Computer vision algorithms and hardware implementations: A survey

Published

Journal Article (Review)

© 2019 The Authors The field of computer vision is experiencing a great-leap-forward development today. This paper aims at providing a comprehensive survey of the recent progress on computer vision algorithms and their corresponding hardware implementations. In particular, the prominent achievements in computer vision tasks such as image classification, object detection and image segmentation brought by deep learning techniques are highlighted. On the other hand, review of techniques for implementing and optimizing deep-learning-based computer vision algorithms on GPU, FPGA and other new generations of hardware accelerators are presented to facilitate real-time and/or energy-efficient operations. Finally, several promising directions for future research are presented to motivate further development in the field.

Full Text

Duke Authors

Cited Authors

  • Feng, X; Jiang, Y; Yang, X; Du, M; Li, X

Published Date

  • November 1, 2019

Published In

Volume / Issue

  • 69 /

Start / End Page

  • 309 - 320

International Standard Serial Number (ISSN)

  • 0167-9260

Digital Object Identifier (DOI)

  • 10.1016/j.vlsi.2019.07.005

Citation Source

  • Scopus