2017 IEEE International Conference on Imaging Systems and Techniques (IST)最新文献

HEVC based mixed-resolution stereo video coding for low bitrate transmission 基于HEVC的低比特率混合分辨率立体声视频编码

2017 IEEE International Conference on Imaging Systems and Techniques (IST) Pub Date : 2017-10-18 DOI: 10.1109/IST.2017.8261495

Bruhanth Mallik, A. S. Akbari, A. Kor

引用次数: 1

Thoracic impedance measurement for lung function evaluation 胸阻抗测量用于肺功能评估

2017 IEEE International Conference on Imaging Systems and Techniques (IST) Pub Date : 2017-10-01 DOI: 10.1109/IST.2017.8261534

Hua Li, Hang Xu, Yixin Ma, Hui Qin, Peng Qin, X. Ji, Tao Han, P. Cai

引用次数: 3

SAR imaging of thick composite structures using circular aperture probe 厚复合材料结构的圆孔径探头SAR成像

2017 IEEE International Conference on Imaging Systems and Techniques (IST) Pub Date : 2017-10-01 DOI: 10.1109/IST.2017.8261517

Xie Xingyu, Mohammed Saif ur Rahman, M. Abou-Khousa

{"title":"SAR imaging of thick composite structures using circular aperture probe","authors":"Xie Xingyu, Mohammed Saif ur Rahman, M. Abou-Khousa","doi":"10.1109/IST.2017.8261517","DOIUrl":"https://doi.org/10.1109/IST.2017.8261517","url":null,"abstract":"Composite materials are commonly utilized in the manufacturing of many critical aerospace components. While these composites exhibit competitive properties compared to other materials, they are prone to subsurface defects which could compromise their structural integrity and potentially lead to catastrophic failures. Therefore, monitoring the integrity of these composites during their lifecycle becomes of paramount importance. Microwave and millimeter wave non-destructive testing techniques have proven to be effective in inspection of these materials. However, inspecting thick composite structures remains a challenge for conventional near-field non-destructive testing methods. In this paper, a Teflon-loaded circular waveguide operating from 22 GHz to 26 GHz is used to produce synthetically focused image of thick five-layered dielectric composite structure. Additionally, the same composite structure is also imaged by a conventional K-band rectangular waveguide and the obtained image is compared to the one produced using the circular waveguide. Finally, the synthetically focused images are compared with the images obtained using near-field imaging. It is demonstrated that the circular waveguide produces better quality images with clear indications of the defects in the composite sample compared to the rectangular waveguide.","PeriodicalId":187213,"journal":{"name":"2017 IEEE International Conference on Imaging Systems and Techniques (IST)","volume":"72 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":"127155742","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

Variable parameter estimation of SAR signal based on compression sensing 基于压缩感知的SAR信号变参数估计

2017 IEEE International Conference on Imaging Systems and Techniques (IST) Pub Date : 2017-10-01 DOI: 10.1109/IST.2017.8261558

Shuai Gao, Huaping Xu, Xue Qiu, Bo Yang

{"title":"Variable parameter estimation of SAR signal based on compression sensing","authors":"Shuai Gao, Huaping Xu, Xue Qiu, Bo Yang","doi":"10.1109/IST.2017.8261558","DOIUrl":"https://doi.org/10.1109/IST.2017.8261558","url":null,"abstract":"The linear frequency modulation (LFM) signal is widely used in the radar field because of its large time-bandwidth product and low intercept rate. It is very valuable to estimate the parameters of the LFM signal in the practical applications. With the increasing requirements for radar system, the radar signal parameters are no longer constant, and may change in accordance with a certain law. Then the traditional parameter estimation methods cannot attain an accurate result for a radar signal with variable parameters. In this paper, an estimation of variable parameter SAR signal based on compression sensing (CS) is proposed. The parameter value of every pulse is obtained with the existed parameter estimation method firstly. The law of parameter change with time is supposed to be a polynomial, then we take into account the sparsity of polynomial coefficients and use the compression sensing theory to reconstruct the polynomial coefficient vector denoting the change of the parameter. After introducing the theory of compression sensing, the timing characteristics of SAR signal are analyzed. The validity of the proposed method is verified by theoretical analysis and simulation. Especially for SAR signal with few pulses, this method can be used in the absence of a priori information and restore the variable parameters of the SAR signal ith higher accuracy.","PeriodicalId":187213,"journal":{"name":"2017 IEEE International Conference on Imaging Systems and Techniques (IST)","volume":"86 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":"127355360","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

