Evaluation of real-time LBP computing in multiple architectures
Local binary pattern (LBP) is a texture operator that is used in several different computer vision applica- tions 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 opti- mizations that can be made to reach real-time performance on multiple platforms and their different available com- puting 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 process- ing units, mobile processors and a hybrid programming model image coprocessor. We have extended the evalua- tion of some of the solutions that can be found in previous work. In addition, we publish the source code of our implementations.
keywords: Local binary pattern, Mobile devices, GPGPU, Census transform, Implementation