Computer vision algorithms and hardware implementations: A survey

By Xin Feng, You-Han Jiang, Xuejiao Yang, Ming Du, Xin Li. Integr., 2019

385 citations recorded by the source corpus.

Abstract 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.

Subfields: Computer Vision and Pattern Recognition, Electrical and Electronic Engineering

View Paper: https://www.semanticscholar.org/paper/e03aade1d4856e2fe9d2963ceb7de7bc4f3e13fd

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Computer Vision and Pattern Recognition

1. Thien Huynh-The
2. Mamoun Alazab
3. Baljit Singh Saini
4. Cherry Khosla

Citations in the corpus

• Metaverse for Healthcare: A Survey on Potential Applications, Challenges and Future Directions (2022, background): “essential parts of human visual systems [169], [170].”
• Enhancing Performance of Deep Learning Models with different Data Augmentation Techniques: A Survey (2020, background): “searchers to design algorithms for such visual perceptions [1].”