首页> 美国政府科技报告 >Graphical Geometric and Learning/Optimization-Based Methods in Statistical Signal and Image Processing Object Recognition and Data Fusion

【24h】

Graphical Geometric and Learning/Optimization-Based Methods in Statistical Signal and Image Processing Object Recognition and Data Fusion

机译：基于图形几何和学习/优化的统计信号和图像处理方法识别和数据融合方法

获取原文

页面导航

摘要
著录项
相似文献
相关主题

摘要

This report summarizes our accomplishments under this grant. Our research covers several interrelated areas: (a) the use of graphical, hierarchical, and multiresolution representations for the development of statistical modeling methodologies for complex phenomena and for the construction of scalable algorithms for the fusion of multiple heterogeneous sources of information; (b) the development of first principles methods for constructing statistical models for shapes and for the use of these models in developing robust and statistically optimal methods of shape estimation and recognition; and (c) the development of new statistical learning and optimization algorithms for feature extraction, signal and image restoration, and sensor fusion. Our research blends methods from several fields-statistics and probability, signal and image processing, mathematical physics, scientific computing, statistical learning theory, and differential geometry-to produce new approaches to emerging and challenging problems in signal and image processing, and each aspect of our program contains both fundamental research in mathematical sciences and important applications of direct relevance to Air Force missions. In particular, our research is relevant to automatic target recognition based on synthetic aperture radar and laser radar imagery; wide- area surveillance and information preparation of the battlefield; global awareness and higher-level fusion for situational assessment; and fusion of multiple heterogeneous sensors. In all of these areas we have contacts and interactions with AFRL staff and with industry involved in Air Force programs.

著录项

作者
Willsky, A. S.;
展开▼
作者单位

展开▼
年度 2008
页码 1-28
总页数 28
原文格式 PDF
正文语种 eng
中图分类工业技术;
关键词
Optical radar ; Image processing ; Geometric forms ; Statistical analysis ; Graphical user interface ; Data fusion ; Mathematical models ; Geographic areas ; Air force operations ; First principles calculations ; Sensor fusion ; Feature extraction ; Image restoration ; Area coverage ; Battlefields ; Target recognition ; Parallel processing ; Synthetic aperture radar ; Construction;

机译：光学雷达;图像处理;几何形式;统计分析;图形用户界面;数据融合;数学模型;地理区域;空军作战;第一原理计算;传感器融合;特征提取;图像恢复;区域覆盖;战场;目标识别;并行处理;合成孔径雷达;结构;

相似文献

外文文献
中文文献
专利

1. Quantification and characterization of microporosity by image processing, geometric measurement and statistical methods: Application on SEM images of clay materials1^ [J] . Chun Liu, Bin Shi, Jian Zhou, Applied clay science . 2011,第1期

机译：通过图像处理，几何测量和统计方法对微孔进行定量和表征：在粘土材料的SEM图像中的应用1 ^
2. A Deep Learning Fusion Recognition Method Based On SAR Image Data [J] . Zhai Jia, Dong Guangchang, Chen Feng, Procedia Computer Science . 2019,第22期

机译：基于SAR图像数据的深度学习融合识别方法
3. Visual Image Processing RAM: Memory Architecture With 2-D Data Location Search and Data Consistency Management for a Multicore Object Recognition Processor [J] . Circuits and Systems for Video Technology, IEEE Transactions on . 2010,第4期

机译：视觉图像处理RAM：具有多核对象识别处理器的2-D数据位置搜索和数据一致性管理的内存架构
4. Multi-resolution Object Detection and Data Fusion for Large-scale Remote Sensing Images based on Deep Learning Method [C] . Yingrui Wu, Hang Hu, Yong Zhang IEEE Advanced Information Management, Communicates, Electronic and Automation Control Conference . 2019

机译：基于深度学习方法的大规模遥感图像多分辨率目标检测与数据融合
5. Statistical signal processing approaches to image fusion. [D] . Yang, Jinzhong. 2007

机译：统计信号处理方法用于图像融合。
6. Lung nodule malignancy classification using only radiologist-quantified image features as inputs to statistical learning algorithms: probing the Lung Image Database Consortium dataset with two statistical learning methods [O] . Matthew C. Hancock, Jerry F. Magnan 2016

机译：仅使用放射科医生量化的图像特征作为统计学习算法的输入的肺结节恶性分类：使用两种统计学习方法探查肺图像数据库联盟数据集
7. Lung nodule malignancy classification using only radiologist-quantified image features as inputs to statistical learning algorithms: probing the Lung Image Database Consortium dataset with two statistical learning methods [O] . Matthew C. Hancock, Jerry F. Magnan 2016

机译：肺结结恶性分类使用仅放射科学定量的图像特征作为统计学习算法的输入：探测肺图像数据库联盟数据集，具有两个统计学习方法

Graphical Geometric and Learning/Optimization-Based Methods in Statistical Signal and Image Processing Object Recognition and Data Fusion

摘要

著录项

相似文献

相关主题

期刊订阅