2017 10th International Congress on Image and Signal Processing, BioMedical Engineering and Informatics (CISP-BMEI)最新文献_第6页

A shadow removal method for tesseract text recognition 一种用于方块文本识别的阴影去除方法

2017 10th International Congress on Image and Signal Processing, BioMedical Engineering and Informatics (CISP-BMEI) Pub Date : 2017-10-01 DOI: 10.1109/CISP-BMEI.2017.8301946

Huimin Lu, B. Guo, Juntao Liu, Xijun Yan

引用次数: 11

A membrane-genetics algorithm for multi-objective optimization problems 多目标优化问题的膜遗传算法

2017 10th International Congress on Image and Signal Processing, BioMedical Engineering and Informatics (CISP-BMEI) Pub Date : 2017-10-01 DOI: 10.1109/CISP-BMEI.2017.8302326

Taowei Chen, Yiming Yu, Kun Zhao, Zhibing Yu

引用次数: 4

The application of BI-RADS feature in the ultrasound breast tumor CAD system BI-RADS特征在超声乳腺肿瘤CAD系统中的应用

2017 10th International Congress on Image and Signal Processing, BioMedical Engineering and Informatics (CISP-BMEI) Pub Date : 2017-10-01 DOI: 10.1109/CISP-BMEI.2017.8302276

Fan Zhang, Qinghua Huang, Xuelong Li

{"title":"The application of BI-RADS feature in the ultrasound breast tumor CAD system","authors":"Fan Zhang, Qinghua Huang, Xuelong Li","doi":"10.1109/CISP-BMEI.2017.8302276","DOIUrl":"https://doi.org/10.1109/CISP-BMEI.2017.8302276","url":null,"abstract":"The breast cancer is one of the most common diseases in women. This paper proposed a breast tumor computer aided diagnosis (CAD) system utilized the Breast Imaging Reporting and Data System (BI-RADS) features. The BI-RADS feature scoring scheme is designed to transform the BI-RADS report to a vector. And the decision tree algorithm is adopted to classify the vector. Compared with previous CAD system, the proposed system is easier to be understood by the clinician. Without the image preprocessing, the proposed system can be applied in different ultrasound machines. There are 440 samples collected from the Cancer Center of Sun Yat-sen University. In the experiment, the five-fold cross validation is employed to evaluate the proposed system. The result shows that the performance of the proposed system is better than the CAD method which takes the BI-RADS feature as a guide to extract features from images. The average accuracy achieves 89.38%, specificity is 90.74%, sensitivity is 86.18%, positive predictive value (PPV) reaches 93.57% and negative predictive value (NVP) is 79.82%.","PeriodicalId":6474,"journal":{"name":"2017 10th International Congress on Image and Signal Processing, BioMedical Engineering and Informatics (CISP-BMEI)","volume":"6 1","pages":"1-5"},"PeriodicalIF":0.0,"publicationDate":"2017-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"88780570","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

Video saliency detection based on robust seeds generation and spatio-temporal propagation 基于鲁棒种子生成和时空传播的视频显著性检测

2017 10th International Congress on Image and Signal Processing, BioMedical Engineering and Informatics (CISP-BMEI) Pub Date : 2017-10-01 DOI: 10.1109/CISP-BMEI.2017.8301936

Kai Tian, Zongqing Lu, Q. Liao, Na Wang

引用次数: 2

Facial expression recognition by learning spatiotemporal features with multi-layer independent subspace analysis 基于多层独立子空间分析学习时空特征的面部表情识别

2017 10th International Congress on Image and Signal Processing, BioMedical Engineering and Informatics (CISP-BMEI) Pub Date : 2017-10-01 DOI: 10.1109/CISP-BMEI.2017.8301920

ChenHan Lin, Fei Long, Yongjie Zhan

引用次数: 5

Study on super-resolution reconstruction algorithm based on sparse representation and dictionary learning for remote sensing image 基于稀疏表示和字典学习的遥感图像超分辨率重建算法研究

2017 10th International Congress on Image and Signal Processing, BioMedical Engineering and Informatics (CISP-BMEI) Pub Date : 2017-10-01 DOI: 10.1109/CISP-BMEI.2017.8302035

Xiangyu Zhao, Ru Yang, Zhentao Qin, Jianbing Wu

引用次数: 1

Classification Bayesian models of orientation identification with the known reference 分类贝叶斯模型的方向识别与已知的参考

