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

基本信息

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

项目摘要

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，通过利用相关域的相关稀疏模式变化来解决大量的动态视觉问题。重点是在存在显着照明变化的情况下，例如可变形轮廓跟踪或目标跟踪等高维视觉跟踪问题。由于较高的维度，由于杂物，闭塞或低对比度，这些观察模型是高度非线性和/或非高斯的，因此很困难。但是，在大多数此类问题中，即使状态（例如，轮廓变形或照明）是高度的，在任何给定时间，大多数变化都仅在几个主要方向上发生。长期以来，这组方向可以随时间逐渐改变。大多数现有的方法都需要一组过去的状态估算，以在跟踪嘈杂或非线性系统的同时估算此变化。研究团队通过将其重新解释为“递归地重建稀疏状态序列，并以缓慢的时间变化的稀疏模式重建稀疏状态序列”，从而为此提供了一个全新的解决方案。该项目为研究生提供了研究机会，并涉及本科生，包括代表性不足的少数民族，直到夏季，高级设计项目和REU项目。