper presents an emotional classification algorithm combining dictionary and ensemble classifier. Firstly, based on the fusion of multiple dictionaries such as emotional dictionary, degree dictionary, and negative dictionary, output the negative and positive scores of each sentence according to the designed emotion calculation algorithm. Defining the difference value between negative and positive scores as the emotional tendency value, the samples are sorted by the amount of emotional tendency value and samples with the highest emotional tendency value are selected as the training samples. Finally, the ensemble classifier is used to classify the text emotions. Based on six machine learning algorithms including polynomial Bayes, decision tree, random forest, k-nearest neighbor, SVM, and logistic regression, the ensemble classifier aims to achieve the best classification effect and minimize the disadvantages of individual classifiers. The results show that the classification accuracy of the ensemble classifier is better than that of individual classifiers.","PeriodicalId":90079,"journal":{"name":"... International Workshop on Pattern Recognition in NeuroImaging. International Workshop on Pattern Recognition in NeuroImaging","volume":"26 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2018-07-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"84773346","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

Aesthetic QR code generation with background contrast enhancement and user interaction 美观的QR码生成与背景对比度增强和用户交互

... International Workshop on Pattern Recognition in NeuroImaging. International Workshop on Pattern Recognition in NeuroImaging Pub Date : 2018-07-26 DOI: 10.1117/12.2502054

Lijian Lin, Xinyi Zou, L. He, Sijiang Liu, Bo Jiang

引用次数: 1

An abnormal telephone identification model based on ensemble algorithm 基于集成算法的异常电话识别模型

... International Workshop on Pattern Recognition in NeuroImaging. International Workshop on Pattern Recognition in NeuroImaging Pub Date : 2018-07-26 DOI: 10.1117/12.2501790

Y. Yuan, Ke Ji, R. Sun, Kun Ma

{"title":"An abnormal telephone identification model based on ensemble algorithm","authors":"Y. Yuan, Ke Ji, R. Sun, Kun Ma","doi":"10.1117/12.2501790","DOIUrl":"https://doi.org/10.1117/12.2501790","url":null,"abstract":"Due to the rapid development of the communications industry and the popularization of telephones, more and more personal information leaks and telephone fraud cases have occurred in the life.For the problem of fraudulent calls, there are deficiencies for operators to solve these problems.Inspired by the ensemble algorithm, it was found that the bagging algorithm can solve the classification problem of unbalanced data.This paper proposes an abnormal phone recognition model based on bagging algorithm.In particular, we used PCA dimension reduction in processing data to better mine the effective features of the sample, Multiple training sets are constructed by bootstrap sampling, and the ensemble of multiple training set-trained learners can solve the classification problem of unbalanced abnormal telephone data. Experiments show that the accuracy of prediction results of the abnormal phone recognition model based on the integrated algorithm is better than the prediction results of the single decision tree model, and the problem of unbalanced samples was solved and a relatively ideal prediction effect was achieved.","PeriodicalId":90079,"journal":{"name":"... International Workshop on Pattern Recognition in NeuroImaging. International Workshop on Pattern Recognition in NeuroImaging","volume":"41 21 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2018-07-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"88757459","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

Target regression tracking based on convolutional neural network 基于卷积神经网络的目标回归跟踪

... International Workshop on Pattern Recognition in NeuroImaging. International Workshop on Pattern Recognition in NeuroImaging Pub Date : 2018-07-26 DOI: 10.1117/12.2501844

Hongwei Zhang, Xiang Fan, Bin Zhu, Bo Xie, Qi Ma

引用次数: 0

A computer aided diagnosis system for lung cancer detection using support vector machine 基于支持向量机的肺癌计算机辅助诊断系统

... International Workshop on Pattern Recognition in NeuroImaging. International Workshop on Pattern Recognition in NeuroImaging Pub Date : 2018-07-26 DOI: 10.1117/12.2502010

B. Şekeroğlu, Erkan Emirzade

{"title":"A computer aided diagnosis system for lung cancer detection using support vector machine","authors":"B. Şekeroğlu, Erkan Emirzade","doi":"10.1117/12.2502010","DOIUrl":"https://doi.org/10.1117/12.2502010","url":null,"abstract":"Computer aided diagnosis (CAD) is started to be implemented broadly in the diagnosis and detection of many varieties of abnormalities acquired during various imaging procedures. The main aim of the CAD systems is to increase the accuracy and decrease the time of diagnoses, while the general achievement for CAD systems are to find the place of nodules and to determine the characteristic features of them. As lung cancer is one of the fatal and leading cancer types, there has been plenty of studies for the usage of the CAD systems to detect lung cancer. Yet, the CAD systems need to be developed a lot to identify the different shapes of nodules, lung segmentation and to have higher level of sensitivity, specifity and accuracy. In this paper, Lung Image Database Consortium (LIDC) database is used which comprises of an image set of lung cancer thoracic documented CT scans. After performing image pre-processing, segmentation, feature extraction/selection steps, classification is utilized using Support Vector Machine (SVM) with Gaussian RBF and 97.3% of specificity and 92.0% of sensitivity is achieved which is superior to recently proposed CAD systems.","PeriodicalId":90079,"journal":{"name":"... International Workshop on Pattern Recognition in NeuroImaging. International Workshop on Pattern Recognition in NeuroImaging","volume":"67 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2018-07-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"78673894","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: 18

