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

这个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，并评估这种表示方法对更高级别任务（如对象识别）的价值。 这一目标需要证明这种树的稳定性下的对象观察条件的变化，并开发强大的算法匹配分割树，以找到相应的区域在多个视图中的同一个对象。探索这一思路的动机来自PI最近的工作，这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观点相矛盾，即由于低级别图像分割会随成像条件而有所不同，因此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。本提案的主要目标是解决这些问题并获得结论性结果，以坚定地建立或拒绝PI的初步结论。

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.