CGV: Large: Collaborative Research: Analyzing Images Through Time

该合作研究项目利用了四个研究团队（麻省理工学院IIS-1111415；哈佛大学IIS-1110955；华盛顿大学IIS-1111398；康奈尔大学IIS-1111534）的专业知识。理解时变过程和现象是科学和工程的基础。由于数码摄影技术的巨大进步，图像和视频（包括来自网络摄像机的图像、科学家拍摄的延时摄影、监控视频和互联网图集）正在成为了解我们这个动态世界的重要信息来源。然而，从图像或视频中自动理解和可视化时变过程的技术是稀缺和不发达的，需要基本的新模型和算法来表示随时间的变化。这项研究包括创建系统，使基于图像数据的时变过程建模、分析和可视化。这些模型和算法将构成一套新工具的基础，这些工具可以帮助回答一些重要问题，比如我们的环境是如何变化的，我们的城市是如何发展的，以及世界各地正在发生什么重大事件。随着时间的推移分析图像带来了根本性的新技术挑战。该项目侧重于开发和演示端到端系统，包括：(1)建模时变图像数据集所需的新颖表示；(2)图像数据集中远时对应估计算法；(3)将图像数据集分解为直观的原语（如阴影、照明、反射率和运动）的算法；(4)分析工具，用于从分解的表示（例如，趋势、重复模式和异常事件）中获得更高层次的信息；(5)高级信息可视化工具和图像数据再合成方法。这项工作有可能在随着时间的推移而产生图像的广泛领域产生重大影响，例如生态学、天文学、城市规划、健康和许多其他领域。这项研究的结果将通过项目网站（https://groups.csail.mit.edu/vision/image_time/）公开提供源代码和数据集，并在重要会议上提供教程和组织讲习班，广泛传播。该项目为本科生和研究生以及四所院校的学生提供了教育机会和实践合作研究经验。

This collaborative research project leverages expertise of four research teams (IIS-1111415, Massachusetts Institute of Technology; IIS-1110955, Harvard University; IIS-1111398, Washington University; and IIS-1111534, Cornell University). Understanding time-varying processes and phenomena is fundamental to science and engineering. Due to tremendous progress in digital photography, images and videos (including images from webcams, time- lapse photography captured by scientists, surveillance videos, and Internet photo collections) are becoming an important source of information about our dynamic world. However, techniques for automated understanding and visualization of time-varying processes from images or videos are scarce and underdeveloped, requiring fundamental new models and algorithms for representing changes over time. This research involves creating systems that enable modeling, analysis, and visualization of time-varying processes based on image data. These models and algorithms will form the basis for a new set of tools that can help answer important questions about how our environment is changing, how our cities are evolving, and what significant events are happening around the world.Analyzing images over time poses fundamental new technical challenges. This project focuses on developing and demonstrating end-to-end systems consisting of (1) novel representations necessary to model time-varying image datasets; (2) algorithms for estimating long-range temporal correspondence in image datasets; (3) algorithms for decomposing image datasets into intuitive primitives such as shading, illumination, reflectance, and motion; (4) analysis tools for deriving higher level information from the decomposed representations (e.g., trends, repeated patterns, and unusual events); and (5) tools for visualization of the high-level information and methods for re-synthesis of image data.This work has the potential to have significant impact in a broad range of areas where images are generated over time, e.g., in ecology, astronomy, urban planning, health, and many others. The results of this research will be broadly disseminated by making source code and datasets publicly available via the project web site (https://groups.csail.mit.edu/vision/image_time/) and offering tutorials and organizing workshops at significant conferences. The project provides educational opportunities and offers hands-on collaborative research experience to students at both the undergraduate and graduate levels and the four institutions.