Detail publikace

GP-GPU Implementation of the "Local Rank Differences" Image Feature

HEROUT, A. JOŠTH, R. ZEMČÍK, P. HRADIŠ, M.

Originální název

GP-GPU Implementation of the "Local Rank Differences" Image Feature

Anglický název

GP-GPU Implementation of the "Local Rank Differences" Image Feature

Jazyk

en

Originální abstrakt

A currently popular trend in object detection and pattern recognition is usage of statistical classifiers, namely AdaBoost and its modifications. The speed performance of these classifiers largely depends on the low level image features they are using: both on the amount of information the feature provides and the processor time of its evaluation. Local Rank Di erences is an image feature that is alternative to commonly used haar wavelets. It is suitable for implementation in programmable (FPGA) or specialized (ASIC) hardware, but - as this paper shows - it performs very well on graphics hardware (GPU) used in general purpose manner (GPGPU, namely CUDA in this case) as well. The paper discusses the LRD features and their properties, describes an experimental implementation of the LRD in graphics hardware using CUDA, presents its empirical performance measures compared to alternative approaches, suggests several notes on practical usage of LRD and proposes directions for future work.

Anglický abstrakt

A currently popular trend in object detection and pattern recognition is usage of statistical classifiers, namely AdaBoost and its modifications. The speed performance of these classifiers largely depends on the low level image features they are using: both on the amount of information the feature provides and the processor time of its evaluation. Local Rank Di erences is an image feature that is alternative to commonly used haar wavelets. It is suitable for implementation in programmable (FPGA) or specialized (ASIC) hardware, but - as this paper shows - it performs very well on graphics hardware (GPU) used in general purpose manner (GPGPU, namely CUDA in this case) as well. The paper discusses the LRD features and their properties, describes an experimental implementation of the LRD in graphics hardware using CUDA, presents its empirical performance measures compared to alternative approaches, suggests several notes on practical usage of LRD and proposes directions for future work.

Dokumenty

BibTex


@inproceedings{BUT29556,
  author="Adam {Herout} and Radovan {Jošth} and Pavel {Zemčík} and Michal {Hradiš}",
  title="GP-GPU Implementation of the "Local Rank Differences" Image Feature",
  annote="A currently popular trend in object detection and pattern
recognition is usage of statistical classifiers, namely AdaBoost and its
modifications. The speed performance of these classifiers largely depends on the
low level image features they are using: both on the amount of information the
feature provides and the processor time of its evaluation. Local Rank Dierences
is an image feature that is alternative to commonly used haar wavelets. It is
suitable for implementation in programmable (FPGA) or specialized (ASIC)
hardware, but - as this paper shows - it performs very well on graphics hardware
(GPU) used in general purpose manner (GPGPU, namely CUDA in this case) as well.
The paper discusses the LRD features and their properties, describes an
experimental implementation of the LRD in graphics hardware using CUDA, presents
its empirical performance measures compared to alternative approaches, suggests
several notes on practical usage of LRD and proposes directions for future work.",
  address="Springer Verlag",
  booktitle="Proceedings of International Conference on Computer Vision and Graphics 2008",
  chapter="29556",
  edition="Lecture Notes in Computer Science",
  howpublished="print",
  institution="Springer Verlag",
  year="2008",
  month="november",
  pages="1--11",
  publisher="Springer Verlag",
  type="conference paper"
}