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

CGV：小：RUI：分析子空间结构以实现组级图像理解

• 批准号: 1219016
• 负责人: Lopamudra Mukherjee
• 金额: $ 23.13万
• 依托单位: University of Wisconsin-Whitewater
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2012
• 资助国家: 美国
• 起止时间: 2012-07-15 至 2018-06-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1219016&HistoricalAwards=false
• 关键词: CGV; Small; RUI; Analyzing; subspace

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.

如今生成的图像数量的大幅增长为计算机视觉研究开辟了新的方向。今天的图像几乎从来都不是独立生成的，而是表现为集合。因此，我们现在需要考虑可能具有显着相关结构的组内的图像，而不是将图像视为单独的实体。然而，一些基本图像分析任务（例如图像分割）的形式化在很大程度上仍然一次考虑一张图像。该项目证明了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，允许在组级别对图像进行分割，因此适用于视觉数据通常呈现的各种场景。主要目标是设计一个全面的系统来解决这个问题的各个方面——从使用训练数据预处理输入，到提供考虑组结构的强大分割模型，再到构建可用于有效分割一组相关图像的字典。该项目为吸引本科生在学期和夏季全日制研究中提供了一种途径。学生接触并参与计算机视觉领域最先进的研究，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。这可以显着增加这些学生选择继续深造计算机科学的可能性。