SGER: Segmentation Trees and Their Robust Matching as Core Technologies for Recognition

SGER：分割树及其鲁棒匹配作为识别的核心技术

• 批准号: 0743014
• 负责人: Narendra Ahuja
• 金额: $ 10万
• 依托单位: University of Illinois at Urbana-Champaign
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2007
• 资助国家: 美国
• 起止时间: 2007-08-01 至 2008-07-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0743014&HistoricalAwards=false
• 关键词: SGER; Segmentation; Trees; Their; Robust

AbstractThe goal of this SGER proposal is to investigate the feasibility of using a segmentation tree as a general purpose multiscale representation of image structure, and assess the value of this representation for higher-level tasks such as object recognition. This objective requires demonstrating the stability of such a tree under changes in object viewing conditions, and developing robust algorithms for matching segmentation trees to find corresponding regions in multiple views of the same object. The motivation to explore this line of thinking has come from the recent work of the PIs, which has indicated that segmentation trees have the potential of making a significant impact on the state of the art in object recognition. This finding is controversial as it contradicts a widely held belief in the vision community that since low-level image segmentation varies somewhat with imaging conditions, algorithms that use regions as image features cannot offer a reliable basis for image understanding.The main goal of this proposal is to address those concerns and obtain conclusive results to firmly establish or reject the PIs' preliminary conclusions.

摘要：该 SGER 提案的目标是研究使用分割树作为图像结构的通用多尺度表示的可行性，并评估该表示对于对象识别等更高级别任务的价值。 这一目标需要证明这种树在对象观看条件变化下的稳定性，并开发鲁棒的算法来匹配分割树，以在同一对象的多个视图中找到相应的区域。探索这一思路的动机来自 PI 最近的工作，该工作表明分割树有可能对对象识别的最新技术产生重大影响。这一发现是有争议的，因为它与视觉界广泛持有的信念相矛盾，即由于低级图像分割随成像条件的不同而有所不同，使用区域作为图像特征的算法无法为图像理解提供可靠的基础。该提案的主要目标是解决这些问题并获得结论性的结果，以坚定地建立或拒绝 PI 的初步结论。