Surviving the data deluge: Scalable feature extraction, discrimination and analysis for computer vision tasks using compressed sensed data

在数据洪流中生存：使用压缩感知数据进行计算机视觉任务的可扩展特征提取、区分和分析

• 批准号: DP1093233
• 负责人: Prof Svetha Venkatesh
• 金额: $ 21.58万
• 依托单位: Deakin University
• 依托单位国家: 澳大利亚
• 项目类别: Discovery Projects
• 财政年份: 2010
• 资助国家: 澳大利亚
• 起止时间: 2010-01-01 至 2012-12-31
• 项目状态: 已结题
• 来源: https://dataportal.arc.gov.au/RGS/Web/Grants/DP1093233
• 关键词: Surviving; data; deluge; Scalable; feature

Strategically, our pioneering solutions besides being technically and socially significant, open fresh options for sensor-agnostic data analysis. The technical significance lies through the creation of new technologies for the critical national and global security markets, currently overwhelmed by data. The social significance arises from our solutions being privacy preserving, providing new avenues for the production of novel, socially acceptable products for aged care monitoring. Our methods spearhead future advancement in diverse disciplines due to the wide applicability of the methods to other sensor networks (Square Kilometre Array) and data types, providing new frameworks for addressing crucial problems of data management.

从战略上讲，我们的开创性解决方案除了具有技术和社会意义外，还为与传感器无关的数据分析提供了新的选择。技术意义在于为目前被数据淹没的关键国家和全球安全市场创造新技术。社会意义源于我们的解决方案保护隐私，为生产新颖的、社会可接受的老年护理监控产品提供了新途径。由于我们的方法对其他传感器网络（平方公里阵列）和数据类型的广泛适用性，我们的方法引领了不同学科的未来进步，为解决数据管理的关键问题提供了新的框架。