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2020 13th International Congress on Image and Signal Processing, BioMedical Engineering and Informatics (CISP-BMEI)最新文献

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Soft Subspace Clustering With Entropy Constraints 具有熵约束的软子空间聚类
2020 13th International Congress on Image and Signal Processing, BioMedical Engineering and Informatics (CISP-BMEI) Pub Date : 2020-10-17 DOI: 10.1109/CISP-BMEI51763.2020.9263597
Man Li, Lihong Wang
{"title":"Soft Subspace Clustering With Entropy Constraints","authors":"Man Li, Lihong Wang","doi":"10.1109/CISP-BMEI51763.2020.9263597","DOIUrl":"https://doi.org/10.1109/CISP-BMEI51763.2020.9263597","url":null,"abstract":"This paper investigates a class of soft subspace clustering algorithms which integrates the negative entropy term in the objective functions. It is necessary and hard to determine the coefficient of negative entropy in practice, which prevents the algorithms from applications. In order to solve the problem of parameter selection, a modified objective function with entropy constraints is proposed by moving the negative entropy from the objective function of ERKM (Entropy Regularization K-Means) algorithm to the constraints. The updating rules are given by theoretical analysis and the performance is evaluated experimentally using ten UCI datasets. The experimental studies demonstrate that the results of the proposed algorithm (ERKM+) outperform the original ERKM and other two k-means-type clustering algorithms in most cases.","PeriodicalId":346757,"journal":{"name":"2020 13th International Congress on Image and Signal Processing, BioMedical Engineering and Informatics (CISP-BMEI)","volume":"119 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":"123480243","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
Deep Learning-Based Fully Automated Detection and Segmentation of Breast Mass 基于深度学习的乳腺肿块全自动检测与分割
2020 13th International Congress on Image and Signal Processing, BioMedical Engineering and Informatics (CISP-BMEI) Pub Date : 2020-10-17 DOI: 10.1109/CISP-BMEI51763.2020.9263538
Hui Yu, Ru Bai, J. An, Rui Cao
{"title":"Deep Learning-Based Fully Automated Detection and Segmentation of Breast Mass","authors":"Hui Yu, Ru Bai, J. An, Rui Cao","doi":"10.1109/CISP-BMEI51763.2020.9263538","DOIUrl":"https://doi.org/10.1109/CISP-BMEI51763.2020.9263538","url":null,"abstract":"In the field of breast mass detection, there are many of small-scale masses in the image. However, most of the existing target detection models have low accuracy in detecting small-scale masses, which is prone to error detection and missing detection. In order to improve the detection accuracy of small-scale masses, this paper proposed a small scale target detection model Dense-Mask R-CNN based on Mask R-CNN, which is suitable for breast masses detection. Firstly, this paper improves the internal structure of FPN, and modifies the lateral connection mode in the original FPN structure to dense connection. Secondly, modify the size of the anchor of RPN to improve the location accuracy of small-scale masses. This paper uses the CBIS-DDSM dataset for all experiments. The results show that the AP value of the improved model for detecting breast masses reached 0.65 in the test set, which was 0.04 higher than that of the original Mask R-CNN.","PeriodicalId":346757,"journal":{"name":"2020 13th International Congress on Image and Signal Processing, BioMedical Engineering and Informatics (CISP-BMEI)","volume":"51 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":"123601696","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
An approach to detecting ErrP elicited by feedback of P300 Speller BCI based on coefficients of determination 基于确定系数的P300拼写者脑机接口反馈ErrP检测方法
2020 13th International Congress on Image and Signal Processing, BioMedical Engineering and Informatics (CISP-BMEI) Pub Date : 2020-10-17 DOI: 10.1109/CISP-BMEI51763.2020.9263583
Ting Li, Zhihua Huang
