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

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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.