Design for omni-directional mobile wheelchair control system based on brain computer interface 基于脑机接口的全向移动轮椅控制系统设计

... International Workshop on Pattern Recognition in NeuroImaging. International Workshop on Pattern Recognition in NeuroImaging Pub Date : 2018-07-26 DOI: 10.1117/12.2501771

Jiaxing Lu, Linyan Wu, Weiwei Zai, Nuo Gao

{"title":"Design for omni-directional mobile wheelchair control system based on brain computer interface","authors":"Jiaxing Lu, Linyan Wu, Weiwei Zai, Nuo Gao","doi":"10.1117/12.2501771","DOIUrl":"https://doi.org/10.1117/12.2501771","url":null,"abstract":"Based on Stable-State Visual Evoked Potentials (SSVEP) signal generation method, the multi-mode control system with manual control, remote control and brain computer signal control for omni-directional wheelchair system is designed. The system structure design, EEG signal acquisition and recognition processing technology are introduced. The software architecture of the overall control system, kinematical modeling of the omni-directional wheelchair, implementation of fundamental motion control algorithm and the design of wheelchair motion control algorithm are expounded. The paper illustrates the software architecture of main control system, and the design of motion scheduling and controlling algorithm. The system utilizes user’s EEG signal to control movement of wheelchair; remotely controlling movement of wheelchair. The system has friendly interactive interface for staffs of monitor center or relatives of patients to supervise state of wheelchair motion and information of environment in real time. Experimental results prove that the system could stably and reliably analyze EEG signal, possessing some practical value.","PeriodicalId":90079,"journal":{"name":"... International Workshop on Pattern Recognition in NeuroImaging. International Workshop on Pattern Recognition in NeuroImaging","volume":"43 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2018-07-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"77363513","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

Hadoop-based analysis model of network public opinion and its implementation 基于hadoop的网络舆情分析模型及其实现

... International Workshop on Pattern Recognition in NeuroImaging. International Workshop on Pattern Recognition in NeuroImaging Pub Date : 2018-07-26 DOI: 10.1117/12.2502133

Fei Wang, Peiyu Liu, Zhenfang Zhu

引用次数: 1

Surface defects detection of paper dish based on Mask R-CNN 基于Mask R-CNN的纸盘表面缺陷检测

... International Workshop on Pattern Recognition in NeuroImaging. International Workshop on Pattern Recognition in NeuroImaging Pub Date : 2018-07-26 DOI: 10.1117/12.2502097

Xuelong Wang, Ying Gao, Junyu Dong, Xukun Qin, Lin Qi, Hui Ma, J. Liu

引用次数: 2

Rapid image retrieval with binary hash codes based on deep learning 基于深度学习的二进制哈希码快速图像检索

... International Workshop on Pattern Recognition in NeuroImaging. International Workshop on Pattern Recognition in NeuroImaging Pub Date : 2018-07-26 DOI: 10.1117/12.2502072

GuangWei Deng, Cheng Xu, Xiaohan Tu, Tao Li, Nan Gao

{"title":"Rapid image retrieval with binary hash codes based on deep learning","authors":"GuangWei Deng, Cheng Xu, Xiaohan Tu, Tao Li, Nan Gao","doi":"10.1117/12.2502072","DOIUrl":"https://doi.org/10.1117/12.2502072","url":null,"abstract":"With the ever-growing large-scale image data on the web, rapid image retrieval has become one of the hot spots in the multimedia field. And it is still very difficult to reliable image retrieval due to the complex image appearance variations. Inspired by the robustness of convolutional neural networks features, we propose an effective deep learning framework to generate compact similarity-preserving binary hash codes for rapid image retrieval. Our main idea is incorporating deep convolutional neural network (CNN) into hash functions to jointly learn feature representations and mappings from them to hash codes. In particular, our approach which learns hash codes and image representations takes pairs of images as training inputs. Meanwhile, an effective loss function is used to maximize the differentiability of the output space by encoding the supervised information from the input image pairs. We extensively evaluate the retrieval performance on two large-scale datasets CIFAR-10 and NUS-WIDE, and the evaluation shows that our method gives a better performance than traditional hashing learning methods in image retrieval.","PeriodicalId":90079,"journal":{"name":"... International Workshop on Pattern Recognition in NeuroImaging. International Workshop on Pattern Recognition in NeuroImaging","volume":"31 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2018-07-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"84295306","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