cuRCD: Region covariance descriptor CUDA implementation


Asan M. A. , ÖZSOY A.

MULTIMEDIA TOOLS AND APPLICATIONS, vol.80, pp.19737-19751, 2021 (SCI-Expanded) identifier identifier

  • Publication Type: Article / Article
  • Volume: 80
  • Publication Date: 2021
  • Doi Number: 10.1007/s11042-021-10644-2
  • Journal Name: MULTIMEDIA TOOLS AND APPLICATIONS
  • Journal Indexes: Science Citation Index Expanded (SCI-EXPANDED), Scopus, FRANCIS, ABI/INFORM, Applied Science & Technology Source, Compendex, Computer & Applied Sciences, INSPEC, zbMATH
  • Page Numbers: pp.19737-19751
  • Hacettepe University Affiliated: Yes

Abstract

Region covariance is a robust feature descriptor that allows the use of even the simplest image features like intensity and gradient combined to form a well-performing descriptor for regions on the image. Beyond its robustness, it requires many identical heavy computations on different parts of input data which makes it a good candidate for parallel execution. In this manuscript, we present a real-time parallel implementation of the region covariance which, to our best knowledge, is the first in the literature. We experimented against existing implementations and achieved 6 times faster execution time over vectorized CPU parallel implementation that provides necessary speed up for real-time processing. Additionally, we improved the existing integral image calculation method on CUDA, reducing memory usage by 50%, achieving the fastest computation speed compared to exist- ing solutions, and improved the covariance matrix comparison metric by using a distance metric that is lightweight to compute and easy to implement.