cuRCD: Region covariance descriptor CUDA implementation

Asan M. A., ÖZSOY A.

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

  • Yayın Türü: Makale / Tam Makale
  • Cilt numarası: 80
  • Basım Tarihi: 2021
  • Doi Numarası: 10.1007/s11042-021-10644-2
  • Dergi Adı: MULTIMEDIA TOOLS AND APPLICATIONS
  • Derginin Tarandığı İndeksler: Science Citation Index Expanded (SCI-EXPANDED), Scopus, FRANCIS, ABI/INFORM, Applied Science & Technology Source, Compendex, Computer & Applied Sciences, INSPEC, zbMATH
  • Sayfa Sayıları: ss.19737-19751
  • Hacettepe Üniversitesi Adresli: Evet

Özet

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.