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

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Design of a Differential Vector Phase Locked Loop for Single Frequency RTK Receivers 单频RTK接收机差分矢量锁相环的设计
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.2018.8633106
S. Z. Farooq, T. Jin, Dongkai Yang, Echoda Ngbede Joshua Ada
{"title":"Design of a Differential Vector Phase Locked Loop for Single Frequency RTK Receivers","authors":"S. Z. Farooq, T. Jin, Dongkai Yang, Echoda Ngbede Joshua Ada","doi":"10.1109/CISP-BMEI.2018.8633106","DOIUrl":"https://doi.org/10.1109/CISP-BMEI.2018.8633106","url":null,"abstract":"Continuous tracking of carrier-phase signals is an important requirement for real-time kinematic (RTK) positioning under dynamic conditions. With the mass-market adaption of carrier-phase differential GNSS technology, there is a strong incentive to use single-frequency commercial receivers. For such receivers, a vector tracking based receiver architecture is a potential candidate for RTK due to improved performance under lower carrier-to-noise power density ratios and higher dynamics as compared to conventional scalar receivers. The performance of vector tracking loops can be further improved for differential positioning, if differential carrier-phase measurements are directly used in the tracking loop of rover receiver. This study presents the design of a vector phase locked loop using single difference carrier-phase measurements directly in the tracking loop for a single frequency dynamic rover. With careful system design and cycle slip detection and correction strategy, phase lock can be maintained providing a precise solution.","PeriodicalId":117227,"journal":{"name":"2018 11th International Congress on Image and Signal Processing, BioMedical Engineering and Informatics (CISP-BMEI)","volume":"45 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":"127977615","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
An Improved K_Means Algorithm for Document Clustering Based on Knowledge Graphs 基于知识图的改进K_Means聚类算法
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.2018.8633187
Xiaoli Wang, Ying Li, Meihong Wang, Zixiang Yang, Huailin Dong
{"title":"An Improved K_Means Algorithm for Document Clustering Based on Knowledge Graphs","authors":"Xiaoli Wang, Ying Li, Meihong Wang, Zixiang Yang, Huailin Dong","doi":"10.1109/CISP-BMEI.2018.8633187","DOIUrl":"https://doi.org/10.1109/CISP-BMEI.2018.8633187","url":null,"abstract":"K _means algorithm is one of the typical clustering algorithms in text mining tasks. K_means algorithm is widely used in many areas because of its easy to implement and ability to handle large datasets with better scalability. However, the random selection of initial cluster centroid in traditional K_means algorithm for text clustering easily leads to local optimization and instability of clustering results. Therefore, in order to overcome this shortcoming, this paper propose an improved K_means algorithm for document clustering which based on following two points: (i)we used concept distance to optimize the choice of the initial cluster centroid, which can avoid the drawbacks caused by random selection; (ii)we adopted knowledge graphs to improve traditional k_means text clustering algorithm by optimizing the calculation of text similarity. Theoretical analysis and experimental results show that the improved algorithm could optimize the accuracy of text clustering effectively.","PeriodicalId":117227,"journal":{"name":"2018 11th International Congress on Image and Signal Processing, BioMedical Engineering and Informatics (CISP-BMEI)","volume":"742 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":"133692687","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: 6
Characterizing Adversarial Samples of Convolutional Neural Networks 卷积神经网络对抗性样本的表征
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.2018.8633182
Cheng Jiang, Qiyang Zhao, Yuzhong Liu
{"title":"Characterizing Adversarial Samples of Convolutional Neural Networks","authors":"Cheng Jiang, Qiyang Zhao, Yuzhong Liu","doi":"10.1109/CISP-BMEI.2018.8633182","DOIUrl":"https://doi.org/10.1109/CISP-BMEI.2018.8633182","url":null,"abstract":"Adversarial samples aim to make deep convolutional neural networks predict incorrectly under small perturbations. This paper investigates non-targeted adversarial samples of convolutional neural networks and makes a primitive attempt to characterize adversarial samples. Two observations are made: first, adversarial perturbations are mainly in the high-frequency domain; second, adversarial categories usually have strong semantic relevance to the original categories. Our two observations provide a solid basis to understand the behavior of convolutional neural networks and thus to improve their robustness against adversarial samples.","PeriodicalId":117227,"journal":{"name":"2018 11th International Congress on Image and Signal Processing, BioMedical Engineering and Informatics (CISP-BMEI)","volume":"23 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":"116572567","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
Motion-Blind Blur Removal for CT Images with Wasserstein Generative Adversarial Networks 基于Wasserstein生成对抗网络的CT图像运动盲模糊去除
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.2018.8633203
Yilin Lyu, Wei Jiang, Yaniun Lin, L. Voros, Miao Zhang, B. Mueller, B. Mychalczak, Yulin Song
