2017 IEEE International Conference on Imaging Systems and Techniques (IST)最新文献_第4页

A tissue classification approach for brain tumor segmentation using MRI 脑肿瘤MRI分割的组织分类方法

2017 IEEE International Conference on Imaging Systems and Techniques (IST) Pub Date : 2017-10-01 DOI: 10.1109/IST.2017.8261542

V. Pezoulas, M. Zervakis, I. Pologiorgi, Stavros Seferlis, Georgios M. Tsalikis, G. Zarifis, G. Giakos

引用次数: 11

Homogeneity patch search method for efficient vehicle color classification using front-of-vehicle image 基于前车图像的同质斑搜索车辆颜色分类方法

2017 IEEE International Conference on Imaging Systems and Techniques (IST) Pub Date : 2017-10-01 DOI: 10.1109/IST.2017.8261544

Y. Jeong, Kil-Houm Park, Dae-Geun Park

{"title":"Homogeneity patch search method for efficient vehicle color classification using front-of-vehicle image","authors":"Y. Jeong, Kil-Houm Park, Dae-Geun Park","doi":"10.1109/IST.2017.8261544","DOIUrl":"https://doi.org/10.1109/IST.2017.8261544","url":null,"abstract":"As one of the important features of a vehicle, color features are very discernible to classify the vehicle color using a front-of-the-vehicle image acquired from CCTV. The color identification of vehicles plays a significant role tracking crime vehicles in imaging systems based on CCTV. In this paper, we newly define a color homogeneity patch and propose its searching method. The proposed method focuses on reducing a intra-class variance by avoiding another color distribution, not a vehicle color, which is included in vehicle color features. In addition we could effectively improve this method based on a voting strategy. A region of interest (ROI) including bonnet is detected by predefined information about the given car and the search method is applied to ROI for selecting color homogeneity patches. The proposed approach extracts HSV histogram for each patch, and adopts the multi-adaboost algorithm to classify the color of each patches. We integrate the proposed method with the voting strategy to determine the color of the vehicle. For the validation of the feasibility of our approach, we compare the result with just a sliding window without consideration of homogeneity. Experiment results show that homogeneity patches search method can be efficiently applied to recognize vehicle color.","PeriodicalId":187213,"journal":{"name":"2017 IEEE International Conference on Imaging Systems and Techniques (IST)","volume":"15 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130941977","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

Digital image correlation using ring template for 3D reconstruction 利用环形模板进行数字图像相关三维重建

2017 IEEE International Conference on Imaging Systems and Techniques (IST) Pub Date : 2017-10-01 DOI: 10.1109/IST.2017.8261470

Anqi Huang, Yonghong Wang, Xizuo Dan, Yue Hu, Siyuan Bao

{"title":"Digital image correlation using ring template for 3D reconstruction","authors":"Anqi Huang, Yonghong Wang, Xizuo Dan, Yue Hu, Siyuan Bao","doi":"10.1109/IST.2017.8261470","DOIUrl":"https://doi.org/10.1109/IST.2017.8261470","url":null,"abstract":"Three-dimensional reconstruction is one of the most important applications of digital image correlation technology. At present, three-dimensional reconstruction of complex surface is completed by matching images caught by binocular vision measurement system. When three-dimensional objects are projected onto a two-dimensional plane image, various image factors are integrated into single gray level information. In terms of binocular vision measurement system, the images are caught by two cameras at the same time. Because of the different illumination and the stationary angle between two cameras, there might be holistic gray value differences between images caught by two cameras. These differences will affect both image matching precision and final result. In this paper, an improved binocular matching algorithm based on ring template and Newton-Raphson iteration methods is applied. To satisfy the partial convergence of Newton-Raphson iteration method, a ring template is chosen to match the integer pixel location of nodal points. After that, the integer coordinates of nodal points are already estimated which are taken as the initial value of Newton-Raphson iteration method. The morphology reconstruction of complicated surface can be implemented successfully by using algorithm mentioned in this paper. The algorithm can not only reduce the error caused by holistic gray value differences, but also improve the accuracy of reconstruction.","PeriodicalId":187213,"journal":{"name":"2017 IEEE International Conference on Imaging Systems and Techniques (IST)","volume":"23 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126907696","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

Construction of sensitivity matrix involving location information for ultrasound modulated electrical impedance tomography 超声调制电阻抗层析成像中包含位置信息的灵敏度矩阵的构建

2017 IEEE International Conference on Imaging Systems and Techniques (IST) Pub Date : 2017-10-01 DOI: 10.1109/IST.2017.8261556

Shengnan Zhang, Yanbin Xu, F. Dong

引用次数: 1

Design of temperature compensation for silicon-sapphire pressure sensor 硅蓝宝石压力传感器的温度补偿设计

2017 IEEE International Conference on Imaging Systems and Techniques (IST) Pub Date : 2017-10-01 DOI: 10.1109/IST.2017.8261481

