2018 11th International Congress on Image and Signal Processing, BioMedical Engineering and Informatics (CISP-BMEI)最新文献_第4页

Application of High Level Synthesis in the Blade Tip Clearance Measurement System 高阶综合技术在叶尖间隙测量系统中的应用

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.2018.8633194

Dengyue Zhai, Min Xie, Jiadong Yuan, Siyuan Liu

引用次数: 1

A New Sidelobe Suppression Algorithm for SAR Images with an Arbituary Doppler Centroid 一种新的任意多普勒质心SAR图像旁瓣抑制算法

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.2018.8633018

Chunyan Mao, Junfeng Wang, Lei Tao

引用次数: 2

Nonlinear Manifold Feature Extraction Based on Spectral Supervised Canonical Correlation Analysis for Facial Expression Recognition with RRNN 基于谱监督典型相关分析的非线性流形特征提取在RRNN面部表情识别中的应用

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.2018.8633244

Asad Ullah, Jing Wang, M. Anwar, Usman Ahmad, Jin Wang, Uzair Saeed

{"title":"Nonlinear Manifold Feature Extraction Based on Spectral Supervised Canonical Correlation Analysis for Facial Expression Recognition with RRNN","authors":"Asad Ullah, Jing Wang, M. Anwar, Usman Ahmad, Jin Wang, Uzair Saeed","doi":"10.1109/CISP-BMEI.2018.8633244","DOIUrl":"https://doi.org/10.1109/CISP-BMEI.2018.8633244","url":null,"abstract":"A feature extraction method for Facial Expression Recognition Systems is proposed based on Spectral Supervised Canonical Correlation Analysis. For proper classification of expression it has been trained with Rethinking recurrent neural network. The Cohn Kanade Extensive and JAFFE databases are used in this paper. The images have been preprocessed using image normalization and then contrast limited adaptive histogram equalization to remove the illumination variance and noises. After down-sampling, the dimensions with factor data is provided to Spectral Supervised Canonical Correlation Analysis (SSCCA) which constructs affinity matrix that incorporates both the local structure and class information of the data points provided. Spectral feature is used for extracting features with more discriminative details, and revealing the nonlinear manifold structure of the data. SSCCA can effectively utilize the local structural information to discover low frequency coefficients more precisely. The method yields to more accurate and effective extraction compared to other methods. Data is provided to Rethinking recurrent neural network for training purpose. Meanwhile, the proposed method is more robust and effective compared to other methods in this field.","PeriodicalId":117227,"journal":{"name":"2018 11th International Congress on Image and Signal Processing, BioMedical Engineering and Informatics (CISP-BMEI)","volume":"13 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":"128368793","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: 4

Pedestrian Detection Based on Laplace Operator Image Enhancement Algorithm and Faster R-CNN 基于拉普拉斯算子图像增强算法和更快R-CNN的行人检测

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.2018.8633093

Q. Tian, Guangda Xie, Yanping Wang, Yuan Zhang

{"title":"Pedestrian Detection Based on Laplace Operator Image Enhancement Algorithm and Faster R-CNN","authors":"Q. Tian, Guangda Xie, Yanping Wang, Yuan Zhang","doi":"10.1109/CISP-BMEI.2018.8633093","DOIUrl":"https://doi.org/10.1109/CISP-BMEI.2018.8633093","url":null,"abstract":"Pedestrian detection is an important branch of computer vision. Many car manufactures have used this technology in real situation. Recently, deep learning has become the best method of pedestrian detection, and the advantage of deep neural network is that it can use statistical method to extract high-level features from raw sensory data and to obtain effective feature. Currently, Faster R-CNN is a typical framework, which commonly be used in the field of image processing. However, in order to achieve better performance in pedestrian detection, Faster R-CNN requires a large number of high-quality training samples. Due to the change of light and pedestrian density, the quality of the collected image is poor. Based on this problem, our research introduces Laplacian operator, it can enhance local image comparison. By introducing the Laplace operator, the proposed method can effectively preprocess the samples of the Faster R-CNN. The real data experiments verify the effectively of this algorithm as well as good robustness to the interference.","PeriodicalId":117227,"journal":{"name":"2018 11th International Congress on Image and Signal Processing, BioMedical Engineering and Informatics (CISP-BMEI)","volume":"91 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":"128274357","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: 7

A Fast Training Method for SAR Large Scale Samples Based on CNN for Targets Recognition 基于CNN的SAR大样本目标识别快速训练方法

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.2018.8633175

Yuan Zhang, Yang Song, Yanping Wang, Hongquan Qu

引用次数: 6

Analysis of Depression Magnetoencephalography Based on Modified Permutation Entropy 基于改进排列熵的抑郁脑磁图分析

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.2018.8633155

Zizhen Yuan, Wei Yan, Jun Wang, Jin Li, F. Hou

引用次数: 2

Deep Convolutional Network Method for Automatic Sleep Stage Classification Based on Neurophysiological Signals 基于神经生理信号的深度卷积网络睡眠阶段自动分类方法

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.2018.8633058

Yudong Sun, Bei Wang, Jing Jin, Xingyu Wang

引用次数: 25

Solving The Multi-Objective Optimization Model for Alpine Grassland Grazing With Modified Genetic Algorithms 用改进遗传算法求解高寒草地放牧多目标优化模型

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.2018.8633177

Xiaofeng Qin, Chen Zhang, Zijie Sun, Yu-an Zhang, R. Song, Meiyun Du

引用次数: 0

Track Segment Association of Maneuvering Target Based on Expectation Maximization 基于期望最大化的机动目标航迹段关联

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.2018.8633238

Jinping Sun, Naiyu Wang, Zhiguo Zhang

引用次数: 1

Steganalysis Using Unsupervised End-to-End CNN Fused with Residual Image 融合残差图像的无监督端到端CNN隐写分析

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.2018.8633234

Yao Wu, Hui Li, Junkai Yi

引用次数: 2