Publication detail

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

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

Original Title

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

English Title

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

Type

conference paper

Language

en

Original Abstract

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.

English abstract

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.

Keywords

pattern recognition, adaptive boosting, AdaBoost, WaldBoost, image features, LRD, Local Rank Differences, hardware acceleration

RIV year

2008

Released

12.11.2008

Publisher

Springer Verlag

Location

Heidelberg

ISBN

978-3-642-02344-6

Book

Proceedings of International Conference on Computer Vision and Graphics 2008

Edition

Lecture Notes in Computer Science

Edition number

NEUVEDEN

Pages from

1

Pages to

11

Pages count

11

URL

Documents

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"
}