Computer vision algorithms and hardware implementations: A survey

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.

Duke Scholars

Author Xin Li Electrical and Computer Engineering