
Proceedings Paper
Understanding video transmission decisions in cloud based computer vision servicesFormat | Member Price | Non-Member Price |
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Paper Abstract
This paper presents a study about the effect of the quality of the input video source on the computer vision system
robustness and how to make use of the findings to create a framework generating a set of recommendation or rules for
researchers and developers in the field to use. The study is of high importance especially for cloud based computer vision
platforms where the transmission of raw uncompressed video is not possible, as such it is desired to have a sweet spot
where the usage of bandwidth is at optimal level while maintaining high recognition rate. Experimental results showed
that creating such rules is possible and beneficial to integrate in an end to end cloud based computer vision service.
Paper Details
Date Published: 27 February 2015
PDF: 9 pages
Proc. SPIE 9405, Image Processing: Machine Vision Applications VIII, 94050V (27 February 2015); doi: 10.1117/12.2083563
Published in SPIE Proceedings Vol. 9405:
Image Processing: Machine Vision Applications VIII
Edmund Y. Lam; Kurt S. Niel, Editor(s)
PDF: 9 pages
Proc. SPIE 9405, Image Processing: Machine Vision Applications VIII, 94050V (27 February 2015); doi: 10.1117/12.2083563
Show Author Affiliations
Nijad Anabtawi, Arizona State Univ. (United States)
Rony M. Ferzli, Arizona State Univ. (United States)
Published in SPIE Proceedings Vol. 9405:
Image Processing: Machine Vision Applications VIII
Edmund Y. Lam; Kurt S. Niel, Editor(s)
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