Huang Manguo, L. Defeng, Lu Chao, Guo Zhanshe, Li Xin

引用次数: 1

Remote sensing image registration based on feature points of global edge 基于全局边缘特征点的遥感图像配准

2017 IEEE International Conference on Imaging Systems and Techniques (IST) Pub Date : 2017-10-01 DOI: 10.1109/IST.2017.8261564

Siying Liu, Jie Jiang

引用次数: 2

Classification of cataract fundus image based on deep learning 基于深度学习的白内障眼底图像分类

2017 IEEE International Conference on Imaging Systems and Techniques (IST) Pub Date : 2017-10-01 DOI: 10.1109/IST.2017.8261463

Yanyan Dong, Qinyan Zhang, Zhiqiang Qiao, Jijiang Yang

{"title":"Classification of cataract fundus image based on deep learning","authors":"Yanyan Dong, Qinyan Zhang, Zhiqiang Qiao, Jijiang Yang","doi":"10.1109/IST.2017.8261463","DOIUrl":"https://doi.org/10.1109/IST.2017.8261463","url":null,"abstract":"Cataract is a dulling or clouding of the lens inside the eye. Which is one of the most common diseases that might cause blindness. Considering the damage impact of cataract, we propose to use computer science for automatic cataract detection, which is based on the classification of retinal image. This method focuses on the feature extraction step of retinal image. Firstly, the maximum entropy method is used to preprocess the fundus images. Next, we use deep learning network which is based on Caffe to automatically extract more distinctive features of fundus images. Last, several representative classification algorithms are used to identify automatically extracted features. Comparing to features extracted by deep learning and wavelet feature extracted from retinal vascular, SVM(support vector machines) and Softmax are usedfor cataract classification. Finally, cataract images are classified into normal, slight, medium or severe four-class. Through comparing with the results of classification, the feature extracted from deep learning which is classified by Softmax get better accuracy. The results demonstrate that our research on deep learning is effective and has practical value.","PeriodicalId":187213,"journal":{"name":"2017 IEEE International Conference on Imaging Systems and Techniques (IST)","volume":"15 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131324533","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: 63

Dynamic path planning algorithm for wearable visual navigation system based on the improved A𢈗基于改进A的可穿戴视觉导航系统动态路径规划算法𢈗

2017 IEEE International Conference on Imaging Systems and Techniques (IST) Pub Date : 2017-10-01 DOI: 10.1109/IST.2017.8261468

Yidou Zhang, Yan Zhao, Tong Wei, Jiemin Chen

引用次数: 1

Sparse reconstruction-based SAR tomography and it's application 基于稀疏重建的SAR层析成像及其应用

2017 IEEE International Conference on Imaging Systems and Techniques (IST) Pub Date : 2017-10-01 DOI: 10.1109/IST.2017.8261514

Xinwu Li, Xing Peng, L. Liang

引用次数: 0

Comparison of two approaches for land cover classification from ICESat/GLAS waveform data 基于ICESat/GLAS波形数据的两种土地覆盖分类方法的比较

2017 IEEE International Conference on Imaging Systems and Techniques (IST) Pub Date : 2017-10-01 DOI: 10.1109/IST.2017.8261452

Qunchong Zhao, Xiaolu Li, Lijun Xu

{"title":"Comparison of two approaches for land cover classification from ICESat/GLAS waveform data","authors":"Qunchong Zhao, Xiaolu Li, Lijun Xu","doi":"10.1109/IST.2017.8261452","DOIUrl":"https://doi.org/10.1109/IST.2017.8261452","url":null,"abstract":"The Ice, Cloud, and land Elevation Satellite/Geoscience Laser Altimeter System (ICESat/GLAS) as a full-waveform satellite LiDAR enables land cover classification estimation. Although it has already been retired, its successor satellites will provide valuable information in climate change and icecap glaciology studies. In order to study the way of waveform-based land cover classification using machine learning methods, we utilized decision tree (DT) and Gaussian process (GP) methods to analyze the accuracy of land cover classification. DT classification is a convenient and practical method used in previous reports. GP classification can provide the state-of-the-art recognition performance using an elegant Bayesian framework. To generate the true labels of machine learning classifiers, a solution of the time-matching and location-matching between ICESat/GLAS laser footprints and LANDSAT images has been achieved. Plenty of experiments have been implemented in the Jakobshavn Glacier by evaluating classifiers features selection, training data ratio and classification confusion matrix. Experimental results show that the overall accuracy of the GP classification is solidly higher than DT classification but GP classification consumes more time. At the best training data ratio of 70%, GP classification accuracy is 92.22% which is higher than DT classification accuracy of 87.78%.","PeriodicalId":187213,"journal":{"name":"2017 IEEE International Conference on Imaging Systems and Techniques (IST)","volume":"46 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"117053250","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