Collaborative Research: Accurate, Efficient and Robust Computational Algorithms for Detecting Changes in a Scene Given Indirect Data

协作研究:准确、高效和稳健的计算算法,用于检测给定间接数据的场景变化

基本信息

  • 批准号:
    1939203
  • 负责人:
  • 金额:
    $ 14万
  • 依托单位:
  • 依托单位国家:
    美国
  • 项目类别:
    Standard Grant
  • 财政年份:
    2019
  • 资助国家:
    美国
  • 起止时间:
    2019-08-01 至 2023-07-31
  • 项目状态:
    已结题

项目摘要

Detecting change from a temporal sequence of collected data is important in a wide variety of applications, including speech recognition, medical monitoring, credit card fraud detection, automated target recognition, and video surveillance. In applications such as medical monitoring, it is very important to find where the change occurs. In other applications, such as video surveillance, the type of change, e.g. the movement or insertion/deletion of an object of interest, is also critical. While detecting such changes from direct data (e.g. images already formed) has been well studied, there are many applications, such as magnetic resonance imaging (MRI), ultrasound, and synthetic aperture radar (SAR) where the temporal sequence of data are acquired indirectly. The typical approach to detecting changes in these applications would be to first form the image or signal of interest. As a consequence, information that is stored in the indirect data that may be valuable to detecting change is often lost. Therefore, this project seeks to develop accurate, efficient, and robust computational algorithms for detecting changes in a signal or image from a given temporal sequence of indirect data without first reconstructing the signal or image of interest. Additionally, the project seeks to incorporate the change information to develop better image and signal reconstruction algorithms. Both graduate and undergraduate students will be involved in the research investigations to enhance their career preparation in science and engineering. The participants will apply these new techniques on publicly available data sets, notably obtained for MRI, ultrasound, and SAR applications. The PIs will employ tools in frame theory, optimization, and statistics to develop and rigorously analyze new change detection and image/signal recovery algorithms. Specifically, the PIs will address the following technical issues in the proposed work: (1) the incorporation of prior information with appropriate mathematical/statistical formulation in the model; (2) the extraction of rotation/translation of an object from a sequence of indirect data; (3) model parameters tuning through statistical analysis; (4) the employment of intra- and inter-signal correlations in the recovery algorithms; (5) the design of distributed algorithms for the resulting large-size optimization model.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.
从收集的数据的时间序列中检测变化在各种应用中非常重要,包括语音识别、医疗监控、信用卡欺诈检测、自动目标识别和视频监控。在医疗监测等应用中,找到发生变化的位置是非常重要的。在诸如视频监视的其他应用中,改变的类型,例如感兴趣对象的移动或插入/删除,也是关键的。虽然从直接数据(例如,已经形成的图像)中检测这种变化已经被很好地研究,但在许多应用中,例如磁共振成像(MRI)、超声波和合成孔径雷达(SAR),间接地获取数据的时间序列。检测这些应用中的变化的典型方法是首先形成感兴趣的图像或信号。结果,存储在间接数据中的可能对检测变化有价值的信息经常丢失。因此,该项目寻求开发准确、高效和健壮的计算算法,用于从给定的间接数据的时间序列中检测信号或图像的变化,而无需首先重建感兴趣的信号或图像。此外,该项目寻求纳入变化信息,以开发更好的图像和信号重建算法。研究生和本科生都将参与研究调查,以加强他们在科学和工程领域的职业准备。参与者将在公开可用的数据集上应用这些新技术,特别是在核磁共振、超声波和合成孔径雷达应用中获得的数据集。PI将使用框架理论、优化和统计方面的工具来开发和严格分析新的变化检测和图像/信号恢复算法。具体地说,PIS将解决拟议工作中的以下技术问题:(1)将先验信息与适当的数学/统计公式结合在模型中;(2)从间接数据序列中提取对象的旋转/平移;(3)通过统计分析调整模型参数;(4)在恢复算法中使用信号内和信号间的相关性;(5)为所产生的大型优化模型设计分布式算法。该奖项反映了NSF的法定使命,并通过使用基金会的智力优势和更广泛的影响审查标准进行评估,被认为值得支持。