2017 10th International Congress on Image and Signal Processing, BioMedical Engineering and Informatics (CISP-BMEI) Pub Date : 2017-10-01 DOI: 10.1109/CISP-BMEI.2017.8302106

Renyu Ye, Xinsheng Liu

{"title":"Classification Bayesian models of orientation identification with the known reference","authors":"Renyu Ye, Xinsheng Liu","doi":"10.1109/CISP-BMEI.2017.8302106","DOIUrl":"https://doi.org/10.1109/CISP-BMEI.2017.8302106","url":null,"abstract":"Humans may integrate the information of cues into target identification. We investigate how better the human brain identifies the orientation in the presence of the known reference. We first design a psychophysical experiment of orientation identification. The subjects estimate the orientation of a line which is intersected by a known oriented reference line. Four subjects performed the identification task. Estimates of the orientations exhibit the systematic increasing biases with the angle between the target line and the reference line increasing, and then the estimation precision of tilt orientations is obviously improved. We expound the identification process by Bayesian inference theory. We assume that the subjects first classify the stimuli and subsequently identify them. Then we put forward two classification Bayesian identification models: Directly Identifying Classification Bayesian Model (DCB) and Indirectly Identifying Classification Bayesian Model (ICB), in which the Equal-precision and Variable-precision encoding are considered. We compare our models' predictions to the experimental data. The results show that the variable-precision indirectly identifying classification Bayesian model fit better to the performance.","PeriodicalId":6474,"journal":{"name":"2017 10th International Congress on Image and Signal Processing, BioMedical Engineering and Informatics (CISP-BMEI)","volume":"10 1","pages":"1-6"},"PeriodicalIF":0.0,"publicationDate":"2017-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"77311546","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 gradient-domain image enhancement method for traffic signs in nighttime surveillance 一种夜间交通标志监控的梯度域图像增强方法

2017 10th International Congress on Image and Signal Processing, BioMedical Engineering and Informatics (CISP-BMEI) Pub Date : 2017-10-01 DOI: 10.1109/CISP-BMEI.2017.8301943

Simin Wang, Xiaoguang Li, Hui Zhang, L. Zhuo

引用次数: 1

Spectrum sensing for cognitive radio based on convolution neural network 基于卷积神经网络的认知无线电频谱感知

2017 10th International Congress on Image and Signal Processing, BioMedical Engineering and Informatics (CISP-BMEI) Pub Date : 2017-10-01 DOI: 10.1109/CISP-BMEI.2017.8302117

Dong Han, Gounou Charles Sobabe, Chenjie Zhang, Xuemei Bai, Zhijun Wang, Shuai Liu, Bin Guo

{"title":"Spectrum sensing for cognitive radio based on convolution neural network","authors":"Dong Han, Gounou Charles Sobabe, Chenjie Zhang, Xuemei Bai, Zhijun Wang, Shuai Liu, Bin Guo","doi":"10.1109/CISP-BMEI.2017.8302117","DOIUrl":"https://doi.org/10.1109/CISP-BMEI.2017.8302117","url":null,"abstract":"The problem in the process of spectrum sensing that the detection rate of the the primary user (PU) signal is low in the environment of low signal-to-noise (SNR) is present, a novel spectrum sensing algorithm based on convolution neural network (CNN) is proposed. The CNN is widely used in image recognition and speech recognition, and has good classification performance. Therefore, the CNN is employed to solve spectrum sensing which can be viewed as a binary hypothesis-testing problem. Firstly, the feature of the presence of the PU signal and the presence of only the noise signal are extracted, including cyclostationary feature and energy feature. And then, the extracted features should be pre-processed, which are used as the training input of the CNN model. Finally, the test data is fed into the trained CNN model, which is aiming to detect the presence of the PU. Experiment results show that a reasonable CNN model is built and the proposed algorithm has higher detection probability than cyclostationary feature detection (CFD) about 0.5 in −20dB.","PeriodicalId":6474,"journal":{"name":"2017 10th International Congress on Image and Signal Processing, BioMedical Engineering and Informatics (CISP-BMEI)","volume":"36 1","pages":"1-6"},"PeriodicalIF":0.0,"publicationDate":"2017-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"90688003","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: 45

A real-time distributed computing mechanism for P300 speller BCI P300拼写BCI的实时分布式计算机制

2017 10th International Congress on Image and Signal Processing, BioMedical Engineering and Informatics (CISP-BMEI) Pub Date : 2017-10-01 DOI: 10.1109/CISP-BMEI.2017.8302264

Wei Huang, Zhihua Huang

引用次数: 2