{"title":"An approach to detecting ErrP elicited by feedback of P300 Speller BCI based on coefficients of determination","authors":"Ting Li, Zhihua Huang","doi":"10.1109/CISP-BMEI51763.2020.9263583","DOIUrl":"https://doi.org/10.1109/CISP-BMEI51763.2020.9263583","url":null,"abstract":"Error-related potentials (ErrP) is elicited when an individual makes or perceives an error while conducting or observing a task. Many researchers are striving to improve performance of brain-computer interfaces (BCIs) by detecting ErrP in brain signals of single trial. However, it is still difficult to attain high performance of ErrP detections, which is needed by those who attempt to use ErrP. In this paper, we propose a novel approach to detecting ErrP. It computes a coefficient of determination for each time point after error onset according to brain signals of all trials in training set of an individual, constructs a curve of coefficients of determination (CCD) over time for each channel, obtains a time window for an individual by searching the peak of the mean of CCDs over his/her frontocentral scalp area, transforms the brain signals in the time window to feature vectors, and finally builds a classifier on the feature vectors for an individual. We compared this approach with such commonly used methods as xDAWN, PCA and ICA in a brain signal data set of eight individuals, which was acquired during the experiments of P300 Speller BCI comprising feedback, showing that our approach could achieve a better performance from a comprehensive perspective on accuracy, AUC and F1-score.","PeriodicalId":346757,"journal":{"name":"2020 13th International Congress on Image and Signal Processing, BioMedical Engineering and Informatics (CISP-BMEI)","volume":"63 ","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-10-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"120877211","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
Sentence Modeling via Graph Construction and Graph Neural Networks for Semantic Textual Similarity 基于图构建和图神经网络的句子语义文本相似度建模
2020 13th International Congress on Image and Signal Processing, BioMedical Engineering and Informatics (CISP-BMEI) Pub Date : 2020-10-17 DOI: 10.1109/CISP-BMEI51763.2020.9263691
Ke Zhou, Ke Xu, Tanfeng Sun, Yueguo Zhang
{"title":"Sentence Modeling via Graph Construction and Graph Neural Networks for Semantic Textual Similarity","authors":"Ke Zhou, Ke Xu, Tanfeng Sun, Yueguo Zhang","doi":"10.1109/CISP-BMEI51763.2020.9263691","DOIUrl":"https://doi.org/10.1109/CISP-BMEI51763.2020.9263691","url":null,"abstract":"Recently, using graph neural networks to model the hidden features of natural language has achieved success. In this paper, a novel sentence modeling method named TextSimGNN based on graphical representation is proposed to measure the semantic textual similarity. For embedding sentences into a graphical structure, we first construct a semantic textual graph which combines textual structure information and semantic information together. Then an end-to-end graph neural network is used to measure the similarity between graph pairs. The experiments show that our method has achieved good performance in semantic textual similarity task, which proves the advantage and effectiveness of graphical representation on natural language sentence modeling.","PeriodicalId":346757,"journal":{"name":"2020 13th International Congress on Image and Signal Processing, BioMedical Engineering and Informatics (CISP-BMEI)","volume":"72 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":"114720246","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 Super-pixel based Method for Instance Segmentation Post-processing 基于超像素的实例分割后处理方法
2020 13th International Congress on Image and Signal Processing, BioMedical Engineering and Informatics (CISP-BMEI) Pub Date : 2020-10-17 DOI: 10.1109/CISP-BMEI51763.2020.9263652
Yao Li, Lizhuang Ma
{"title":"A Super-pixel based Method for Instance Segmentation Post-processing","authors":"Yao Li, Lizhuang Ma","doi":"10.1109/CISP-BMEI51763.2020.9263652","DOIUrl":"https://doi.org/10.1109/CISP-BMEI51763.2020.9263652","url":null,"abstract":"We present a simple post-processing method for object instance segmentation. Instances segment images into parts with rich semantics while less texture consistency. Superpixels segment images into parts with great texture consistency while less semantics. We design a method, joining super-pixel to the instance segmentation workflow, in order to enhance the instance segmentation results. The workflow is, firstly calling a certain instance segmentation method (for example, Mask Region Convolutional Neural Network (R-CNN), Mask R-CNN) on the image to get prediction masks preliminary; then utilizing super-pixels as the assistant information to modify the prediction masks; and finally obtaining the better segmentation results. Our method is train-free, while it can refine the instance segmentation masks. Our experiments performed on multiple neural networks and the Microsoft Common Objects in Contexts (MS-COCO) dataset demonstrate the effectiveness of our method.","PeriodicalId":346757,"journal":{"name":"2020 13th International Congress on Image and Signal Processing, BioMedical Engineering and Informatics (CISP-BMEI)","volume":"108 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":"128160253","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
