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Evaluation of real-time LBP computing in multiple architectures

Tutkimustuotosvertaisarvioitu

Standard

Evaluation of real-time LBP computing in multiple architectures. / Bordallo López, Miguel; Nieto, Alejandro; Boutellier, Jani; Hannuksela, Jari; Silvén, Olli.

julkaisussa: Journal of Real-Time Image Processing, Vuosikerta 13, Nro 2, 06.2017.

Tutkimustuotosvertaisarvioitu

Harvard

Bordallo López, M, Nieto, A, Boutellier, J, Hannuksela, J & Silvén, O 2017, 'Evaluation of real-time LBP computing in multiple architectures' Journal of Real-Time Image Processing, Vuosikerta. 13, Nro 2. https://doi.org/10.1007/s11554-014-0410-5

APA

Bordallo López, M., Nieto, A., Boutellier, J., Hannuksela, J., & Silvén, O. (2017). Evaluation of real-time LBP computing in multiple architectures. Journal of Real-Time Image Processing, 13(2). https://doi.org/10.1007/s11554-014-0410-5

Vancouver

Bordallo López M, Nieto A, Boutellier J, Hannuksela J, Silvén O. Evaluation of real-time LBP computing in multiple architectures. Journal of Real-Time Image Processing. 2017 kesä;13(2). https://doi.org/10.1007/s11554-014-0410-5

Author

Bordallo López, Miguel ; Nieto, Alejandro ; Boutellier, Jani ; Hannuksela, Jari ; Silvén, Olli. / Evaluation of real-time LBP computing in multiple architectures. Julkaisussa: Journal of Real-Time Image Processing. 2017 ; Vuosikerta 13, Nro 2.

Bibtex - Lataa

@article{e3f91e5cc7c542e49434da54253a37bd,
title = "Evaluation of real-time LBP computing in multiple architectures",
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.",
keywords = "Census transform, GPGPU, Implementation, Local binary pattern, Mobile devices",
author = "{Bordallo L{\'o}pez}, Miguel and Alejandro Nieto and Jani Boutellier and Jari Hannuksela and Olli Silv{\'e}n",
year = "2017",
month = "6",
doi = "10.1007/s11554-014-0410-5",
language = "English",
volume = "13",
journal = "Journal of Real-Time Image Processing",
issn = "1861-8200",
publisher = "Springer Verlag",
number = "2",

}

RIS (suitable for import to EndNote) - Lataa

TY - JOUR

T1 - Evaluation of real-time LBP computing in multiple architectures

AU - Bordallo López, Miguel

AU - Nieto, Alejandro

AU - Boutellier, Jani

AU - Hannuksela, Jari

AU - Silvén, Olli

PY - 2017/6

Y1 - 2017/6

N2 - 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.

AB - 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.

KW - Census transform

KW - GPGPU

KW - Implementation

KW - Local binary pattern

KW - Mobile devices

UR - http://www.scopus.com/inward/record.url?scp=84896410683&partnerID=8YFLogxK

U2 - 10.1007/s11554-014-0410-5

DO - 10.1007/s11554-014-0410-5

M3 - Article

VL - 13

JO - Journal of Real-Time Image Processing

JF - Journal of Real-Time Image Processing

SN - 1861-8200

IS - 2

ER -