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A vision for computer vision: emerging technologies

Published:24 July 2016Publication History

ABSTRACT

Computer vision is a rapidly evolving discipline. It includes methods for acquiring, processing, and understanding still images and video to model, replicate, and sometimes, exceed human vision and perform useful tasks.

Computer vision will be commonly used for a broad range of services in upcoming devices, and implemented in everything from movies, smartphones, cameras, drones and more. Demand for CV is driving the evolution of image sensors, mobile processors, operating systems, application software, and device form factors in order to meet the needs of upcoming applications and services that benefit from computer vision. The resulting impetus means rapid advancements in:

• visual computing performance

• object recognition effectiveness

• speed and responsiveness

• power efficiency

• video image quality improvement

• real-time 3D reconstruction

• pre-scanning for movie animation

• image stabilization

• immersive experiences

• and more...

Comprised of innovation leaders of computer vision, this panel will cover recent developments, as well as how CV will be enabled and used in 2016 and beyond.

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      • Published in

        cover image ACM Conferences
        SIGGRAPH '16: ACM SIGGRAPH 2016 Panels
        July 2016
        4 pages
        ISBN:9781450343756
        DOI:10.1145/2927383

        Copyright © 2016 Owner/Author

        Permission to make digital or hard copies of part or all of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for third-party components of this work must be honored. For all other uses, contact the Owner/Author.

        Publisher

        Association for Computing Machinery

        New York, NY, United States

        Publication History

        • Published: 24 July 2016

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        Acceptance Rates

        Overall Acceptance Rate1,822of8,601submissions,21%

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