CISP-BMEI 2020 Index CISP-BMEI 2020指数
2020 13th International Congress on Image and Signal Processing, BioMedical Engineering and Informatics (CISP-BMEI) Pub Date : 2020-10-17 DOI: 10.1109/cisp-bmei51763.2020.9263497
{"title":"CISP-BMEI 2020 Index","authors":"","doi":"10.1109/cisp-bmei51763.2020.9263497","DOIUrl":"https://doi.org/10.1109/cisp-bmei51763.2020.9263497","url":null,"abstract":"","PeriodicalId":346757,"journal":{"name":"2020 13th International Congress on Image and Signal Processing, BioMedical Engineering and Informatics (CISP-BMEI)","volume":"8 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":"127987090","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 Tracking Algorithm for Occlusion Problem Based on STAPLE 一种基于STAPLE的改进遮挡跟踪算法
2020 13th International Congress on Image and Signal Processing, BioMedical Engineering and Informatics (CISP-BMEI) Pub Date : 2020-10-17 DOI: 10.1109/CISP-BMEI51763.2020.9263493
Fengxu Guan, Ziqi Wang, Xu Zhang, Haodong Cong, Shuai Gao
{"title":"An Improved Tracking Algorithm for Occlusion Problem Based on STAPLE","authors":"Fengxu Guan, Ziqi Wang, Xu Zhang, Haodong Cong, Shuai Gao","doi":"10.1109/CISP-BMEI51763.2020.9263493","DOIUrl":"https://doi.org/10.1109/CISP-BMEI51763.2020.9263493","url":null,"abstract":"Occlusion is one of the common problems in target tracking, which presents challenges for real-time and robust tracking. In order to solve the problem that the target is lost after being obscured, a STAPLE algorithm combined with SVM is presented in this paper. On this basis, occlusion detection, LBP-based deformation detection and multi-peak repositioning algorithm are added to solve the problem of template contaminated caused by target occlusion and insufficient robustness to distortion. When the target is distorted, the target model is continuously updated to maintain its robustness. When the decrease in confidence level is not caused by deformation, the target detection mechanism is activated and the update of the target model is stopped. The experimental results show that the proposed method is much better than previous algorithms.","PeriodicalId":346757,"journal":{"name":"2020 13th International Congress on Image and Signal Processing, BioMedical Engineering and Informatics (CISP-BMEI)","volume":"114 16","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-10-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131942762","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
Human Pose Estimation Based on the Multistage Learning and the Dense Connection 基于多阶段学习和密集连接的人体姿态估计
2020 13th International Congress on Image and Signal Processing, BioMedical Engineering and Informatics (CISP-BMEI) Pub Date : 2020-10-17 DOI: 10.1109/CISP-BMEI51763.2020.9263617
Weimin Shi, Qiaoning Yang, Juan Chen
{"title":"Human Pose Estimation Based on the Multistage Learning and the Dense Connection","authors":"Weimin Shi, Qiaoning Yang, Juan Chen","doi":"10.1109/CISP-BMEI51763.2020.9263617","DOIUrl":"https://doi.org/10.1109/CISP-BMEI51763.2020.9263617","url":null,"abstract":"The Human pose estimation is an important and challenging task for the computer vision. Existing human pose estimation networks often improve the accuracy of the pose estimation by feature fusion. However, most of them still suffer from information loss since they only consider high level features. In order to solve this problem, we propose a novel Multistage Learning Network named MS-Net. The key idea behind our approach is that different characteristics of joints may require different levels of feature. MS-Net first predicts joints at different stages of the network in a coarse-to-fine manner. By doing so, both geometric and semantic characteristics of the pose can be learned. Then, to deeper the networks understanding towards the pose, a dense connection is utilized to multiplex multi-level features, which further compensates for the loss of low-level features. We conduct comprehensive experiments on the coco dataset and results show that our model achieves remarkable improvements over state-of-the-art baselines.","PeriodicalId":346757,"journal":{"name":"2020 13th International Congress on Image and Signal Processing, BioMedical Engineering and Informatics (CISP-BMEI)","volume":"23 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":"134040145","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
