Collaborative Research: Analysis and processing of multidimensional data using sparse directional multiscale representations
协作研究:使用稀疏定向多尺度表示分析和处理多维数据
基本信息
- 批准号:1008900
- 负责人:
- 金额:$ 34.07万
- 依托单位:
- 依托单位国家:美国
- 项目类别:Continuing Grant
- 财政年份:2010
- 资助国家:美国
- 起止时间:2010-10-01 至 2014-09-30
- 项目状态:已结题
- 来源:
- 关键词:
项目摘要
Labate, DMS-1008900Guo, DMS-1008907 Following the spectacular success of wavelets in signal andimage processing, several attempts have been made to adapt theiroptimal efficiency from the one- to the multi-dimensionalsetting. In fact, in spite of their remarkable properties,wavelets are not very efficient in capturing the intrinsicgeometry of multidimensional phenomena. In recent years,directional multiscale methods such as the shearletrepresentation, introduced by the investigators and theircollaborators, have emerged as the most effective extension ofthe wavelet framework to the multidimensional setting. Indeed,the shearlet representation encompasses the mathematical theoryof affine systems and, to date, is the only method able tocombine optimal sparsity and fast transforms through the power ofmultiresolution analysis. The proposed research focuses onapplications of the shearlet approach to a number of challengingproblems of analysis and processing of multidimensional data. First, the shearlet representation is applied to provide aprecise geometric characterization of the discontinuities ofmultivariate functions and distributions. Combining techniquesfrom harmonic analysis and differential geometry, this providesthe groundwork for the development of improved algorithms foredge detection and feature extraction. Second, the shearletframework is applied to develop a new generation of methods forthe regularized inversion of ill-posed problems. Building on theability of shearlets to provide sparse representations of Fourierintegral operators, efficient decompositions for the Radon andRay transforms are computed. These are used to developalgorithms for the Radon inversion from local and incomplete dataand for image deconvolution. Third, a novel mathematical andcomputational approach for viewpoint-invariant texture retrievalis introduced. This is achieved by jointly designing a frameworkfor feature extraction and similarity measurement in anappropriate statistical setting, and relies on the unique abilityof shearlets to capture local geometric information. Over the past several years, there has been a continuouslyincreasing pressure to handle more efficiently the ever largerand higher dimensional data sets generated from a wide rangeapplications such as electronic surveillance, remote sensing, andmedical imaging. The challenge is to rapidly, accurately andreliably extract the relevant information, so that it can beefficiently processed, transmitted and stored. The projectfocuses on the applications of the shearlet representation -- amethod introduced by the investigators and their collaboratorsthat provides a unique combination of optimal sparsity andcomputational efficiency, within an innovative mathematical andcomputational framework. The notion of optimal sparsity, inparticular, implies that this approach has the ability to veryeffectively and reliably identify the most relevant featurescontained in the data. Specifically, this project leads toadvanced techniques for edge detection, feature extraction, andtexture retrieval from medical, industrial and satellite imagery. This results in innovative and improved computational algorithmsfor the analysis and processing of high-dimensional data andfacilitates technological advances in sensitive applications suchas remote sensing, medical diagnostics, data classification andelectronic surveillance.
