CGV: Small: RUI: Analyzing subspace structure for group level image understanding

当今生成的图像数量的大幅增长为计算机视觉研究开辟了一个新的方向。今天的图像几乎从来没有独立生成，而是表现为集合。因此，我们现在需要考虑的不是将图像视为单个实体，而是可能具有显著相关结构的组中的图像。然而，几个基本的图像分析任务，如图像分割的形式化，在大多数情况下，仍然考虑一个图像的时间。该项目利用这种共享结构来理解图像中的内容。它开发了一个有效的框架，允许在组级别的图像分割，因此适用于各种场景中，视觉数据通常会出现自己。主要目标是设计一个全面的系统，解决这个问题的各个方面-从预处理输入使用训练数据，提供强大的分割模型，考虑到组结构，建立字典，然后可以用来有效地分割一组相关的图像。这个项目提供了一个工具，让本科生在学期和夏季全职沉浸在研究中。学生接触并参与计算机视觉的最先进研究，这进一步加深了他们对这些主题的理解，并激发了他们的求知欲。这可以大大增加这些学生选择在计算机科学领域进行更高学习的可能性。

The massive growth in the number of images being generated today has fostered a new direction in computer vision research. Images today are almost never generated independently, rather manifest as collections. Therefore, instead of thinking of images as individual entities, we now need to consider images within a group that may have significant correlational structure. Yet, formalizations of several fundamental image analysis tasks such as image segmentation, for the most part, still consider one image at a time. This project makes the case for exploiting this shared structure for understanding content in images. It develops an efficient framework which permits the segmentation of images at a group level and therefore is applicable to various scenarios in which visual data typically presents itself. The primary objective is to design a comprehensive system that addresses all aspects of this problem -- from preconditioning the input using training data, to providing powerful segmentation models that take the group structure into account, to building dictionaries which can then be used to effectively segment a set of related images. This project provides a vehicle for engaging undergraduates to become immersed in research during the semesters and full-time in summers. Students are exposed to and participate in state-of-the-art research in computer vision which furthers their understanding of these topics and stimulate their intellectual curiosity. This can significantly increase the possibility of these students choosing to pursue higher studies in computer science.