Stereo Depth Estimation Algorithms: Difference between revisions
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imported>Student2017 Created page with '== Introduction == == Background == == Methods == == Results == == Conclusions == == Appendix ==' |
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== Introduction == | == Introduction == | ||
The brain is a fantastic super computer with unmatched 3D object recognition and depth estimation. The ocular system has over 120 million rods and 6 million cone which send signals that are funneled into of our visual cortex resulting in our capability to see. The massive amounts of data and processing power of our brain makes it a fascinating phenomena. Stereo depth is a major part of this machinery and in this paper we delve into the topic by investigating existing computer algorithms. Depth extraction from stereo estimation boils down to Triangulation. The papers seeks to explain three primary approaches and details surrounding stereo depth extraction. | |||
== Background == | == Background == | ||
Revision as of 06:01, 12 December 2017
Introduction
The brain is a fantastic super computer with unmatched 3D object recognition and depth estimation. The ocular system has over 120 million rods and 6 million cone which send signals that are funneled into of our visual cortex resulting in our capability to see. The massive amounts of data and processing power of our brain makes it a fascinating phenomena. Stereo depth is a major part of this machinery and in this paper we delve into the topic by investigating existing computer algorithms. Depth extraction from stereo estimation boils down to Triangulation. The papers seeks to explain three primary approaches and details surrounding stereo depth extraction.