Object oriented framework for real-time image processing on GPU

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.