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首页> 外文期刊>Journal of Real-Time Image Processing >Evaluation of real-time LBP computing in multiple architectures
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Evaluation of real-time LBP computing in multiple architectures

机译：评估多种架构中的实时LBP计算

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摘要

Local binary pattern (LBP) is a texture operator that is used in several different computer vision applications requiring, in many cases, real-time operation in multiple computing platforms. The irruption of new video standards has increased the typical resolutions and frame rates, which need considerable computational performance. Since LBP is essentially a pixel operator that scales with image size, typical straightforward implementations are usually insufficient to meet these requirements. To identify the solutions that maximize the performance of the real-time LBP extraction, we compare a series of different implementations in terms of computational performance and energy efficiency, while analyzing the different optimizations that can be made to reach real-time performance on multiple platforms and their different available computing resources. Our contribution addresses the extensive survey of LBP implementations in different platforms that can be found in the literature. To provide for a more complete evaluation, we have implemented the LBP algorithms in several platforms, such as graphics processing units, mobile processors and a hybrid programming model image coprocessor. We have extended the evaluation of some of the solutions that can be found in previous work. In addition, we publish the source code of our implementations.
机译：本地二进制模式（LBP）是一种纹理运算符，用于多种不同的计算机视觉应用程序中，在许多情况下，它们需要在多个计算平台中进行实时操作。新视频标准的推出提高了典型的分辨率和帧速率，这需要相当大的计算性能。由于LBP本质上是随图像大小缩放的像素运算符，因此典型的简单实现通常不足以满足这些要求。为了确定使实时LBP提取性能最大化的解决方案，我们在计算性能和能效方面比较了一系列不同的实现，同时分析了为实现多个平台上的实时性能而进行的不同优化。及其不同的可用计算资源。我们的贡献致力于在文献中找到的在不同平台上对LBP实现的广泛调查。为了提供更完整的评估，我们已在多个平台上实现了LBP算法，例如图形处理单元，移动处理器和混合编程模型图像协处理器。我们扩展了对以前工作中可以找到的一些解决方案的评估。此外，我们还发布了实现的源代码。

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