An improvement of energy-transfer features using DCT for face detection

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F61989100%3A27240%2F14%3A86092994" target="_blank" >RIV/61989100:27240/14:86092994 - isvavai.cz</a>

Výsledek na webu

<a href="http://link.springer.com/chapter/10.1007%2F978-3-319-07998-1_59" target="_blank" >http://link.springer.com/chapter/10.1007%2F978-3-319-07998-1_59</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1007/978-3-319-07998-1_59" target="_blank" >10.1007/978-3-319-07998-1_59</a>

Jazyk výsledku

angličtina

Název v původním jazyce

An improvement of energy-transfer features using DCT for face detection

Popis výsledku v původním jazyce

The basic idea behind the energy-transfer features (ETF) is that the appearance of objects can be successfully described using the function of energy distribution in the image. This function has to be reduced into a reasonable number of values. These values are then considered as the vector that is used as an input for the SVM classifier. The process of reducing can be simply solved by sampling; the input image is divided into the regular cells and inside each cell, the mean of the values is calculated.In this paper, we propose an improvement of this process; the Discrete Cosine Transform (DCT) coefficients are calculated inside the cells (instead of the mean values) to construct the feature vector. In addition, the DCT coefficients are reduced usingthe Principal Component Analysis (PCA) to create the feature vector with a relatively small dimensionally. The results show that using this approach, the objects can be efficiently encoded with the relatively small set of numbers with pro

Název v anglickém jazyce

An improvement of energy-transfer features using DCT for face detection

Popis výsledku anglicky

The basic idea behind the energy-transfer features (ETF) is that the appearance of objects can be successfully described using the function of energy distribution in the image. This function has to be reduced into a reasonable number of values. These values are then considered as the vector that is used as an input for the SVM classifier. The process of reducing can be simply solved by sampling; the input image is divided into the regular cells and inside each cell, the mean of the values is calculated.In this paper, we propose an improvement of this process; the Discrete Cosine Transform (DCT) coefficients are calculated inside the cells (instead of the mean values) to construct the feature vector. In addition, the DCT coefficients are reduced usingthe Principal Component Analysis (PCA) to create the feature vector with a relatively small dimensionally. The results show that using this approach, the objects can be efficiently encoded with the relatively small set of numbers with pro

Druh

D - Stať ve sborníku

CEP obor

IN - Informatika

OECD FORD obor

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Projekt

—

Návaznosti

S - Specificky vyzkum na vysokych skolach

Rok uplatnění

2014

Kód důvěrnosti údajů

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

Název statě ve sborníku

Lecture Notes in Computer Science. Volume 8509

ISBN

978-3-319-07997-4

ISSN

0302-9743

e-ISSN

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Počet stran výsledku

9

Strana od-do

511-519

Název nakladatele

Springer Verlag

Místo vydání

London

Místo konání akce

Cherbourg

Datum konání akce

30. 6. 2014

Typ akce podle státní příslušnosti

WRD - Celosvětová akce

Kód UT WoS článku

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