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Low Complexity Optical Flow Using Neighbor-Guided Semi-Global Matching

Abstract Many real-time vision applications require accurate estimation of optical flow. This problem is quite challenging due to extremely high computation and memory requirements. This thesis focuses on designing low complexity dense optical flow algorithms.

First, a new method for optical flow that is based on Semi-Global Matching (SGM), a popular dynamic programming algorithm for stereo vision, is presented. In SGM, the disparity of each pixel is calculated by aggregating local matching costs over the entire image to resolve local ambiguity in texture-less and occluded regions. The proposed method, Neighbor-Guided Semi-Global Matching (NG-fSGM) achieves significantly less complexity compared to SGM, by 1) operating on a subset of the search spa... (more)
Created Date 2017
Contributor Xiang, Jiang (Author) / Chakrabarti, Chaitali (Advisor) / Karam, Lina (Committee member) / Kim, Hun Seok (Committee member) / Arizona State University (Publisher)
Subject Computer engineering / Computer Vision / Low Complexity / Optical Flow / Parallel / Semi-Global Matching
Type Masters Thesis
Extent 71 pages
Language English
Reuse Permissions All Rights Reserved
Note Masters Thesis Computer Science 2017
Collaborating Institutions Graduate College / ASU Library
Additional Formats MODS / OAI Dublin Core / RIS

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Description Dissertation/Thesis