Energy-transfer features and their application in the task of face detection

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F61989100%3A27240%2F13%3A86088599" target="_blank" >RIV/61989100:27240/13:86088599 - isvavai.cz</a>

Výsledek na webu

<a href="http://ieeexplore.ieee.org/xpl/articleDetails.jsp?arnumber=6636631" target="_blank" >http://ieeexplore.ieee.org/xpl/articleDetails.jsp?arnumber=6636631</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1109/AVSS.2013.6636631" target="_blank" >10.1109/AVSS.2013.6636631</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Energy-transfer features and their application in the task of face detection

Popis výsledku v původním jazyce

In this paper, we describe a novel and interesting approach for extracting the image features. The features we propose are efficient and robust; the feature vectors of relatively small dimensions are sufficient for successful recognition. We call them the energy-transfer features. In contrast, the classical features (e.g. HOG, Haar features) that are combined with the trainable classifiers (e.g. a support vector machine, neural network) require large training sets due to their high dimensionality. The large training sets are difficult to acquire in many cases. In addition to that, the large training sets slow down the training phase. Moreover, the high dimension of feature vector also slows down the detection phase and the methods for the reduction offeature vector must be used. These shortcomings became the motivation for creating the features that are able to describe the object of interest with a relatively small number of numerical values without the use of methods for the reducti

Název v anglickém jazyce

Energy-transfer features and their application in the task of face detection

Popis výsledku anglicky

In this paper, we describe a novel and interesting approach for extracting the image features. The features we propose are efficient and robust; the feature vectors of relatively small dimensions are sufficient for successful recognition. We call them the energy-transfer features. In contrast, the classical features (e.g. HOG, Haar features) that are combined with the trainable classifiers (e.g. a support vector machine, neural network) require large training sets due to their high dimensionality. The large training sets are difficult to acquire in many cases. In addition to that, the large training sets slow down the training phase. Moreover, the high dimension of feature vector also slows down the detection phase and the methods for the reduction offeature vector must be used. These shortcomings became the motivation for creating the features that are able to describe the object of interest with a relatively small number of numerical values without the use of methods for the reducti

Druh

D - Stať ve sborníku

CEP obor

IN - Informatika

OECD FORD obor

—

Projekt

—

Návaznosti

S - Specificky vyzkum na vysokych skolach

Rok uplatnění

2013

Kód důvěrnosti údajů

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

Název statě ve sborníku

2013 10th IEEE International Conference on Advanced Video and Signal Based Surveillance, AVSS 2013

ISBN

978-1-4799-0703-8

ISSN

—

e-ISSN

—

Počet stran výsledku

6

Strana od-do

147 - 152

Název nakladatele

IEEE

Místo vydání

Piscataway

Místo konání akce

Krakow

Datum konání akce

27. 8. 2013

Typ akce podle státní příslušnosti

WRD - Celosvětová akce

Kód UT WoS článku

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