Polyphase-modulated radar signal recognition based on time-frequency amplitude and phase features 基于时频幅值和相位特征的多相调制雷达信号识别
2020 13th International Congress on Image and Signal Processing, BioMedical Engineering and Informatics (CISP-BMEI) Pub Date : 2020-10-17 DOI: 10.1109/CISP-BMEI51763.2020.9263543
Xue Ni, Huali Wang, Yu Yang, Ying Zhu, Zhiguang Zhang
{"title":"Polyphase-modulated radar signal recognition based on time-frequency amplitude and phase features","authors":"Xue Ni, Huali Wang, Yu Yang, Ying Zhu, Zhiguang Zhang","doi":"10.1109/CISP-BMEI51763.2020.9263543","DOIUrl":"https://doi.org/10.1109/CISP-BMEI51763.2020.9263543","url":null,"abstract":"The current recognition method based on time-frequency analysis only uses the amplitude spectrum and ignores the phase spectrum, which leads to the recognition rate of radar signal low. In this paper, we propose an automatic recognition method based on time-frequency amplitude and phase features. For the signal time-frequency analysis, we take the short-time Fourier-based synchrosqueezing transform for radar signals to obtain two-dimensional time-frequency representation with complex values. Then, we extract the time-frequency amplitude spectrum and phase spectrum and take the absolute value of them as the two inputs of the recognition network. Next, we construct a deep convolutional network with two channels for automatic feature extraction and recognition. The simulation results on 5 kinds of polyphase codes show that the proposed method has superior performance in distinguishing the polyphase codes even at low SNR, and the phase features help to improve the recognition rate.","PeriodicalId":346757,"journal":{"name":"2020 13th International Congress on Image and Signal Processing, BioMedical Engineering and Informatics (CISP-BMEI)","volume":"39 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":"131626508","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
A Density-Based Adaptive Distance Fuzzy Clustering Algorithm Based on the Multi-target Traffic Radar 基于多目标交通雷达的密度自适应距离模糊聚类算法
2020 13th International Congress on Image and Signal Processing, BioMedical Engineering and Informatics (CISP-BMEI) Pub Date : 2020-10-17 DOI: 10.1109/CISP-BMEI51763.2020.9263685
Xinyi Zhang, Lin Cao, Tao Wang
{"title":"A Density-Based Adaptive Distance Fuzzy Clustering Algorithm Based on the Multi-target Traffic Radar","authors":"Xinyi Zhang, Lin Cao, Tao Wang","doi":"10.1109/CISP-BMEI51763.2020.9263685","DOIUrl":"https://doi.org/10.1109/CISP-BMEI51763.2020.9263685","url":null,"abstract":"In the multi-target traffic radar scene, the clustering accuracy between vehicles with close driving distance is relatively low. To solve this problem, this paper proposes a new clustering algorithm, namely a density-based adaptive distance fuzzy (DB-ADF) clustering algorithm. Firstly, the adaptive distance is used to calculate the similarity between points. Secondly, the neighborhood radius clusters are adaptively searched and discovered, which is the result of the initial clustering through cluster merging. Finally, the algorithm takes the result of the first clustering as the input of the second clustering, and iterates over the membership matrix and cluster center to obtain the clustering result. In the experiment, the proposed algorithm was run on the real radar datasets. The clustering performance of the DB-ADF algorithm was compared with the fuzzy c-means (FCM), Gustafson-Kessel (GK), and the density-based spatial clustering of applications with noise (DBSCAN) algorithm. The results show that the DB-ADF algorithm has higher clustering accuracy for the real radar data in some short-range vehicle scenarios.","PeriodicalId":346757,"journal":{"name":"2020 13th International Congress on Image and Signal Processing, BioMedical Engineering and Informatics (CISP-BMEI)","volume":"48 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":"132967613","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
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