Malaysian Journal of Mathematical Sciences, May 2022, Vol. 16, No. 2
Performance of CPU-GPU Parallel Architecture on Segmentation and Geometrical Features Extraction of Malaysian Herb Leaves
Hadi, N. A., Halim, S. A., Lazim, N. S. M., and Alias, N.
Corresponding Email: normi@fskm.uitm.edu.my
Received date: 15 July 2021
Accepted date: 22 April 2022
Abstract:
Image recognition includes the segmentation of image boundary, geometrical features extraction,
and classification is used in the particular image database development. The ultimate challenge
in this task is it is computationally expensive. This paper highlighted a CPU-GPU architecture
for image segmentation and features extraction processes of 125 images of Malaysian
Herb Leaves. Two (2) GPUs and three (3) kernels are utilized in the CPU-GPU platform using
MATLAB software. Each of herb image has pixel dimensions 16161080. The segmentation
process uses the Sobel operator, which is then used to extract the boundary points. Finally,
seven (7) geometrical features are extracted for each image. Both processes are first executed
on the CPU alone before bringing it onto a CPU-GPU platform to accelerate the computational
performance. The results show that the developed CPU-GPU platformhas accelerated the computation
process by a factor of 4.13. However, the efficiency shows a decline, which suggests
that the processors utilization must be improved in the future to balance the load distribution.
Keywords: CPU-GPU; Parallel computing; Malaysian herb leaves; features extraction; image segmentation.
