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Evaluation of real-time LBP computing in multiple architectures

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Abstract

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.

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Authors and Affiliations

  1. Center for Machine Vision Research, University of Oulu, 90570, Oulu, Finland

    Miguel Bordallo López, Jani Boutellier, Jari Hannuksela & Olli Silvén

  2. University of Santiago de Compostela, Centro de Investigación en Tecnoloxías da Información (CITIUS), Santiago de Compostela, Spain

    Alejandro Nieto

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  1. Miguel Bordallo López
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Correspondence to Miguel Bordallo López.

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Bordallo López, M., Nieto, A., Boutellier, J. et al. Evaluation of real-time LBP computing in multiple architectures. J Real-Time Image Proc 13, 375–396 (2017). https://doi.org/10.1007/s11554-014-0410-5

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