2017 10th International Congress on Image and Signal Processing, BioMedical Engineering and Informatics (CISP-BMEI)最新文献_第2页

The Research of Electronic Countermeasure Intelligence Correlation Analysis Based on Machine Learning 基于机器学习的电子对抗智能关联分析研究

2017 10th International Congress on Image and Signal Processing, BioMedical Engineering and Informatics (CISP-BMEI) Pub Date : 2018-01-01 DOI: 10.1109/CISP-BMEI.2018.8633088

Ziyan Shi, Guolin Zhao, Qiaolin Hu

引用次数: 0

Measurements of rainfall rates from videos 从视频中测量降雨量

2017 10th International Congress on Image and Signal Processing, BioMedical Engineering and Informatics (CISP-BMEI) Pub Date : 2017-10-16 DOI: 10.1109/CISP-BMEI.2017.8302066

Rong Dong, Juan Liao, Bo Li, Huiyu Zhou, D. Crookes

引用次数: 10

Mind interactive multimedia system for disabled people 残疾人心灵互动多媒体系统

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.2017.8302322

F. Jabeen, L. Tao, Xinyue Wang

引用次数: 2

A study of historical documents denoising 历史文献去噪研究

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.2017.8301947

Guoming Chen, Qiang Chen, Xiongyong Zhu, Yiqun Chen

引用次数: 4

Segmentation of GBM in MRI images using an efficient speed function based on level set method 基于水平集的高效速度函数分割MRI图像中GBM

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.2017.8301983

Alireza Mojtabavi, P. Farnia, A. Ahmadian, M. Alimohamadi, Ahmad Pourrashidi, H. S. Rad, J. Alirezaie

{"title":"Segmentation of GBM in MRI images using an efficient speed function based on level set method","authors":"Alireza Mojtabavi, P. Farnia, A. Ahmadian, M. Alimohamadi, Ahmad Pourrashidi, H. S. Rad, J. Alirezaie","doi":"10.1109/CISP-BMEI.2017.8301983","DOIUrl":"https://doi.org/10.1109/CISP-BMEI.2017.8301983","url":null,"abstract":"Accurate segmentation and characterization of abnormalities in brain tumor are challenging task, especially in the case of GBM tumors, where the ambiguities presented in the boundaries of these tumors necessitates using efficient segmentation method. Level set methods have proven to be a flexible and powerful tool for image segmentation because of being shape-driven method with a properly defined speed function to grow or shrink the boundaries to segment complex objects of interest, precisely. In this study a combined level set algorithm consists of both region and boundary terms for GBM segmentation is proposed. The modified speed function incorporates threshold based level set and the Laplacian filter to highlight the fine details for performing an accurate extraction of the tumor region using multiple seed points selected by the user. An evaluation was performed on a dataset containing 6 patients with GBM by using three measures Dice, false positive error (FPE) and false negative error (FNE). Manual segmentation of GBM is considered as gold standard. Compared to traditional method, the mean of FPE and FNE are improved by 53.5% and 53.1%, respectively. The mean of Dice coefficients between our results and gold standard measurement reached to 0.88. As the results proved, the proposed combined method improves the accuracy of GBM segmentation by 16% compared to conventional level set method with threshold based speed function. Our method is also robust to change of parameters.","PeriodicalId":6474,"journal":{"name":"2017 10th International Congress on Image and Signal Processing, BioMedical Engineering and Informatics (CISP-BMEI)","volume":"96 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":"74866332","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

Convolutional neural network on neural compute stick for voxelized point-clouds classification 基于神经计算棒的卷积神经网络体素化点云分类

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.2017.8302078

Xiaofang Xu, Joao Amaro, Sam Caulfield, A. Forembski, G. Falcão, D. Moloney

{"title":"Convolutional neural network on neural compute stick for voxelized point-clouds classification","authors":"Xiaofang Xu, Joao Amaro, Sam Caulfield, A. Forembski, G. Falcão, D. Moloney","doi":"10.1109/CISP-BMEI.2017.8302078","DOIUrl":"https://doi.org/10.1109/CISP-BMEI.2017.8302078","url":null,"abstract":"2D Convolutional Neural Networks (CNNs) have enjoyed a surge in popularity over the last few years, mainly because they outperform traditional algorithms/methods in a myriad of computer vision (and other fields) tasks. On the other hand, the problem becomes more complex when dealing with 3D volumes. Lack of readily available training data, memory and computational requirements are just some of the factors hindering the progress of 3D CNNs. We propose a synthetic 3D voxelized point-clouds generation method containing object and scene in this paper. Furthermore, an efficient 3D volumetric representation called VOLA is applied. VOLA (Volumetric Accelerator) is a sexaquaternary (power-of-four subdivision) tree-based representation which aims to save significant memory for volumetric data. After training the model, it was deployed onto Movidius Neural Compute Stick which is a USB, containing a low-power processing unit as well as dedicated CNN hardware blocks. The trained model on NCS takes only ∼ 90 frames per second to perform inference on each 3D volume, with an average power consumption of 1.2W.","PeriodicalId":6474,"journal":{"name":"2017 10th International Congress on Image and Signal Processing, BioMedical Engineering and Informatics (CISP-BMEI)","volume":"895 1","pages":"1-7"},"PeriodicalIF":0.0,"publicationDate":"2017-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"75457016","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: 10

