Object oriented framework for real-time image processing on GPU

Nicolas Seiller; Williem; Nitin Singhal; In Kyu Park

doi:10.1007/s11042-013-1440-x

Object oriented framework for real-time image processing on GPU

Nicolas Seiller, Williem, Nitin Singhal, In Kyu Park

Research output: Contribution to journal › Article › peer-review

2 Scopus citations

Abstract

General purpose computation on graphics processing unit (GPGPU) provides a significant gain in terms of the processing time compared with CPU. Images are particularly good subjects for massive parallel implementations on GPU. Thus, the processing time can be improved for computer vision and image/video processing algorithms. However, GPGPU has a fairly complex integration process in a framework and they evolve very rapidly. In this paper, we present a framework that provides all the desired primitives related to GPGPU-based image processing algorithms, which makes it easy and straightforward for the user to exploit. The proposed framework is object-oriented, and it utilizes design patterns. The user can benefit from all the advantages of object-oriented programming, such as code reusability/extensibility, flexibility, information hiding, and complexity hiding. This makes it possible to rapidly integrate new technologies and functionality as they appear.

Original language	English
Pages (from-to)	2347-2368
Number of pages	22
Journal	Multimedia Tools and Applications
Volume	70
Issue number	3
DOIs	https://doi.org/10.1007/s11042-013-1440-x
State	Published - Jun 2014

Bibliographical note

Funding Information:
Acknowledgements This work was supported by the Industrial Strategic Technology Development Program (10041664, The Development of Fusion Processor based on Multi-Shader GPU) funded by the Ministry of Knowledge Economy (MKE, Korea).

Keywords

Computer vision
Design patterns
GPGPU
Image/video processing
Object-oriented

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Cite this

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abstract = "General purpose computation on graphics processing unit (GPGPU) provides a significant gain in terms of the processing time compared with CPU. Images are particularly good subjects for massive parallel implementations on GPU. Thus, the processing time can be improved for computer vision and image/video processing algorithms. However, GPGPU has a fairly complex integration process in a framework and they evolve very rapidly. In this paper, we present a framework that provides all the desired primitives related to GPGPU-based image processing algorithms, which makes it easy and straightforward for the user to exploit. The proposed framework is object-oriented, and it utilizes design patterns. The user can benefit from all the advantages of object-oriented programming, such as code reusability/extensibility, flexibility, information hiding, and complexity hiding. This makes it possible to rapidly integrate new technologies and functionality as they appear.",

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Object oriented framework for real-time image processing on GPU

Abstract

Bibliographical note

Keywords

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