Open AccessImage feature extraction algorithm based on CUDA architecture: case study GFD and GCFD
Author(s) -
Bahri Haythem,
Sayadi Fatma,
Khemiri Randa,
Chouchene Marwa,
Atri Mohamed
Publication year - 2017
Publication title -
iet computers and digital techniques
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.219
H-Index - 46
eISSN - 1751-861X
pISSN - 1751-8601
DOI - 10.1049/iet-cdt.2016.0135
Subject(s) - cuda , computer science , graphics processing unit , context (archaeology) , feature extraction , feature (linguistics) , general purpose computing on graphics processing units , image processing , parallel computing , graphics , computational science , pattern recognition (psychology) , artificial intelligence , image (mathematics) , computer graphics (images) , paleontology , linguistics , philosophy , biology
Optimising computing times of applications is an increasingly important task in many different areas such as scientific and industrial applications. Graphics processing unit (GPU) is considered as one of the powerful engines for computationally demanding applications since it proposes a highly parallel architecture. In this context, the authors introduce an algorithm to optimise the computing time of feature extraction methods for the colour image. They choose generalised Fourier descriptor (GFD) and generalised colour Fourier descriptor (GCFD) models, as a method to extract the image feature for various applications such as colour object recognition in real‐time or image retrieval. They compare the computing time experimental results on central processing unit and GPU. They also present a case study of these experimental results descriptors using two platforms: a NVIDIA GeForce GT525M and a NVIDIA GeForce GTX480. Their experimental results demonstrate that the execution time can considerably be reduced until 34× for GFD and 56× for GCFD.