{"title":"Motion-Blind Blur Removal for CT Images with Wasserstein Generative Adversarial Networks","authors":"Yilin Lyu, Wei Jiang, Yaniun Lin, L. Voros, Miao Zhang, B. Mueller, B. Mychalczak, Yulin Song","doi":"10.1109/CISP-BMEI.2018.8633203","DOIUrl":"https://doi.org/10.1109/CISP-BMEI.2018.8633203","url":null,"abstract":"Advanced deblurring techniques for computed tomography (CT) images are necessary and crucial to the improvement of accuracy of patient diagnosis in radiology and patient setup and treatment response assessment in radiation oncology. Currently, medical image deblurring is a challenging technical problem due to the unpredictability of patient motion. This paper introduces a new method of computed tomography image deblurring based on Conditional Generative Adversarial Networks (CGAN) that have been broadly implemented in computer vision research. A Wasserstein Generative Adversarial Network (WGAN) with adversarial loss and l1 perceptual loss was proposed and trained by a blur-sharp image pair dataset created in-house and evaluated by Peak Signal to Noise Ratio (PSNR) and Structural Similarity Index (SSIM). These experiments showed the effectiveness of the approach, which outperforms other competing deblurring techniques both quantitatively and qualitatively.","PeriodicalId":117227,"journal":{"name":"2018 11th International Congress on Image and Signal Processing, BioMedical Engineering and Informatics (CISP-BMEI)","volume":"10 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":"131186904","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
Learning Multi-Domain Convolutional Network for RGB-T Visual Tracking 学习多域卷积网络用于RGB-T视觉跟踪
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.2018.8633180
Xingming Zhang, Xuehan Zhang, Xuedan Du, Xiangming Zhou, Jun Yin
{"title":"Learning Multi-Domain Convolutional Network for RGB-T Visual Tracking","authors":"Xingming Zhang, Xuehan Zhang, Xuedan Du, Xiangming Zhou, Jun Yin","doi":"10.1109/CISP-BMEI.2018.8633180","DOIUrl":"https://doi.org/10.1109/CISP-BMEI.2018.8633180","url":null,"abstract":"Object tracking is one of the challenging problems in the field of computer vision. Affected by the unstructured environments, for example, the occlusion, noise, and light, These factors can affect the appearance of the specific object and result in failures when tracking specific objects. To address this issue, we propose a novel visual tracking method based on multimodal convolutional network learning. Our framework adopts a parallel structure, which consists of two shallow convolutional neural networks. First, the parallel network is used to draw the different features of the RGB- T (RGB and thermal) data separately. Second, this two kind of features are mixed together and finally the mixed feature is sent to domain-specific layers for binary classification and identification of the targets. We perform comprehensive experiments on RGBT234 visual data and the results prove that the proposed visual tracking method improves the effects significantly through the use of multi-modal features, which illustrates that our method is competitive in performances against with the state-of-the-art tracking algorithms.","PeriodicalId":117227,"journal":{"name":"2018 11th International Congress on Image and Signal Processing, BioMedical Engineering and Informatics (CISP-BMEI)","volume":"128 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":"128182374","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: 16
Fast Image Registration by LB Method 基于LB的快速图像配准
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.2018.8633107
Yu Chen, G. Courbebaisse, Dongxiang Lu
{"title":"Fast Image Registration by LB Method","authors":"Yu Chen, G. Courbebaisse, Dongxiang Lu","doi":"10.1109/CISP-BMEI.2018.8633107","DOIUrl":"https://doi.org/10.1109/CISP-BMEI.2018.8633107","url":null,"abstract":"Image registration is a key pre-procedure for high level image processing. However, for the complexity and accuracy of the algorithm, the image registration algorithm always has high time complexity. This means it is not suitable for realtime image processing, such as real-time target tracking. To speed up the registration algorithm, parallel computation is a good solution, for example, parallelizing the algorithm by LB method. In this paper, we proposed a LB based model for image registration. The main idea of our method consists in simulating the convection diffusion equation by establishing a LB model. Experiments shows our model is effective. Theoretically, our model can not get more accurate registration than the classical numerical method. But as a kind of numerical tool, our model is stable and faster, the most important is the potential for parallel image processing.","PeriodicalId":117227,"journal":{"name":"2018 11th International Congress on Image and Signal Processing, BioMedical Engineering and Informatics (CISP-BMEI)","volume":"1 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":"128456819","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
Music Emotions Recognition Based on Feature Analysis 基于特征分析的音乐情绪识别
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.2018.8633223
Chaohui Lv, Shengnan Li, Linxiao Huang
{"title":"Music Emotions Recognition Based on Feature Analysis","authors":"Chaohui Lv, Shengnan Li, Linxiao Huang","doi":"10.1109/CISP-BMEI.2018.8633223","DOIUrl":"https://doi.org/10.1109/CISP-BMEI.2018.8633223","url":null,"abstract":"Music emotions recognition (MER) is a challenging field of studies addressed in multiple disciplines such as musicology, cognitive science, psychology, arts and affective computing. In this paper, music emotions are classified into four types known as exciting, happy, serene and sad. MER is formulated as a classification problem in cognitive computing where music features are extracted. And, the feature sets are input into Support Vector Machine (SVM) and Convolutional Neural Networks to classify the music emotion. It can be seen that the best accuracy of 88.2% in VGG16 where Chirplet has been turned into features images. The results show that the feature graph is feasible for music emotion classification.","PeriodicalId":117227,"journal":{"name":"2018 11th International Congress on Image and Signal Processing, BioMedical Engineering and Informatics (CISP-BMEI)","volume":"19 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":"134184297","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
