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

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216305%3A26230%2F08%3APU80209" target="_blank" >RIV/00216305:26230/08:PU80209 - isvavai.cz</a>

Výsledek na webu

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DOI - Digital Object Identifier

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Jazyk výsledku

angličtina

Název v původním jazyce

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

Popis výsledku v původním jazyce

A currently popular trend in object detection and pattern<br>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.

Název v anglickém jazyce

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

Popis výsledku anglicky

A currently popular trend in object detection and pattern<br>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.

Druh

D - Stať ve sborníku

CEP obor

JC - Počítačový hardware a software

OECD FORD obor

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Projekt

Výsledek vznikl pri realizaci vícero projektů. Více informací v záložce Projekty.

Návaznosti

P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)<br>Z - Vyzkumny zamer (s odkazem do CEZ)

Rok uplatnění

2008

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Název statě ve sborníku

Proceedings of International Conference on Computer Vision and Graphics 2008

ISBN

978-3-642-02344-6

ISSN

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e-ISSN

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Počet stran výsledku

11

Strana od-do

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Název nakladatele

Springer Verlag

Místo vydání

Heidelberg

Místo konání akce

Varšava

Datum konání akce

10. 11. 2008

Typ akce podle státní příslušnosti

EUR - Evropská akce

Kód UT WoS článku

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