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1 June 2020 Operator overloading for cv::UMat converted to equivalent function calls at compile time
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Proceedings Volume 11515, International Workshop on Advanced Imaging Technology (IWAIT) 2020; 115152K (2020) https://doi.org/10.1117/12.2566938
Event: International Workshop on Advanced Imaging Technologies 2020 (IWAIT 2020), 2020, Yogyakarta, Indonesia
Abstract
OpenCV is an open source programming library for computer vision and image processing, and has been used worldwide in industry and academia as a de facto standard for more than 10 years, from large-scale computers to embedded devices such as smartphones. OpenCV has image data representation classes cvMat and cvUMat as image processing APIs, and the latter can implement parallel computation using heterogeneous computing such as the OpenCL framework. Operator overloading is a technique that enables intuitive programming using arithmetic operations and assignment operators, and is defined in cvMat. On the other hand, operator overloading is not defined in cvUMat, and it is necessary to call functions appropriately. Therefore, programming using cvUMat is difficult, and there is a problem that the code is not compatible with cvMat using operator overloading. In addition, cvMat operator overloading has a problem of extra memory reallocation at runtime, so it is not appropriate to apply it directly to cvUMat. Therefore, in this paper, we propose a method to realize operator overloading that does not require extra memory reallocation at runtime and can be equivalently converted to cvUMat function calls at compile time. This method enables programming that achieves intuitive operator overloading without any overhead at runtime.
© (2020) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Yusuke Kameda, Yoshihiro Maeda, and Takayuki Hamamoto "Operator overloading for cv::UMat converted to equivalent function calls at compile time", Proc. SPIE 11515, International Workshop on Advanced Imaging Technology (IWAIT) 2020, 115152K (1 June 2020); https://doi.org/10.1117/12.2566938
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