CIF: Small: Beyond Sparsity - Exploiting Saliency in Compressive and Adaptive Sensing

CIF：小：超越稀疏性 - 利用压缩和自适应传感中的显着性

• 批准号: 1217751
• 负责人: Jarvis Haupt
• 金额: $ 30.9万
• 依托单位: University of Minnesota-Twin Cities
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2012
• 资助国家: 美国
• 起止时间: 2012-07-01 至 2016-06-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1217751&HistoricalAwards=false
• 关键词: CIF; Small; Beyond; Sparsity; Exploiting

The presence of salient features or anomalous behavior is a characteristic shared in many modern application domains. For example, an anomalous region in an MRI image could indicate the presence of a tumor or lesion; accurately locating conspicuous features in perhaps very high-dimensional images is a critical step in automated surveillance; and identification of anomalies in network traffic is a crucial step in detecting various attacks. Each of these applications is illustrative of an underlying theme, where subsets of data or image sub-regions are deemed interesting based on their relationship with (or, more precisely, their deviation from) typical behavior exhibited by the bulk of the data. In this sense, saliency can be understood as a natural generalization of the notion of sparsity, but one that is intrinsic to the data itself.Recent developments in compressive and adaptive sensing have demonstrated that tremendous improvements in sensing resource efficiency can be realized when inferring high-dimensional data or objects that possess simple, low-dimensional representations. This research develops new theory and methods extending resource-efficient compressive and adaptive sensing techniques, which exploit sparsity as a model for data parsimony, to procedures that exploit saliency as a low-dimensional model for certain high-dimensional data. In particular, this effort (1) advances the current state-of-the-art theory and methods in compressive and adaptive sensing by developing a novel set of efficient saliency-based sensing methods, (2) demonstrates the robustness of these methods to uncertainties and noise, (3) integrates these new developments into the curriculum at the University of Minnesota, and (4) leverages emerging mobile device technologies as a novel vehicle for demonstrating and broadly disseminating the results of this effort to potentially new and diverse audiences.

显着特征或异常行为的存在是许多现代应用领域的共同特征。例如，MRI 图像中的异常区域可能表明存在肿瘤或病变；在可能非常高维的图像中准确定位显着特征是自动监视的关键步骤；识别网络流量异常是检测各种攻击的关键步骤。 这些应用程序中的每一个都说明了一个基本主题，其中数据子集或图像子区域根据它们与大量数据所表现出的典型行为的关系（或者更准确地说，它们的偏差）被认为是有趣的。从这个意义上说，显着性可以理解为稀疏性概念的自然概括，但它是数据本身固有的。压缩和自适应传感的最新发展表明，当推断高值时，可以实现传感资源效率的巨大改进。 - 具有简单、低维表示的维数据或对象。这项研究开发了新的理论和方法，将资源高效的压缩和自适应传感技术（利用稀疏性作为数据简约模型）扩展到利用显着性作为某些高维数据的低维模型的程序。特别是，这项工作（1）通过开发一套新颖的基于显着性的高效传感方法，推进了压缩和自适应传感领域当前最先进的理论和方法，（2）证明了这些方法对不确定性的鲁棒性和噪音，(3) 将这些新发展融入明尼苏达大学的课程中，(4) 利用新兴的移动设备技术作为一种新颖的工具，向潜在的新的和多样化的受众展示和广泛传播这项努力的成果。