Abstract
We are witnessing a proliferation of massive visual data. Unfortunately, scaling existing computer vision algorithms to large datasets leaves researchers repeatedly solving the same algorithmic, logistical, and infrastructural problems. Our goal is to democratize computer vision; one should not have to be a computer vision, big data, and distributed computing expert to have access to state-of-the-art distributed computer vision algorithms. We present CloudCV, a comprehensive system to provide access to state-of-the-art distributed computer vision algorithms as a cloud service through a web interface and APIs.
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Acknowledgments
This work was partially supported by the Virginia Tech ICTAS JFC Award, and the National Science Foundation CAREER award IIS-1350553. The views and conclusions contained herein are those of the authors and should not be interpreted as necessarily representing the official policies or endorsements, either expressed or implied, of the U.S. Government or any sponsor.
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Agrawal, H. et al. (2015). CloudCV: Large-Scale Distributed Computer Vision as a Cloud Service. In: Hua, G., Hua, XS. (eds) Mobile Cloud Visual Media Computing. Springer, Cham. https://doi.org/10.1007/978-3-319-24702-1_11
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DOI: https://doi.org/10.1007/978-3-319-24702-1_11
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