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框架，通过利用适当领域中的相关稀疏模式变化来解决一大类动态视觉问题。重点是高维视觉跟踪问题，例如存在显着照明变化的可变形轮廓跟踪或目标跟踪。这些是困难的，因为高维度并且因为观测模型由于杂波、遮挡或低对比度而高度非线性和/或非高斯。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，但在任何给定时间，大多数变化仅发生在几个主要方向上。在一个很长的序列中，这组方向可能会随着时间的推移而逐渐改变。大多数现有方法需要一组过去的状态估计来动态估计这种变化，同时跟踪噪声或非线性系统。研究团队通过将其重新解释为“具有缓慢时变稀疏模式的递归重建稀疏状态序列”的问题，并借鉴他们正在进行的递归稀疏恢复工作的思想，为这个难题提供了一个全新的解决方案。该项目丰富了计算机视觉的知识库，可应用于医学图像分析和视频监控等许多不同的应用。该项目为研究生提供研究机会，并让本科生（包括代表性不足的少数族裔）参与暑期项目、高级设计项目和 REU 项目。