Robust Image and Video Matching and Its Application to View Integration
Date Issued
2015
Date
2015
Author(s)
Chen, Hsin-I
Abstract
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.
Subjects
computer vision
feature correspondence
co-segmentation
video matching
image warping
Type
thesis
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