GPU Implementation of the Affine Transform for 3D Image Registration

D. Crookes, K. Boyle, P. Miller, C. Gillan
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引用次数: 6

Abstract

Recent developments in 3D low-light level CCD (L3CCD) image capture have resulted in vast volumes of data being produced in real time which require image registration. The amount of data involved means that acceleration of the processing is essential. One of the key steps in one iterative registration algorithm is the application of an affine transform to all the planes of a 3D image. This paper presents details and performance results for a number of parallelized implementations of the affine transform on the NVIDIA 8800 GPU series, and shows that the transform runs 128 times faster on the GPU than a C++ version on a PC, or 54 times faster when data transfer between the GPU and the host PC is included.
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三维图像配准仿射变换的GPU实现
三维微光CCD (L3CCD)图像捕获技术的最新发展导致了大量的实时数据产生,这些数据需要图像配准。所涉及的数据量意味着加速处理是必不可少的。迭代配准算法的关键步骤之一是对三维图像的所有平面进行仿射变换。本文介绍了在NVIDIA 8800系列GPU上并行实现仿射变换的细节和性能结果,并表明该变换在GPU上的运行速度比PC上的c++版本快128倍,当包括GPU和主机PC之间的数据传输时,速度快54倍。
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