Detail publikace

"Local Rank Differences" Image Feature Implemented on GPU

Originální název

"Local Rank Differences" Image Feature Implemented on GPU

Anglický název

"Local Rank Differences" Image Feature Implemented on GPU

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 executional time of its evaluation. Local Rank Differences 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) as well. The paper discusses the LRD features and their properties, describes an experimental implementation of LRD in graphics hardware, presents its empirical performance measures compared to alternative approaches and 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 executional time of its evaluation. Local Rank Differences 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) as well. The paper discusses the LRD features and their properties, describes an experimental implementation of LRD in graphics hardware, presents its empirical performance measures compared to alternative approaches and suggests several notes on practical usage of LRD and proposes directions for future work.

BibTex


@inproceedings{BUT29656,
  author="Lukáš {Polok} and Adam {Herout} and Pavel {Zemčík} and Michal {Hradiš} and Roman {Juránek} and Radovan {Jošth}",
  title=""Local Rank Differences" Image Feature Implemented on GPU",
  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
executional time of its evaluation. Local Rank Differences 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) as well.
The paper discusses the LRD features and their properties, describes an
experimental implementation of LRD in graphics hardware, presents its empirical
performance measures compared to alternative approaches and suggests several
notes on practical usage of LRD and proposes directions for future work.",
  address="Springer Verlag",
  booktitle="Proceedings of the 10th International Conference on Advanced Concepts for Intelligent Vision Systems",
  chapter="29656",
  edition="Lecture Notes In Computer Science; Vol. 5259",
  howpublished="print",
  institution="Springer Verlag",
  year="2008",
  month="october",
  pages="170--181",
  publisher="Springer Verlag",
  type="conference paper"
}