Conference proceedings. IEEE International Conference on Signal and Image Processing Applications最新文献_第7页

Keynote 1: Joint image reconstruction: Advances in structure-coupled multi-physics data inversion in environmental and energy industries 主题演讲1:联合图像重建:环境和能源行业结构耦合多物理场数据反演的进展

Conference proceedings. IEEE International Conference on Signal and Image Processing Applications Pub Date : 2017-09-01 DOI: 10.1109/ICSIPA.2017.8120565

M. Meju

{"title":"Keynote 1: Joint image reconstruction: Advances in structure-coupled multi-physics data inversion in environmental and energy industries","authors":"M. Meju","doi":"10.1109/ICSIPA.2017.8120565","DOIUrl":"https://doi.org/10.1109/ICSIPA.2017.8120565","url":null,"abstract":"Joint reconstruction and multi-modality/multi-spectral imaging (or joint geophysical inversion) is of growing importance in a wide range of contemporary issues including cost-effective environmental and groundwater investigations, natural hazard monitoring, carbon dioxide sequestration and efficient prediction and extraction of fossil and renewable fuels. It is also emerging rapidly in biomedical and materials science imaging. It combines data acquired using different methods (or modalities) to provide more realistic images of the subject under investigation than achievable using an individual modality as now well-known in environmental and energy investigations. Combining observations of multiple physical phenomena on an object of investigation has potential for accurate predictions and hence risk reduction in decision making with data. In the environmental and energy industries, the challenge in this integrated imaging of the subsurface is how to combine large-volumes of correlated data from interrelated physical phenomena or disparate data from unrelated physical phenomena and taking into account the different support volumes of the data (due to the different spatial scales or foot-prints of measurement modalities). In this paper, I describe some important considerations for adequate sampling of subsurface targets and data homogenization (or pre-conditioning), which data sets and physical constraints are most important for the joint image reconstruction process to be successful, uncertainty analysis, and the recent advances in structure-coupled inverse modeling of spatio-temporal multiphysics observations in petroleum and environmental investigations.","PeriodicalId":92495,"journal":{"name":"Conference proceedings. IEEE International Conference on Signal and Image Processing Applications","volume":"1 1","pages":"vii"},"PeriodicalIF":0.0,"publicationDate":"2017-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"79761733","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

Keynote 3: Random forests for biomedical data classification 主题演讲3:生物医学数据分类的随机森林

Conference proceedings. IEEE International Conference on Signal and Image Processing Applications Pub Date : 2017-09-01 DOI: 10.1109/ICSIPA.2017.8120567

L. Heutte

{"title":"Keynote 3: Random forests for biomedical data classification","authors":"L. Heutte","doi":"10.1109/ICSIPA.2017.8120567","DOIUrl":"https://doi.org/10.1109/ICSIPA.2017.8120567","url":null,"abstract":"Learning robust machine models is still a challenging issue for classifying biomedical data. In order to deal with high dimensionality, low sample size, imbalanced classes, Random Forests (RF) have been widely adopted in this field. RF consists in building a classifier ensemble, with randomization to produce a diverse pool of tree-based classifiers. Since their introduction in 2001 by Leo Breiman, RF have been extensively studied, both theoretically and experimentally, and have shown competitive performance with state of the art classifiers. However, only a few studies have addressed the issues raised by the choice of the hyper-parameters and their influence on RF performance. This talk will first address our attempts to better understand and explain the performance of RF through their hyper-parameters that have led us to propose different variants of RF, namely Forest-RK and Dynamic Random Forests, to be less sensitive to the choice, sometimes critical on the generalization performance, of the parametrization. In a second part I will illustrate the use of RF on two medical applications: the classification of endomicroscopic images of the lungs and cancer stage/patient prediction with Radiomics, a domain which is increasingly attracting attention. When dealing with medical data, it might happen that only data of one class (eg healthy patient) is available for training. This is typically the case for endomicroscopic images of the lungs and we have proposed an original approach to deal with outliers in medical image classification, namely One Class Random Forests, which has shown to be effective for our problem and competitive with other state of the art one class classifiers. The second application of RF is Radiomics, a new (2012) concept which refers to the analysis of large amount of quantitative tumor features, extracted from multimodal medical images and other information like clinical data and gene or protein data to predict the patient's evolution and/or survival rate. In this case, data are both highly dimensional and heterogeneous. As part of an on going work, we have proposed a dissimilarity-based multi-view learning model with random forest, in which each data view (or group of features) is processed separately so that the data dimension is smaller in each view. By combining different views together, we can take advantage of the heterogeneity between views while avoiding using conventional feature selection methods for reducing the high dimensionality of data.","PeriodicalId":92495,"journal":{"name":"Conference proceedings. IEEE International Conference on Signal and Image Processing Applications","volume":"41 1","pages":"ix"},"PeriodicalIF":0.0,"publicationDate":"2017-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"85719201","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

