In this research, we have proposed the parallel GPU-accelerated algorithms to compute the Jacobi moments defined in a rectangular region with substantially improved computational efficiency and highly satisfied accuracy. In our algorithms, the parallel 3-D matrix multiplications are adopted to increase the computational efficiency, while the techniques of coalesced memory access, shared memory and heterogeneous computation are utilized to optimize the computing performance on a GPU platform. Our new GPU-accelerated system can provide any required moment computational accuracy without additional computing time. To verify the performance of our new parallel GPU-accelerated algorithms, we conducted a series of 2D and 3-D image analysis tests via the Jacobi moments with encouraging outcome, while the computing times for all these experimental tasks are in the level of milliseconds. It is expected that our new parallel GPU-accelerated algorithms will expedite the research in 3-D image analysis via the moment methods in the real-time range.

中文翻译：

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通过雅可比矩进行实时 3D 图像分析

在这项研究中，我们提出了并行 GPU 加速算法来计算矩形区域中定义的雅可比矩，大大提高了计算效率和高度令人满意的精度。在我们的算法中，采用并行3维矩阵乘法来提高计算效率，同时利用联合内存访问、共享内存和异构计算技术来优化GPU平台上的计算性能。我们新的 GPU 加速系统可以提供任何所需的时刻计算精度，而无需额外的计算时间。为了验证我们新的并行 GPU 加速算法的性能，我们通过雅可比矩进行了一系列 2D 和 3D 图像分析测试，取得了令人鼓舞的结果，而所有这些实验任务的计算时间都在毫秒级。预计我们新的并行 GPU 加速算法将通过实时范围内的矩方法加快 3D 图像分析的研究。