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.

显著特征或异常行为的存在是许多现代应用领域中共有的特征。例如，MRI图像中的异常区域可能表明肿瘤或病变的存在;在可能非常高维的图像中准确定位显著特征是自动监控的关键步骤;识别网络流量中的异常是检测各种攻击的关键步骤。 这些应用中的每一个都说明了一个潜在的主题，其中数据或图像子区域的子集基于它们与大部分数据所表现出的典型行为的关系（或者更准确地说，它们与大部分数据所表现出的典型行为的偏差）而被认为是有趣的。在这个意义上，显着性可以被理解为稀疏性概念的自然概括，但它是数据本身固有的，压缩和自适应感知的最新发展表明，当推断高维数据或具有简单，低维表示的对象时，可以实现感知资源效率的巨大改善。这项研究开发了新的理论和方法，扩展资源有效的压缩和自适应传感技术，利用稀疏性作为数据简约的模型，利用显着性作为某些高维数据的低维模型的程序。特别地，这项努力（1）通过开发一组新的有效的基于显著性的感测方法，推进了压缩和自适应感测中的当前最先进的理论和方法，（2）证明了这些方法对不确定性和噪声的鲁棒性，（3）将这些新的发展整合到明尼苏达大学的课程中，以及（4）利用新兴的移动终端技术作为向潜在的新的和不同的受众展示和广泛传播这一努力的结果的新颖工具。