New strategies for generating panoramic images for imperfect image series

Abstract

In this thesis, we re-examine the established panoramic image construction pipeline and introduce three strategies that are useful when the input images are less than ideal. First, we introduce a method to compute a more flexible geometric alignment when the imaged scene contains two dominant planes. We show that this method, termed dual-homography alignment, is able to stitch photographs that current state-of-the-art methods cannot. Second, we show that instead of selecting an alignment based on the geometric fit of matched points between images, we can use the seam-cut step to evaluate several possible geometric transformations and select the alignment that gives the most ``perceptually optimal'' seam. We demonstrate several cases where this seam-driven image stitching is able to produce better results than existing methods. Lastly, for examples when the automatic image stitching procedure fails, we introduce an interactive method that provides the user tools to manually edit the seam-cuts and/or geometric alignment in a local manner. These three works collectively provide solutions for image stitching using imperfect input images while targeting on different parts of the image stitching pipeline.