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Journal of Electronic Imaging

Dense depth maps from correspondences derived from perceived motion
Author(s): Richard Kirby; Ross Whitaker
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Paper Abstract

Many computer vision applications require finding corresponding points between images and using the corresponding points to estimate disparity. Today’s correspondence finding algorithms primarily use image features or pixel intensities common between image pairs. Some 3-D computer vision applications, however, do not produce the desired results using correspondences derived from image features or pixel intensities. Two examples are the multimodal camera rig and the center region of a coaxial camera rig. We present an image correspondence finding technique that aligns pairs of image sequences using optical flow fields. The optical flow fields provide information about the structure and motion of the scene, which are not available in still images but can be used in image alignment. We apply the technique to a dual focal length stereo camera rig consisting of a visible light—infrared camera pair and to a coaxial camera rig. We test our method on real image sequences and compare our results with the state-of-the-art multimodal and structure from motion (SfM) algorithms. Our method produces more accurate depth and scene velocity reconstruction estimates than the state-of-the-art multimodal and SfM algorithms.

Paper Details

Date Published: 25 February 2017
PDF: 13 pages
J. Electron. Imag. 26(1) 013026 doi: 10.1117/1.JEI.26.1.013026
Published in: Journal of Electronic Imaging Volume 26, Issue 1
Show Author Affiliations
Richard Kirby, The Univ. of Utah (United States)
Ross Whitaker, The Univ. of Utah (United States)

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