Fast Autonomous Crater Detection by Image Analysis-For Unmanned Landing on Unknown Terrain 基于图像分析的快速自主陨石坑检测——面向未知地形的无人着陆

Conference proceedings. IEEE International Conference on Signal and Image Processing Applications Pub Date : 2016-05-30 DOI: 10.1007/978-3-319-33618-3_30

Payel Sadhukhan, S. Palit

引用次数: 0

Robust Print-cam Image Watermarking in Fourier Domain 傅里叶域鲁棒打印凸轮图像水印

Conference proceedings. IEEE International Conference on Signal and Image Processing Applications Pub Date : 2016-05-30 DOI: 10.1007/978-3-319-33618-3_36

Khadija Gourrame, H. Douzi, R. Harba, F. Ros, M. Hajji, Rabia Riad, Meina Amar

引用次数: 13

Speaker Discrimination Using Several Classifiers and a Relativistic Speaker Characterization 使用几种分类器和相对说话人特征的说话人识别

Conference proceedings. IEEE International Conference on Signal and Image Processing Applications Pub Date : 2016-05-30 DOI: 10.1007/978-3-319-33618-3_21

S. Ouamour, Z. Hamadache, H. Sayoud

引用次数: 2

Super-Resolved Enhancement of a Single Image and Its Application in Cardiac MRI 单幅图像的超分辨增强及其在心脏MRI中的应用

Conference proceedings. IEEE International Conference on Signal and Image Processing Applications Pub Date : 2016-05-30 DOI: 10.1007/978-3-319-33618-3_19

Guang Yang, Xujiong Ye, G. Slabaugh, J. Keegan, R. Mohiaddin, D. Firmin

引用次数: 2

Measuring Spectral Reflectance and 3D Shape Using Multi-primary Image Projector 利用多主图像投影仪测量光谱反射率和三维形状

Conference proceedings. IEEE International Conference on Signal and Image Processing Applications Pub Date : 2016-05-30 DOI: 10.1007/978-3-319-33618-3_15

K. Hirai, Ryosuke Nakahata, T. Horiuchi

引用次数: 7

Leaf Classification Using Convexity Measure of Polygons 基于多边形凸度测度的叶片分类

Conference proceedings. IEEE International Conference on Signal and Image Processing Applications Pub Date : 2016-05-30 DOI: 10.1007/978-3-319-33618-3_6

J. R. Kala, Serestina Viriri, Deshendran Moodley, J. Tapamo

引用次数: 2

A Chaotic Cryptosystem for Color Image with Dynamic Look-Up Table 一种带动态查找表的彩色图像混沌密码系统

Conference proceedings. IEEE International Conference on Signal and Image Processing Applications Pub Date : 2016-05-30 DOI: 10.1007/978-3-319-33618-3_10

M. Abdmouleh, A. Khalfallah, M. Bouhlel

引用次数: 3

Feature Extraction Based on Bandpass Filtering for Frog Call Classification 基于带通滤波的特征提取蛙叫声分类

Conference proceedings. IEEE International Conference on Signal and Image Processing Applications Pub Date : 2016-05-30 DOI: 10.1007/978-3-319-33618-3_24

Jie Xie, M. Towsey, L. Zhang, Jinglan Zhang, P. Roe

引用次数: 5