RI: Small: Exploiting Correlated Sparsity Pattern Change in Dynamic Vision Problems

RI：小：利用动态视觉问题中的相关稀疏模式变化

• 批准号: 1117509
• 负责人: Namrata Vaswani
• 金额: $ 20.44万
• 依托单位: Iowa State University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2011
• 资助国家: 美国
• 起止时间: 2011-09-01 至 2016-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1117509&HistoricalAwards=false
• 关键词: RI; Small; Exploiting; Correlated; Sparsity

This project develops a new framework to solve a large class of dynamic vision problems by exploiting correlated sparsity pattern change in the appropriate domain. The focus is on high-dimensional visual tracking problems such as deformable contour tracking or target tracking in the presence of significant illumination changes. These are difficult because of the high dimensionality and because the observation models are highly nonlinear and/or non-Gaussian due to clutter, occlusions or low contrast. However, in most such problems, even though the state (e.g., contour deformation or illumination) is high-dimensional, at any given time, most change occurs in only a few principal directions. In a long sequence, this set of directions can gradually change over time. Most existing methods need a set of past state estimates to estimate this change on-the-fly while tracking noisy or nonlinear systems. The research team provides a completely new solution to this difficult problem by re-interpreting it as a problem of "recursively reconstructing sparse state sequences with slow time-varying sparsity patterns" and tapping into ideas from their ongoing recursive sparse recovery work.The research of this project enriches the knowledge base of computer vision and can be applied to many different applications such as medical image analysis and video surveillance. The project provides research opportunities for graduate students and involves undergraduate students, including under-represented minorities, through summer, senior design projects and REU projects.

该项目开发了一个新的框架，通过在适当的领域中利用相关稀疏模式变化来解决一类动态视觉问题。重点是高维视觉跟踪问题，如变形轮廓跟踪或存在显著光照变化的目标跟踪。这些都是困难的，因为高维，因为观察模型是高度非线性和/或非高斯由于杂波，遮挡或低对比度。然而，在大多数此类问题中，即使状态（例如，轮廓变形或照明）是高维的，在任何给定时间，大多数变化只发生在几个主要方向上。在一个长序列中，这组方向可以随着时间的推移而逐渐改变。大多数现有的方法需要一组过去的状态估计来估计这种变化，同时跟踪噪声或非线性系统。研究小组通过将其重新解释为“递归地重建具有缓慢时变稀疏模式的稀疏状态序列”的问题，并利用他们正在进行的递归稀疏恢复工作中的思想，为这个难题提供了一个全新的解决方案。本课题的研究丰富了计算机视觉的知识基础，可应用于医学图像分析、视频监控等多个领域。该项目通过夏季、高级设计项目和REU项目，为研究生和本科生（包括代表性不足的少数民族）提供研究机会。