ITR/AP: Variational-PDE Models Using Level Sets for Computer Vision

ITR/AP：使用水平集进行计算机视觉的变分偏微分方程模型

• 批准号: 0113439
• 负责人: Luminita Vese
• 金额: $ 21.02万
• 依托单位: University of California-Los Angeles
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2001
• 资助国家: 美国
• 起止时间: 2001-10-01 至 2006-03-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0113439&HistoricalAwards=false
• 关键词: ITR; AP; Variational; PDE; Models

This project will attack several important problems in image processing and computer vision. The applications for this work include object detection and recognition (e.g. for tracking objects in video surveillance), segmentation of medical images (e.g. for recognizing white and gray matter in MRI brain data), occlusion and analysis of missing information (e.g. for finding depth from 2-D images), and direction diffusion (e.g. for analyzing fingerprints). The project will work at the boundary between computational and applied mathematics, computer science and engineering, and medical imagery.The research is based on using level set techniques applied to variational models and partial differential equations derived from the images. The work uses an active contour model without edges for object detection, based on the Osher-Sethian level set method and Mumford-Shah segmentation techniques. This model has many advantages over classical ones, including automatic detection of interior contours and robustness with respect to noise. The project will generalize and extend this basic model and provide computationally efficient implementations.

该项目将解决图像处理和计算机视觉中的几个重要问题。这项工作的应用包括对象检测和识别（例如，用于跟踪视频监控中的对象）、医学图像分割（例如，用于识别 MRI 大脑数据中的白质和灰质）、缺失信息的遮挡和分析（例如，用于从图像中查找深度）二维图像）和方向扩散（例如用于分析指纹）。该项目将在计算和应用数学、计算机科学与工程以及医学图像之间的边界开展工作。该研究基于使用水平集技术应用于从图像导出的变分模型和偏微分方程。该工作基于 Osher-Sethian 水平集方法和 Mumford-Shah 分割技术，使用无边缘的活动轮廓模型进行对象检测。该模型比经典模型具有许多优点，包括自动检测内部轮廓和对噪声的鲁棒性。该项目将概括和扩展这个基本模型，并提供计算高效的实现。