| Summary: | 博士 === 國立臺灣大學 === 資訊工程學研究所 === 104 === Establishing feature correspondences is a fundamental problem in many
image analysis tasks, and is required for a wide range of applications. Despite
the great applicability, two main difficulties hinder the advance in establishing
the correspondences of high quality: (1) low precision and (2) low recall. In
addition, how to establish dense mapping between videos is a more challenging
but less addressed in the community. In this dissertation, we introduce
a voting-based algorithm for image matching, and describe an inter-video
mapping framework to establish dense mapping between partial overlapping
videos. First, we propose an algorithm that is based on the Hough transform
to establish feature correspondences, which leads to speed-up in geometric
checking. We also develop an inverted Hough transform, and through an iterative
optimization process, we can enhance the quality of matching in both
precision and recall. Second, we integrate image co-segmentation into feature
matching and combine different descriptors, which can yield more accurate
and dense correspondences. Finally, we present a novel inter-video mapping
approach to align videos with small overlapping regions, and apply it to video
footages from two different dashcams installed on back vehicle. We show that
with our technique, it is able to locally adjust the shape of the unobstructed
view in the preceding vehicle so that its perspective and boundary could be
matched to that of the occluded region in its following vehicle, creating an impression as if the preceding vehicle is transparent thus increases drivers'' visibilities.
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