项目成果

期刊论文数量(7)
专著数量(0)
科研奖励数量(0)
会议论文数量(0)
专利数量(0)
Admissible Concentration Factors for Edge Detection from Non-uniform Fourier Data
非均匀傅立叶数据边缘检测的允许浓度因子
  • DOI:
    10.1007/s10915-020-01307-9
  • 发表时间:
    2020
  • 期刊:
  • 影响因子:
    2.5
  • 作者:
    Song, Guohui;Tucker, Gabe;Xia, Congzhi
  • 通讯作者:
    Xia, Congzhi
FAST MULTISCALE FUNCTIONAL ESTIMATION IN OPTIMAL EMG PLACEMENT FOR ROBOTIC PROSTHESIS CONTROLLERS
A Two-Step Fixed-Point Proximity Algorithm for a Class of Non-differentiable Optimization Models in Machine Learning
  • DOI:
    10.1007/s10915-019-01045-7
  • 发表时间:
    2019-09
  • 期刊:
  • 影响因子:
    2.5
  • 作者:
    Zheng Li;Guohui Song;Yuesheng Xu
  • 通讯作者:
    Zheng Li;Guohui Song;Yuesheng Xu
Multi-task Learning in vector-valued reproducing kernel Banach spaces with the ℓ1 norm
  • DOI:
    10.1016/j.jco.2020.101514
  • 发表时间:
    2019-01
  • 期刊:
  • 影响因子:
    0
  • 作者:
    Rongrong Lin;Guohui Song;Haizhang Zhang
  • 通讯作者:
    Rongrong Lin;Guohui Song;Haizhang Zhang
Sequential Image Recovery from Noisy and Under-Sampled Fourier Data
  • DOI:
    10.1007/s10915-022-01850-7
  • 发表时间:
    2022-06-01
  • 期刊:
  • 影响因子:
    2.5
  • 作者:
    Xiao,Yao;Glaubitz,Jan;Song,Guohui
  • 通讯作者:
    Song,Guohui
{{ item.title }}
{{ item.translation_title }}
  • DOI:
    {{ item.doi }}
  • 发表时间:
    {{ item.publish_year }}
  • 期刊:
  • 影响因子:
    {{ item.factor }}
  • 作者:
    {{ item.authors }}
  • 通讯作者:
    {{ item.author }}

数据更新时间:{{ journalArticles.updateTime }}

{{ item.title }}
  • 作者:
    {{ item.author }}

数据更新时间:{{ monograph.updateTime }}

{{ item.title }}
  • 作者:
    {{ item.author }}

数据更新时间:{{ sciAawards.updateTime }}

{{ item.title }}
  • 作者:
    {{ item.author }}

数据更新时间:{{ conferencePapers.updateTime }}

{{ item.title }}
  • 作者:
    {{ item.author }}

数据更新时间:{{ patent.updateTime }}

Guohui Song其他文献

Reprograming the tumor immunologic microenvironment by neoadjuvant chemotherapy in osteosarcoma.
通过骨肉瘤新辅助化疗重新编程肿瘤免疫微环境。
  • DOI:
  • 发表时间:
    2020
  • 期刊:
  • 影响因子:
    5.7
  • 作者:
    Chuangzhong Deng;Yanyang Xu;Jianchang Fu;Xiaojun Zhu;Hongmin Chen;Huaiyuan Xu;Gaoyuan Wang;Yijiang Song;Guohui Song;Jinchang Lu;Ranyi Liu;Qinglian Tang;Wenlin Huang;Jin Wang
  • 通讯作者:
    Jin Wang
Multi-task Learning in Vector-valued Reproducing Kernel Banach Spaces with the l1 Norm
具有 l1 范数的向量值再生核 Banach 空间中的多任务学习
  • DOI:
  • 发表时间:
    2021
  • 期刊:
  • 影响因子:
    1.7
  • 作者:
    Rongrong Lin;Guohui Song;Haizhang Zhang
  • 通讯作者:
    Haizhang Zhang
Technical and Economic Assessments of a novel biomass-to-synthetic natural gas (SNG) process integrating O2-enriched air gasification under Chinese scenario
  • DOI:
    https://doi.org/10.1016/j.psep.2021.10.025
  • 发表时间:
    2021
  • 期刊:
  • 影响因子:
  • 作者:
    Xiaobo Cui;Guohui Song;Ailing Yao;Hongyan Wang;Liang Wang
  • 通讯作者:
    Liang Wang
High-pressure oxidation of hydrogen diluted in Nsub2/sub with added Hsub2/subO or COsub2/sub at 100 atm in a supercritical-pressure jet-stirred reactor
在超临界压力喷射搅拌反应器中,于100个大气压下,对在氮气中稀释且添加了水或二氧化碳的氢气进行高压氧化
  • DOI:
    10.1016/j.combustflame.2024.113543
  • 发表时间:
    2024-08-01
  • 期刊:
  • 影响因子:
    6.200
  • 作者:
    Hao Zhao;Chao Yan;Guohui Song;Ziyu Wang;Ahren W. Jasper;Stephen J. Klippenstein;Yiguang Ju
  • 通讯作者:
    Yiguang Ju
A High-Dimensional Inverse Frame Operator Approximation Technique
一种高维逆框算子逼近技术
  • DOI:
    10.1137/15m1047593
  • 发表时间:
    2016
  • 期刊:
  • 影响因子:
    0
  • 作者:
    Guohui Song;Jacqueline Davis;Anne Gelb
  • 通讯作者:
    Anne Gelb

Guohui Song的其他文献

{{ item.title }}
{{ item.translation_title }}
  • DOI:
    {{ item.doi }}
  • 发表时间:
    {{ item.publish_year }}
  • 期刊:
  • 影响因子:
    {{ item.factor }}
  • 作者:
    {{ item.authors }}
  • 通讯作者:
    {{ item.author }}

