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Statements

Subject Item
n2:RIV%2F00216305%3A26230%2F12%3APU95988%21RIV13-MSM-26230___
rdf:type
skos:Concept n15:Vysledek
dcterms:description
Detection of objects in images using statistical classifiers is a well studied and documented technique.  Different applications of such detectors often require selection of the image position with the highest response of the detector -- they perform non-maxima suppression.  This article introduces the concept of Early non-Maxima Suppression, which aims to reduce necessary computations by making the non-Maxima Suppression decision early based on incomplete information provided by a partially evaluated classifier. We show that the error of one such speculative decision with respect to a decision made based on response of the complete classifier can be estimated by collecting statistics on unlabeled data.  The article then considers a sequential strategy of multiple early non-Maxima suppression tests which follows the structure of soft-cascade detectors commonly used for object detection. We also show that an optimal (fastest for requested error rate) suppression strategy can be created b Detection of objects in images using statistical classifiers is a well studied and documented technique.  Different applications of such detectors often require selection of the image position with the highest response of the detector -- they perform non-maxima suppression.  This article introduces the concept of Early non-Maxima Suppression, which aims to reduce necessary computations by making the non-Maxima Suppression decision early based on incomplete information provided by a partially evaluated classifier. We show that the error of one such speculative decision with respect to a decision made based on response of the complete classifier can be estimated by collecting statistics on unlabeled data.  The article then considers a sequential strategy of multiple early non-Maxima suppression tests which follows the structure of soft-cascade detectors commonly used for object detection. We also show that an optimal (fastest for requested error rate) suppression strategy can be created b
dcterms:title
EnMS: Early non-Maxima Suppression EnMS: Early non-Maxima Suppression
skos:prefLabel
EnMS: Early non-Maxima Suppression EnMS: Early non-Maxima Suppression
skos:notation
RIV/00216305:26230/12:PU95988!RIV13-MSM-26230___
n15:predkladatel
n19:orjk%3A26230
n3:aktivita
n16:P n16:Z
n3:aktivity
P(LC06008), Z(MSM0021630528)
n3:cisloPeriodika
2
n3:dodaniDat
n9:2013
n3:domaciTvurceVysledku
n10:4439589 n10:1958313 n10:9340386
n3:druhVysledku
n14:J
n3:duvernostUdaju
n20:S
n3:entitaPredkladatele
n11:predkladatel
n3:idSjednocenehoVysledku
134455
n3:idVysledku
RIV/00216305:26230/12:PU95988
n3:jazykVysledku
n12:eng
n3:klicovaSlova
Non-Maxima Suppression, Object Detection, WaldBoost, Sequential Probability Ratio Test
n3:klicoveSlovo
n4:Sequential%20Probability%20Ratio%20Test n4:Object%20Detection n4:WaldBoost n4:Non-Maxima%20Suppression
n3:kodStatuVydavatele
GB - Spojené království Velké Británie a Severního Irska
n3:kontrolniKodProRIV
[683772D01B41]
n3:nazevZdroje
PATTERN ANALYSIS AND APPLICATIONS
n3:obor
n17:IN
n3:pocetDomacichTvurcuVysledku
3
n3:pocetTvurcuVysledku
3
n3:projekt
n18:LC06008
n3:rokUplatneniVysledku
n9:2012
n3:svazekPeriodika
2012
n3:tvurceVysledku
Herout, Adam Hradiš, Michal Zemčík, Pavel
n3:zamer
n8:MSM0021630528
s:issn
1433-7541
s:numberOfPages
12
n13:organizacniJednotka
26230