基于CUDA的立体视觉

在本文中，我们提出了一个基于GPU的加速方法，以加快计算量图像配准

统一设备架构（CUDA技术）。一种新颖的CUDA技术为基础的联合直方图计算方法介绍

在这个文件，该文件还对二维图像配准和其他应用程序的一般图形宝贵。此外，

1算法的改进，提出改进FMRIB广泛使用的线性图像注册工具

（调情）。虽然采取了额外的时间是通过应用该算法的改进，我们的实现表明，

能够执行一个完整的12个自由度（自由度）的两个脑容量图像配准在近35秒，

时间大约是10比本地调情执行速度更快。实验结果表明，我们的

算法的改进与实施能避免一些不当注册。-In this paper, we propose a GPU-based acceleration method to speed up volume image registration using Compute

Unified Device Architecture(CUDA). An novel CUDA-based method for joint histogram computation is introduced

in this paper, which is also valuable for 2D image registration and other general graphics applications. Additionally,

an algorithm refinement is proposed to improve the widely used FMRIB’s Linear Image Registration Tool

(FLIRT). Although extra time is taken by applying that algorithm refinements, our implementation showed the

ability to perform a full 12 DOF (Degrees of Freedom) registration of two brain volume image in nearly 35 seconds,

which is about 10 time faster than the native FLIRT implementation. Experimental results showed that our

implementation with algorithm refinement can avoid some mis-registration.