2017 IEEE International Conference on Imaging Systems and Techniques (IST)最新文献_第5页

Train wheel tread defects detection based on image registration 基于图像配准的列车车轮踏面缺陷检测

2017 IEEE International Conference on Imaging Systems and Techniques (IST) Pub Date : 2017-10-01 DOI: 10.1109/IST.2017.8261509

S. Lv, Fuqiang Zhou, Zhenzhong Wei

引用次数: 6

Polarimetric SAR oil spill detection based on deep networks 基于深度网络的极化SAR溢油检测

2017 IEEE International Conference on Imaging Systems and Techniques (IST) Pub Date : 2017-10-01 DOI: 10.1109/IST.2017.8261559

Guandong Chen, Yu Li, Guangmin Sun, Yuanzhi Zhang

引用次数: 7

Capabilities of code division multiplexed electrical impedance tomography 码分多路电阻抗层析成像的能力

2017 IEEE International Conference on Imaging Systems and Techniques (IST) Pub Date : 2017-10-01 DOI: 10.1109/IST.2017.8261478

M. Tsoeu, M. Inggs

引用次数: 0

Face recognition based on geodesic distance approximations between multivariate normal distributions 基于多变量正态分布之间测地线距离逼近的人脸识别

2017 IEEE International Conference on Imaging Systems and Techniques (IST) Pub Date : 2017-10-01 DOI: 10.1109/IST.2017.8261567

J. Soldera, Kit Dodson, J. Scharcanski

{"title":"Face recognition based on geodesic distance approximations between multivariate normal distributions","authors":"J. Soldera, Kit Dodson, J. Scharcanski","doi":"10.1109/IST.2017.8261567","DOIUrl":"https://doi.org/10.1109/IST.2017.8261567","url":null,"abstract":"We propose a novel generative approach for face recognition, in which sparse facial features are extracted from high resolution color face images using predefined landmark topologies which mark discriminative locations on face images, unlike the appearance-based approach, in which low resolution grayscale face images are used, reducing the computational complexity. By adopting a common landmark topology, the dissimilarity between distinct face images can be scored in terms of the dissimilarities between their corresponding landmarks, which are obtained by proposed geodesic distance approximations between multivariate normal distributions which represent the color intensities in the vicinities of each landmark location. The classification process of new face samples occurs by the determination of the face image sample present in the training set which minimizes the dissimilarity score. The proposed method was compared with representative current state-of-the-art methods using color or grayscale face images and presented the higher recognition rates. Moreover, these results also support a trend in which color information is relevant in face recognition.","PeriodicalId":187213,"journal":{"name":"2017 IEEE International Conference on Imaging Systems and Techniques (IST)","volume":"27 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":"127585500","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

Convolutional neural network and adaptive guided image filter based stereo matching 基于卷积神经网络和自适应引导图像滤波的立体匹配

2017 IEEE International Conference on Imaging Systems and Techniques (IST) Pub Date : 2017-10-01 DOI: 10.1109/IST.2017.8261530

Sihan Wen

引用次数: 2

An image reconstruction method for improving resolution of capacitive wire mesh tomography 一种提高电容式钢丝层析成像分辨率的图像重建方法

2017 IEEE International Conference on Imaging Systems and Techniques (IST) Pub Date : 2017-10-01 DOI: 10.1109/IST.2017.8261493

Weimin Lao, Lihui Peng, Yi Li

引用次数: 1

Imaging and detection of cracks based on a multi-frequency electromagnetic scanning instrument and SVM 基于多频电磁扫描仪和支持向量机的裂纹成像与检测

2017 IEEE International Conference on Imaging Systems and Techniques (IST) Pub Date : 2017-10-01 DOI: 10.1109/IST.2017.8261512

Liyuan Yin, Jiande Wu, Bo Ye, J. S. Avila, Hanyang Xu, Kin Yau How, Yang Tao, W. Yin

引用次数: 3

Effect of media permittivity on imaging quality of electrical capacitance tomography 介质介电常数对电容层析成像质量的影响

2017 IEEE International Conference on Imaging Systems and Techniques (IST) Pub Date : 2017-10-01 DOI: 10.1109/IST.2017.8261446

Shiguo Liang, Jiamin Ye, Haigang Wang, Meng Wu

引用次数: 1

A depth estimation algorithm of plenoptic camera for the measurement of particles 一种用于粒子测量的全光相机深度估计算法

2017 IEEE International Conference on Imaging Systems and Techniques (IST) Pub Date : 2017-10-01 DOI: 10.1109/IST.2017.8261484

Yuanchi Ma, Wu Zhou, T. Qian, Xiaoshu Cai

引用次数: 3

Sparse inverse covariance network-based modeling for mild cognitive impairment classification 基于稀疏反协方差网络的轻度认知障碍分类模型

2017 IEEE International Conference on Imaging Systems and Techniques (IST) Pub Date : 2017-10-01 DOI: 10.1109/IST.2017.8261535

Jie Huang, Yang Li

{"title":"Sparse inverse covariance network-based modeling for mild cognitive impairment classification","authors":"Jie Huang, Yang Li","doi":"10.1109/IST.2017.8261535","DOIUrl":"https://doi.org/10.1109/IST.2017.8261535","url":null,"abstract":"Recent advances in neuroimaging techniques have provided great potential for studying mild cognitive impairments (MCI). Brain function connectivity networks constructed from resting-state functional magnetic resonance imaging (rs-fMRI), have been widely used for classifying MCI from normal controls (NC). In this paper, a sparse representation-based method called sparse inverse covariance estimation (SICE) constructs functional brain connectivity and distinguishes MCI patients from NC. One limitation of SICE methodology is its sensitivity to regularization parameters. To address this issue, a nested-cross validation framework was added to tune the regularization parameter through the machine learning process. After acquiring the optimal regularization parameter, a two-stage feature selection approach is performed on the brain network-based feature matrix to select the most discriminative features. Next, a linear support vector machine (SVM) classifier is trained for classification using these selected optimal features. The experiment results indicated a cross-validation accuracy of 91.89% with a sensitivity of 83.3%, and a specificity of 96%. The positive results illustrate the excellent diagnostic power of the SICE method. The proposed method found comparative differences between brain regions in MCI patients versus NC patients, which is consistent with findings in reported literatures.","PeriodicalId":187213,"journal":{"name":"2017 IEEE International Conference on Imaging Systems and Techniques (IST)","volume":"5 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":"132610570","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