{{ truncateString('Guohui Song', 18)}}的其他基金

Collaborative Research: Accurate, Efficient and Robust Computational Algorithms for Detecting Changes in a Scene Given Indirect Data
协作研究:准确、高效和稳健的计算算法,用于检测给定间接数据的场景变化
  • 批准号:
    1912689
  • 财政年份:
    2019
  • 资助金额:
    $ 14万
  • 项目类别:
    Standard Grant
Collaborative Research: An Integrated Approach to Convex Optimization Algorithms
协作研究:凸优化算法的集成方法
  • 批准号:
    1521661
  • 财政年份:
    2015
  • 资助金额:
    $ 14万
  • 项目类别:
    Standard Grant

相似国自然基金

Research on Quantum Field Theory without a Lagrangian Description
  • 批准号:
    24ZR1403900
  • 批准年份:
    2024
  • 资助金额:
    0.0 万元
  • 项目类别:
    省市级项目
Cell Research
  • 批准号:
    31224802
  • 批准年份:
    2012
  • 资助金额:
    24.0 万元
  • 项目类别:
    专项基金项目
Cell Research
  • 批准号:
    31024804
  • 批准年份:
    2010
  • 资助金额:
    24.0 万元
  • 项目类别:
    专项基金项目
Cell Research (细胞研究)
  • 批准号:
    30824808
  • 批准年份:
    2008
  • 资助金额:
    24.0 万元
  • 项目类别:
    专项基金项目
Research on the Rapid Growth Mechanism of KDP Crystal
  • 批准号:
    10774081
  • 批准年份:
    2007
  • 资助金额:
    45.0 万元
  • 项目类别:
    面上项目

相似海外基金

Collaborative Research: Accurate and Structure-Preserving Numerical Schemes for Variable Temperature Phase Field Models and Efficient Solvers
合作研究:用于变温相场模型和高效求解器的精确且结构保持的数值方案
  • 批准号:
    2309547
  • 财政年份:
    2023
  • 资助金额:
    $ 14万
  • 项目类别:
    Standard Grant
Collaborative Research: Accurate and Structure-Preserving Numerical Schemes for Variable Temperature Phase Field Models and Efficient Solvers
合作研究:用于变温相场模型和高效求解器的精确且结构保持的数值方案
  • 批准号:
    2309548
  • 财政年份:
    2023
  • 资助金额:
    $ 14万
  • 项目类别:
    Standard Grant
Collaborative Research: Time Accurate Fluid-Structure Interactions
合作研究:时间精确的流固耦合
  • 批准号:
    2208220
  • 财政年份:
    2022
  • 资助金额:
    $ 14万
  • 项目类别:
    Standard Grant
Collaborative Research: SHF: Small: Exploiting Performance Correlations for Accurate and Low-cost Performance Testing for Serverless Computing
协作研究:SHF:小型:利用性能相关性对无服务器计算进行准确且低成本的性能测试
  • 批准号:
    2155096
  • 财政年份:
    2022
  • 资助金额:
    $ 14万
  • 项目类别:
    Standard Grant
Collaborative Research: Advances in Quantum Control and Noise Mitigation on A Highly Accurate Testbed
合作研究:高精度测试台上量子控制和噪声抑制的进展
  • 批准号:
    2210013
  • 财政年份:
    2022
  • 资助金额:
    $ 14万
  • 项目类别:
    Standard Grant
Collaborative Research: Advances in Quantum Control and Noise Mitigation on A Highly Accurate Testbed
合作研究:高精度测试台上量子控制和噪声抑制的进展
  • 批准号:
    2210018
  • 财政年份:
    2022
  • 资助金额:
    $ 14万
  • 项目类别:
    Standard Grant
Collaborative Research: Time Accurate Fluid-Structure Interactions
合作研究:时间精确的流固耦合
  • 批准号:
    2208219
  • 财政年份:
    2022
  • 资助金额:
    $ 14万
  • 项目类别:
    Standard Grant
Collaborative Research: SHF: Small: Exploiting Performance Correlations for Accurate and Low-cost Performance Testing for Serverless Computing
协作研究:SHF:小型:利用性能相关性对无服务器计算进行准确且低成本的性能测试
  • 批准号:
    2155097
  • 财政年份:
    2022
  • 资助金额:
    $ 14万
  • 项目类别:
    Standard Grant
Collaborative Research: ATD: Robust, Accurate and Efficient Graph-Structured RNN for Spatio-Temporal Forecasting and Anomaly Detection
合作研究:ATD:用于时空预测和异常检测的鲁棒、准确和高效的图结构 RNN
  • 批准号:
    2110145
  • 财政年份:
    2021
  • 资助金额:
    $ 14万
  • 项目类别:
    Standard Grant
{{ showInfoDetail.title }}

作者:{{ showInfoDetail.author }}

知道了