Joint Space-Time Analysis and Characterization of Image Sequences

图像序列的联合时空分析和表征

• 批准号: 0209055
• 负责人: Janusz Konrad
• 金额: $ 30万
• 依托单位: Trustees of Boston University
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2002
• 资助国家: 美国
• 起止时间: 2002-07-01 至 2005-06-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0209055&HistoricalAwards=false
• 关键词: Joint; Space; Time; Analysis; Characterization

Janusz KonradBoston UniversityThis research is concerned with the processing of visual information captured by a video camera. To date, image sequences have been typically analyzed and processed in groups of two frames; by differentiating one frame from another, short-term image dynamics can be measured, such as changed areas (e.g., occlusions) or pixel displacements. Although the approach has been very successful to date (e.g., MPEG compression standards), further gains are difficult to attain based on two image frames only. This research project offers a different approach to the analysis of image sequences - one based on a joint processing of multiple image frames at a time. This joint treatment of, for example, 10 or 20 frames is expected to result in new gains in video compression, more reliable video database querying, and more accurate detection of innovations (occlusion and exposure areas) that are of interest in surveillance applications.The primary problem attacked in the project is the joint space-time segmentation of an image sequence into "object tunnels", i.e., 3-D volumes carved out in the space of horizontal, vertical and temporal coordinates by a moving object. The estimation of object tunnels is approached as a volume competition problem, and solved using active-surface evolution equations embedded into the level-set solution framework. In order to model motion of points within each object tunnel, a new spatially-parametric, temporally-quadratic motion model is studied. Various cost functionals relating object-tunnel intensities to the underlying motion are investigated. Since the standard volume competition can only extract a single moving object from background, an extension to more objects by means of multiple "repelling" surfaces evolving simultaneously is studied as well.

Janusz Konradboston University这项研究与摄像机捕获的视觉信息有关。迄今为止，通常对图像序列进行了分析和处理，并以两个帧的组组进行处理。通过将一个帧与另一帧区分开来，可以测量短期图像动力学，例如更改的区域（例如遮挡）或像素位移。尽管该方法迄今非常成功（例如，MPEG压缩标准），但仅基于两个图像帧而难以获得进一步的收益。该研究项目为分析图像序列提供了一种不同的方法 - 一个基于一次多个图像帧的联合处理。例如，预计这种联合处理将导致视频压缩，更可靠的视频数据库查询以及对监视应用中感兴趣的创新（遮挡和暴露区域）的更准确检测的新增长，该项目攻击中的主要问题是项目攻击的主要问题。垂直和时间坐标通过移动对象进行坐标。对象隧道的估计是作为音量竞争问题的，并使用嵌入在级别集合解决方案框架中的活动表面进化方程来解决。为了模拟每个对象隧道内的点运动，研究了一个新的空间参数，时间偏二次运动模型。研究了各种成本功能，将对象隧道强度与基础运动有关。由于标准音量竞争只能从背景中提取单个移动对象，因此也研究了通过多个“排斥”表面同时进化的更多对象扩展。