Real time target tracking based on nonlinear mean shift and particle filters 基于非线性均值漂移和粒子滤波的实时目标跟踪

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.2017.8301909

Zhenghua Shu, Guodong Liu, Zhihua Xie, Z. Ren

{"title":"Real time target tracking based on nonlinear mean shift and particle filters","authors":"Zhenghua Shu, Guodong Liu, Zhihua Xie, Z. Ren","doi":"10.1109/CISP-BMEI.2017.8301909","DOIUrl":"https://doi.org/10.1109/CISP-BMEI.2017.8301909","url":null,"abstract":"In radar tracking guidance, intelligent video surveillance, robot vision system, the parameters of position and velocity and steering state often need to get the target of interest, based on the motion characteristics of the target and further to control it. The filtering method is used to estimate the desired state parameters based on the functional relationship between the measured values and the state variables. This method is also called target tracking technique. At present, there are many target tracking technologies for different systems, but there is a big gap between the robustness and real-time requirements of the actual system. In order to solve the problem of large computation and bad real-time performance of Particle Filters, a real-time target tracking algorithm based on nonlinear mean shift and Particle Filters is proposed. The distribution of particles is closer to the actual posterior distribution by selecting the important probability density function. Furthermore, the nonlinear mean shift algorithm is integrated into the Particle Filters, so that the particles are further clustered into the real distribution. Finally, the algorithm is applied in the traffic video surveillance, and the effective tracking of the target motorcycle and vehicle is realized.","PeriodicalId":6474,"journal":{"name":"2017 10th International Congress on Image and Signal Processing, BioMedical Engineering and Informatics (CISP-BMEI)","volume":"8 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":"75563104","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

Deciduous broadleaf forests green FPAR and its relationship with spectral vegetation indices 落叶阔叶林绿色FPAR及其与光谱植被指数的关系

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.2017.8302028

S. Liang, Xuehui Hou, X. Sui, M. Wang

{"title":"Deciduous broadleaf forests green FPAR and its relationship with spectral vegetation indices","authors":"S. Liang, Xuehui Hou, X. Sui, M. Wang","doi":"10.1109/CISP-BMEI.2017.8302028","DOIUrl":"https://doi.org/10.1109/CISP-BMEI.2017.8302028","url":null,"abstract":"The fraction of absorbed photosynthetically active radiation (FPAR) is directly related to the primary productivity of photosynthesis, and is widely used to estimate ecosystem primary production. The forest canopy can be divided into photosynthetically active vegetation and non-photosynthetic vegetation according to their photosynthetical function. In this study, the scattering by arbitrary inclined leaves (SAIL) model was used to partition the PAR absorbed by canopy components into two parts: PAR absorbed by PAV and PAR by NPV. The characteristics of green FPAR (the fraction of PAR absorbed by PAV) and the relationships between green FPAR and spectral vegetation indices (NDVI, EVI, EVI2, SAVI) were analyzed. The results showed that the green FPAR varied with the canopy structure. In broadleaf deciduous forests with high coverage, the green FPAR was close to the total FPAR, and the contribution of NPV to the total FPAR was very low. Plant area index had more important impacts on the green FPAR than the proportion of PAV in the canopy and optical properties of PAV. The green FPAR had significant relationships with four spectral vegetation indices, but the correlation coefficient between green FPAR and EVI was the largest. Therefore, compared with other three vegetation indices, EVI may be more suitable to estimate forest green FPAR.","PeriodicalId":6474,"journal":{"name":"2017 10th International Congress on Image and Signal Processing, BioMedical Engineering and Informatics (CISP-BMEI)","volume":"30 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":"74041626","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

The role of the cooccurrence matrix based on complex extended microstructures in discovering the cirrhosis severity grades within US images 基于复杂扩展显微结构的并发矩阵在超声图像中肝硬化严重程度分级中的作用

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.2017.8302018

D. Mitrea, S. Nedevschi, P. Mitrea, M. Platon-Lupsor, R. Badea

引用次数: 1

An improved algorithm based on convolution dynamic multi-parameter template for microaneurysms detection 基于卷积动态多参数模板的微动脉瘤检测改进算法

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.2017.8302045

Shan Ding, Li Xin

引用次数: 1