Abstract
Catadioptric omnidirectional view sensors have found increasing adoption in various robotic and surveillance applications due to their 360° field of view. However, the inherent distortion caused by the sensors prevents their direct utilisations using existing image processing techniques developed for perspective images. Therefore, a correction processing known as "unwrapping" is commonly performed. However, the unwrapping process incurs additional computational loads on central processing units. In this paper, a method to reduce this burden in the computation is investigated by exploiting the parallelism of graphical processing units (GPUs) based on the Compute Unified Device Architecture (CUDA). More specifically, we first introduce a general approach of parallelisation to the said process. Then, a series of adaptations to the CUDA platform is proposed to enable an optimised usage of the hardware platform. Finally, the performances of the unwrapping function were evaluated on a high-end and low-end GPU to demonstrate the effectiveness of the parallelisation approach.
| Original language | English |
|---|---|
| Pages (from-to) | 1-15 |
| Number of pages | 15 |
| Journal | Journal of Real-Time Image Processing |
| DOIs | |
| Publication status | Accepted/In press - 2013 |
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Keywords
- Bilinear interpolation
- CUDA
- GPU
- Image unwrapping
- Omnidirectional sensor
- Parallelisation
ASJC Scopus subject areas
- Information Systems
Cite this
Accelerated catadioptric omnidirectional view image unwrapping processing using GPU parallelisation. / Chong, Nguan Soon; Wong, M. L Dennis; Kho, Yau Hee.
In: Journal of Real-Time Image Processing, 2013, p. 1-15.Research output: Contribution to journal › Article
}
TY - JOUR
T1 - Accelerated catadioptric omnidirectional view image unwrapping processing using GPU parallelisation
AU - Chong, Nguan Soon
AU - Wong, M. L Dennis
AU - Kho, Yau Hee
PY - 2013
Y1 - 2013
N2 - Catadioptric omnidirectional view sensors have found increasing adoption in various robotic and surveillance applications due to their 360° field of view. However, the inherent distortion caused by the sensors prevents their direct utilisations using existing image processing techniques developed for perspective images. Therefore, a correction processing known as "unwrapping" is commonly performed. However, the unwrapping process incurs additional computational loads on central processing units. In this paper, a method to reduce this burden in the computation is investigated by exploiting the parallelism of graphical processing units (GPUs) based on the Compute Unified Device Architecture (CUDA). More specifically, we first introduce a general approach of parallelisation to the said process. Then, a series of adaptations to the CUDA platform is proposed to enable an optimised usage of the hardware platform. Finally, the performances of the unwrapping function were evaluated on a high-end and low-end GPU to demonstrate the effectiveness of the parallelisation approach.
AB - Catadioptric omnidirectional view sensors have found increasing adoption in various robotic and surveillance applications due to their 360° field of view. However, the inherent distortion caused by the sensors prevents their direct utilisations using existing image processing techniques developed for perspective images. Therefore, a correction processing known as "unwrapping" is commonly performed. However, the unwrapping process incurs additional computational loads on central processing units. In this paper, a method to reduce this burden in the computation is investigated by exploiting the parallelism of graphical processing units (GPUs) based on the Compute Unified Device Architecture (CUDA). More specifically, we first introduce a general approach of parallelisation to the said process. Then, a series of adaptations to the CUDA platform is proposed to enable an optimised usage of the hardware platform. Finally, the performances of the unwrapping function were evaluated on a high-end and low-end GPU to demonstrate the effectiveness of the parallelisation approach.
KW - Bilinear interpolation
KW - CUDA
KW - GPU
KW - Image unwrapping
KW - Omnidirectional sensor
KW - Parallelisation
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U2 - 10.1007/s11554-013-0390-x
DO - 10.1007/s11554-013-0390-x
M3 - Article
SP - 1
EP - 15
JO - Journal of Real-Time Image Processing
JF - Journal of Real-Time Image Processing
SN - 1861-8200
ER -