Improved Denoising Auto-Encoders for Image Denoising 改进的图像去噪自动编码器
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.2018.8633143
Qian Xiang, Xuliang Pang
{"title":"Improved Denoising Auto-Encoders for Image Denoising","authors":"Qian Xiang, Xuliang Pang","doi":"10.1109/CISP-BMEI.2018.8633143","DOIUrl":"https://doi.org/10.1109/CISP-BMEI.2018.8633143","url":null,"abstract":"Image denoising is an important pre-processing step in image analysis. Various denoising algorithms, such as BM3D, PCD and K-SVD, obtain remarkable effects. Recently a deep denoising auto-encoder has been proposed and shown excellent performance compared to conventional image denoising algorithms. In this paper, we study the statistical features of restored image residuals produced by Denoising Auto-encoders and propose an improved training loss function for Denoising Auto-encoders based on Method noise and entropy maximization principle, with residual statistics as constraint conditions. We compare it with conventional denoising algorithms including original Denoising Auto-encoders, BM3D, total variation (TV) minimization, and non-local mean (NLM) algorithms. Experiments indicate that the Improved Denoising Auto-encoders introduce less non-existent artifacts and are more robustness than other state-of-the-art denoising methods in both PSNR and SSIM indexes, especially under low SNR.","PeriodicalId":117227,"journal":{"name":"2018 11th International Congress on Image and Signal Processing, BioMedical Engineering and Informatics (CISP-BMEI)","volume":"782 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":"133216175","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: 12
Differential Activity of Semantic Processing by Chinese-Japanese Bilingual Subjects: An fMRI Study 中日双语被试语义加工差异活动的fMRI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.2018.8633214
Xiujun Li, Jingjing Yang, Qi Li, Dan Tong, Jinglong Wu
{"title":"Differential Activity of Semantic Processing by Chinese-Japanese Bilingual Subjects: An fMRI Study","authors":"Xiujun Li, Jingjing Yang, Qi Li, Dan Tong, Jinglong Wu","doi":"10.1109/CISP-BMEI.2018.8633214","DOIUrl":"https://doi.org/10.1109/CISP-BMEI.2018.8633214","url":null,"abstract":"Previous brain neuro-imaging studies used functional magnetic resonance imaging (fMRI) to elucidate different human brain activities, and bilinguals used their second language (L2) to understand their first language (L1). So how do the first and second foreign languages work in the brain? Based on the study of Chinese and English bilingualism, this paper finds out that reading involves a unique language system, in which alphabetical English reading is formed by Chinese as a bilingual language. The results of brain activity in native English have been determined and some specific brain regions have been identified. However, many studies have shown that there are different activation modes between alphabetic languages and graphic languages. In this study, the subjects was asked to determine whether the two Japanese characters (or Chinese characters) were the same. When controlling tasks, the font size is determined to be 107 and 127 respectively. The subjects responded to the subjects by pressed the corresponding keys of the index their finger and the button corresponded to the middle finger of the right hand. Our conclusion is: second the nervous system of language reading is made up of mother tongue. Our findings support our conclusion.","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":"133474661","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
A Multi-Dimensional Hyperspectral Image Mosaic Method and its Acquisition System 一种多维高光谱图像拼接方法及其采集系统
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.2018.8633038
Chen Yuan, Mei Zhou, Li Sun, Song Qiu, Qingli Li
{"title":"A Multi-Dimensional Hyperspectral Image Mosaic Method and its Acquisition System","authors":"Chen Yuan, Mei Zhou, Li Sun, Song Qiu, Qingli Li","doi":"10.1109/CISP-BMEI.2018.8633038","DOIUrl":"https://doi.org/10.1109/CISP-BMEI.2018.8633038","url":null,"abstract":"With the combination of both spectral and spatial information, hyperspectral images can offer much detailed information for researchers. In this paper, a multi-dimensional hyperspectral image mosaic method has been proposed to stitch hyperspectral images. This method combines texture information of the single gray image, spatial information of the hyperspectral image and the position information gathered during the acquisition process. We apply this method to the medical hyperspectral image stitch and the experimental results prove the accuracy of this method compared to other single-dimensional methods.","PeriodicalId":117227,"journal":{"name":"2018 11th International Congress on Image and Signal Processing, BioMedical Engineering and Informatics (CISP-BMEI)","volume":"1 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":"127668136","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: 2
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