25 February 2017 Dense depth maps from correspondences derived from perceived motion
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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.
© 2017 SPIE and IS&T
Richard Kirby, Richard Kirby, Ross Whitaker, Ross Whitaker, } "Dense depth maps from correspondences derived from perceived motion," Journal of Electronic Imaging 26(1), 013026 (25 February 2017). https://doi.org/10.1117/1.JEI.26.1.013026 . Submission: Received: 7 December 2016; Accepted: 9 February 2017
Received: 7 December 2016; Accepted: 9 February 2017; Published: 25 February 2017

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