随着小波在信号和图像处理中的巨大成功,人们进行了几次尝试,以使它们的最佳效率从一维调整到多维设置。事实上,尽管小波具有显著的性质,但在捕捉多维现象的内在几何学方面并不是很有效。近年来,研究人员及其合作者提出的剪切法等定向多尺度方法已成为小波框架向多维环境最有效的扩展。事实上,Shearlet表示包含了仿射系统的数学理论,并且到目前为止,是唯一能够通过多分辨率分析的能力结合最佳稀疏性和快速变换的方法。所提出的研究集中于将剪切法应用于多维数据的分析和处理中的一些具有挑战性的问题。首先,应用Shearlet表示对多元函数和分布的不连续性进行精确的几何刻画。结合了调和分析和微分几何的技术,为改进的森林检测和特征提取算法的发展奠定了基础。其次,应用Shearlet框架发展了新一代不适定问题的正则化逆问题的方法。基于Searlet能够提供傅里叶积分算子的稀疏表示的能力,计算了Radon Andray变换的有效分解。这些算法被用于从局部和不完整数据中进行Radon反演和图像反褶积。第三,提出了一种新的视点不变纹理检索的数学和计算方法。这是通过在适当的统计设置下联合设计特征提取和相似性度量的框架来实现的,并且依赖于剪切器独特的捕捉局部几何信息的能力。在过去的几年里,人们面临着越来越大的压力,需要更有效地处理从电子监视、遥感和医学成像等广泛应用中产生的越来越大和更高维的数据集。挑战是快速、准确和可靠地提取相关信息,以便能够有效地处理、传输和存储这些信息。该项目侧重于Shearlet表示的应用--这是研究人员及其合作者提出的一种方法,在创新的数学和计算框架内提供最佳稀疏性和计算效率的独特组合。最优稀疏性的概念特别意味着这种方法能够非常有效和可靠地识别数据中包含的最相关的特征。具体地说,该项目带来了从医学、工业和卫星图像中进行边缘检测、特征提取和纹理检索的先进技术。这导致了分析和处理高维数据的创新和改进的计算算法,并促进了遥感、医疗诊断、数据分类和电子监视等敏感应用领域的技术进步。
项目成果
期刊论文数量(0)
专著数量(0)
科研奖励数量(0)
会议论文数量(0)
专利数量(0)
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Demetrio Labate其他文献
Time-Frequency Analysis of Pseudodifferential Operators
- DOI:
10.1007/s006050170028 - 发表时间:
2001-06-01 - 期刊:
- 影响因子:0.800
- 作者:
Demetrio Labate - 通讯作者:
Demetrio Labate
βIV spectrin abundancy, cellular distribution and sensitivity to AKT/GSK3 regulation in schizophrenia
精神分裂症中βIV血影蛋白丰度、细胞分布以及对 AKT/GSK3 调节的敏感性
- DOI:
10.1038/s41380-025-02917-1 - 发表时间:
2025-02-07 - 期刊:
- 影响因子:10.100
- 作者:
Jessica Di Re;Michela Marini;Syed Ibrar Hussain;Aditya K. Singh;Akshaya Venkatesh;Musaad A. Alshammari;Tahani K. Alshammari;Abdul-Rizaq Ali Hamoud;Ali Sajid Imami;Zahra Haghighijoo;Nickolas Fularcyzk;Laura Stertz;Derek Hawes;Angela Mosebarger;Jordan Jernigan;Claire Chaljub;Ralda Nehme;Consuelo Walss-Bass;Anton Schulmann;Marquis P. Vawter;Robert McCullumsmith;Robert D. Damoiseaux;Agenor Limon;Demetrio Labate;Michael F. Wells;Fernanda Laezza - 通讯作者:
Fernanda Laezza
Platelet aggregometry for hip fracture surgery in patients treated with clopidogrel: a pilot study
- DOI:
10.1007/s10877-021-00714-z - 发表时间:
2021-05-06 - 期刊:
- 影响因子:2.200
- 作者:
Marco Tescione;Eugenio Vadalà;Graziella Marano;Enzo Battaglia;Andrea Bruni;Eugenio Garofalo;Federico Longhini;Serena Rovida;Nicola Polimeni;Rosalba Squillaci;Stefano Lascala;Gaetana Franco;Demetrio Labate;Massimo Caracciolo;Sebastiano Macheda - 通讯作者:
Sebastiano Macheda
High throughput microscopy and single cell phenotypic image-based analysis in toxicology and drug discovery
高通量显微镜和基于单细胞表型图像的毒理学和药物发现分析
- DOI:
10.1016/j.bcp.2023.115770 - 发表时间:
2023-10-01 - 期刊:
- 影响因子:5.600
- 作者:
Fabio Stossi;Pankaj K. Singh;Kazem Safari;Michela Marini;Demetrio Labate;Michael A. Mancini - 通讯作者:
Michael A. Mancini
A multistep deep learning framework for the automated detection and segmentation of astrocytes in fluorescent images of brain tissue
用于脑组织荧光图像中星形胶质细胞自动检测和分割的多步骤深度学习框架
- DOI:
10.1038/s41598-020-61953-9 - 发表时间:
2020-03-20 - 期刊:
- 影响因子:3.900
- 作者:
Cihan Bilge Kayasandik;Wenjuan Ru;Demetrio Labate - 通讯作者:
Demetrio Labate
Demetrio Labate的其他文献
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{{ truncateString('Demetrio Labate', 18)}}的其他基金
Multiscale Algorithms for the Geometric Analysis of Hyperspectral Data
高光谱数据几何分析的多尺度算法
- 批准号:
1720452 - 财政年份:2017
- 资助金额:
$ 34.07万 - 项目类别:
Standard Grant
Career: Sparse directional multiscale representations: theory, implementation and applications
职业:稀疏方向多尺度表示:理论、实现和应用
- 批准号:
1005799 - 财政年份:2009
- 资助金额:
$ 34.07万 - 项目类别:
Standard Grant
Career: Sparse directional multiscale representations: theory, implementation and applications
职业:稀疏方向多尺度表示:理论、实现和应用
- 批准号:
0746778 - 财政年份:2008
- 资助金额:
$ 34.07万 - 项目类别:
Standard Grant
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