Cover Image

Performance of CPU_GPU parallel architecture on segmentation and geometrical features extraction of Malaysian herb leaves

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 seg...

Full description

Saved in:
Bibliographic Details
Main Authors: Hadi, N. A., Halim, S. A., Lazim, N. S. M., Alias, N.
Format: Article
Language:English
Published: Universiti Putra Malaysia 2022
Subjects:
Q Science (General)
Online Access:http://eprints.utm.my/id/eprint/98762/1/NAlias2022_PerformanceofCPU-GPUParallelArchitecture.pdf
http://eprints.utm.my/id/eprint/98762/
http://dx.doi.org/10.47836/mjms.16.2.12
Tags: Add Tag
No Tags, Be the first to tag this record!
id my.utm.98762
record_format eprints
spelling my.utm.987622023-02-02T08:30:59Z http://eprints.utm.my/id/eprint/98762/ 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. Alias, N. Q Science (General) 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. Universiti Putra Malaysia 2022 Article PeerReviewed application/pdf en http://eprints.utm.my/id/eprint/98762/1/NAlias2022_PerformanceofCPU-GPUParallelArchitecture.pdf Hadi, N. A. and Halim, S. A. and Lazim, N. S. M. and Alias, N. (2022) Performance of CPU_GPU parallel architecture on segmentation and geometrical features extraction of Malaysian herb leaves. Malaysian Journal of Mathematical Sciences, 16 (2). pp. 363-377. ISSN 1823-8343 http://dx.doi.org/10.47836/mjms.16.2.12 DOI: 10.47836/mjms.16.2.12
institution Universiti Teknologi Malaysia
building UTM Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Teknologi Malaysia
content_source UTM Institutional Repository
url_provider http://eprints.utm.my/
language English
topic Q Science (General)
spellingShingle Q Science (General)
Hadi, N. A.
Halim, S. A.
Lazim, N. S. M.
Alias, N.
Performance of CPU_GPU parallel architecture on segmentation and geometrical features extraction of Malaysian herb leaves
description 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.
format Article
author Hadi, N. A.
Halim, S. A.
Lazim, N. S. M.
Alias, N.
author_facet Hadi, N. A.
Halim, S. A.
Lazim, N. S. M.
Alias, N.
author_sort Hadi, N. A.
title Performance of CPU_GPU parallel architecture on segmentation and geometrical features extraction of Malaysian herb leaves
title_short Performance of CPU_GPU parallel architecture on segmentation and geometrical features extraction of Malaysian herb leaves
title_full Performance of CPU_GPU parallel architecture on segmentation and geometrical features extraction of Malaysian herb leaves
title_fullStr Performance of CPU_GPU parallel architecture on segmentation and geometrical features extraction of Malaysian herb leaves
title_full_unstemmed Performance of CPU_GPU parallel architecture on segmentation and geometrical features extraction of Malaysian herb leaves
title_sort performance of cpu_gpu parallel architecture on segmentation and geometrical features extraction of malaysian herb leaves
publisher Universiti Putra Malaysia
publishDate 2022
url http://eprints.utm.my/id/eprint/98762/1/NAlias2022_PerformanceofCPU-GPUParallelArchitecture.pdf
http://eprints.utm.my/id/eprint/98762/
http://dx.doi.org/10.47836/mjms.16.2.12
_version_ 1758578016050479104
score 13.211869

Similar Items