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首页> 外文期刊>Journal of Real-Time Image Processing >Fast computation of 2D and 3D Legendre moments using multi-core CPUs and GPU parallel architectures
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Fast computation of 2D and 3D Legendre moments using multi-core CPUs and GPU parallel architectures

机译：使用多核CPU和GPU并行架构快速计算2D和3D Legendre矩

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摘要

Legendre moments and their invariants for 2D and 3D image/objects are widely used in image processing, computer vision, and pattern recognition applications. Reconstruction of digital images by nature required higher-order moments to get high-quality reconstructed images. Different applications such as classification of bacterial contamination images utilize high-order moments for feature extraction phase. For big size images and 3D objects, Legendre moments computation is very time-consuming and compute-intensive. This problem limits the use of Legendre moments and makes them impractical for real-time applications. Multi-core CPUs and GPUs are powerful processing parallel architectures. In this paper, new parallel algorithms are proposed to speed up the process of exact Legendre moments computation for 2D and 3D image/objects. These algorithms utilize multi-core CPUs and GPUs parallel architectures where each pixel/voxel of the input digital image/object can be handled independently. A detailed profile analysis is presented where the weight of each part of the entire computational process is evaluated. In addition, we contributed to the parallel 2D/3D Legendre moments by: (1) a modification of the traditional exact Legendre moment algorithm to better fit the parallel architectures, (2) we present the first parallel CPU implementation of Legendre moment, and (3) we present the first parallel CPU and GPU acceleration of the reconstruction phase of the Legendre moments. A set of numerical experiments with different gray-level images are performed. The obtained results clearly show a very close to optimal parallel gain. The extreme reduction in execution times, especially for 8-core CPUs and GPUs, makes the parallel exact 2D/3D Legendre moments suitable for real-time applications.
机译：2D和3D图像/对象的Legendre矩及其不变式已广泛用于图像处理，计算机视觉和模式识别应用中。本质上，数字图像的重建需要更高阶的矩才能获得高质量的重建图像。不同的应用（例如细菌污染图像的分类）将高阶矩用于特征提取阶段。对于大尺寸图像和3D对象，Legendre矩计算非常耗时且计算量很大。此问题限制了Legendre矩的使用，并使其不适用于实时应用。多核CPU和GPU是强大的处理并行体系结构。在本文中，提出了新的并行算法以加快2D和3D图像/对象的精确Legendre矩计算过程。这些算法利用多核CPU和GPU并行架构，其中输入数字图像/对象的每个像素/体素可以独立处理。提出了详细的配置文件分析，其中评估了整个计算过程中每个部分的权重。此外，我们还通过以下方式为并行2D / 3D Legendre矩做出了贡献：（1）对传统的精确Legendre矩算法的修改，以更好地适应并行体系结构；（2）我们提出了Legendre矩的第一个并行CPU实现，并且（ 3）我们提出了Legendre瞬间重建阶段的第一个并行CPU和GPU加速。进行了一组具有不同灰度图像的数值实验。所获得的结果清楚地表明非常接近最佳的并行增益。执行时间的极大减少，尤其是对于8核CPU和GPU而言，使并行的精确2D / 3D Legendre时刻适合实时应用。

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