Combining convolutional and recurrent neural networks for Alzheimer's disease diagnosis using PET images 结合卷积和递归神经网络用于PET图像诊断阿尔茨海默病

2017 IEEE International Conference on Imaging Systems and Techniques (IST) Pub Date : 2017-10-01 DOI: 10.1109/IST.2017.8261461

D. Cheng, Manhua Liu

{"title":"Combining convolutional and recurrent neural networks for Alzheimer's disease diagnosis using PET images","authors":"D. Cheng, Manhua Liu","doi":"10.1109/IST.2017.8261461","DOIUrl":"https://doi.org/10.1109/IST.2017.8261461","url":null,"abstract":"Alzheimer's disease (AD) is a progressive and irreversible brain degenerative disorder which often happens in people aged more than 65 years old. Accurate and early diagnosis of AD is vital for the patient care and development of future treatment. Positrons Emission Tomography (PET) is a functional molecular imaging modality, which proves to be a powerful tool to help understand the brain changes related to AD. Most existing methods extract the handcraft features from images, and then train a classifier to distinguish AD from other groups. The success of these computer-aided diagnosis methods highly depends on the image preprocessing, including rigid registration and segmentation. Motivated by the success of deep learning in image classification, this paper proposes a new classification framework based on combination of 2D convolutional neural networks (CNN) and recurrent neural networks (RNN), which learns the features of 3D PET images by decomposing the 3D image into a sequence of 2D slices. In this framework, the hierarchical 2D CNNs are built to capture the intra-slice features while the gated recurrent unit (GRU) of RNN is used to extract the inter-slice features for final classification. No rigid image registration and segmentation are required for PET images. Our method is evaluated on the baseline PET images from 339 subjects including 93 AD patients, 146 mild cognitive impairments (MCI) and 100 normal controls (NC) from Alzheimer's Disease Neuroimaging Initiative (ADNI) database. Experimental results show that the proposed method achieves an AUC of 95.28% for classification of AD vs. NC and 83.90% for classification of MCI vs. NC, respectively, demonstrating the promising classification performance.","PeriodicalId":187213,"journal":{"name":"2017 IEEE International Conference on Imaging Systems and Techniques (IST)","volume":"74 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":"127379872","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: 38

Analysis of pool characteristics and correlation using auxiliary optical imaging during high power laser welding 大功率激光焊接熔池特性及其相关性的辅助光学成像分析

2017 IEEE International Conference on Imaging Systems and Techniques (IST) Pub Date : 2017-10-01 DOI: 10.1109/IST.2017.8261498

Guiqian Liu, Xiangdong Gao, D. You, Yaowu Song, Nanfeng Zhang

引用次数: 1

Feature extraction of lung ventilation by biomedical electrical impedance tomography 生物医学电阻抗断层扫描肺通气特征提取

2017 IEEE International Conference on Imaging Systems and Techniques (IST) Pub Date : 2017-10-01 DOI: 10.1109/IST.2017.8261546

Xiaoyan Chen, Xiaomin Chang

引用次数: 1

A comparison of feature detection in low atmospheric pressure using thermal and visible images 利用热图像和可见光图像进行低气压特征检测的比较

2017 IEEE International Conference on Imaging Systems and Techniques (IST) Pub Date : 2017-10-01 DOI: 10.1109/IST.2017.8261501

H. Sharif, Christian Pfaab, Matthew S. Hölzel

引用次数: 0

Visual inspection of pulsed Nd: YAG laser welding of PMMA and stainless steel 304 脉冲Nd: YAG激光焊接PMMA与304不锈钢的目视检测

2017 IEEE International Conference on Imaging Systems and Techniques (IST) Pub Date : 2017-10-01 DOI: 10.1109/IST.2017.8261528

Yijie Huang, Xiangdong Gao, Yaowu Song, Nanfeng Zhang, Nvjie Ma

引用次数: 2

Real-time stereo vision for collision detection on autonomous UAVs 自主无人机碰撞检测的实时立体视觉

2017 IEEE International Conference on Imaging Systems and Techniques (IST) Pub Date : 2017-10-01 DOI: 10.1109/IST.2017.8261524

Aleksandar Stanoev, Nicolas Audinet, S. Tancock, N. Dahnoun

引用次数: 5