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Statements

Subject Item
n2:RIV%2F68407700%3A21230%2F13%3A00212523%21RIV15-MSM-21230___
rdf:type
n3:Vysledek skos:Concept
rdfs:seeAlso
ftp://cmp.felk.cvut.cz/pub/cmp/articles/hurycd1/hurych-accv2012.pdf
dcterms:description
We exploit image features multiple times in order to make sequential decision process faster and better performing. In the decision process features providing knowledge about the object presence or absence in a given detection window are successively evaluated. We show that these features also provide information about object position within the evaluated window. The classification process is sequentially interleaved with estimating the correct position. The position estimate is used for steering the features yet to be evaluated. This locally interleaved sequential alignment (LISA) allows to run an object detector on sparser grid which speeds up the process. The position alignment is jointly learned with the detector. We achieve a better detection rate since the method allows for training the detector on perfectly aligned image samples. For estimation of the alignment we propose a learnable regressor that approximates a non-linear regression function and runs in ne2076-1465gligible time. We exploit image features multiple times in order to make sequential decision process faster and better performing. In the decision process features providing knowledge about the object presence or absence in a given detection window are successively evaluated. We show that these features also provide information about object position within the evaluated window. The classification process is sequentially interleaved with estimating the correct position. The position estimate is used for steering the features yet to be evaluated. This locally interleaved sequential alignment (LISA) allows to run an object detector on sparser grid which speeds up the process. The position alignment is jointly learned with the detector. We achieve a better detection rate since the method allows for training the detector on perfectly aligned image samples. For estimation of the alignment we propose a learnable regressor that approximates a non-linear regression function and runs in ne2076-1465gligible time.
dcterms:title
Exploiting Features - Locally Interleaved Sequential Alignment for Object Detection Exploiting Features - Locally Interleaved Sequential Alignment for Object Detection
skos:prefLabel
Exploiting Features - Locally Interleaved Sequential Alignment for Object Detection Exploiting Features - Locally Interleaved Sequential Alignment for Object Detection
skos:notation
RIV/68407700:21230/13:00212523!RIV15-MSM-21230___
n4:aktivita
n9:P
n4:aktivity
P(7E10044), P(GAP103/10/1585), P(GPP103/11/P700)
n4:dodaniDat
n5:2015
n4:domaciTvurceVysledku
n8:1144359 n8:9397000 n8:6464742
n4:druhVysledku
n16:D
n4:duvernostUdaju
n22:S
n4:entitaPredkladatele
n20:predkladatel
n4:idSjednocenehoVysledku
74294
n4:idVysledku
RIV/68407700:21230/13:00212523
n4:jazykVysledku
n17:eng
n4:klicovaSlova
exploit; features; adaboost; waldboost; sequential; regression; predictors; interleaved; alignment; sliding window
n4:klicoveSlovo
n12:regression n12:features n12:predictors n12:alignment n12:exploit n12:adaboost n12:sliding%20window n12:sequential n12:waldboost n12:interleaved
n4:kontrolniKodProRIV
[BFDD0BB9596E]
n4:mistoKonaniAkce
Daejeon
n4:mistoVydani
Heidelberg
n4:nazevZdroje
Computer Vision - ACCV 2012, 11th Asian Conference on Computer Vision, Part 1
n4:obor
n23:JD
n4:pocetDomacichTvurcuVysledku
3
n4:pocetTvurcuVysledku
3
n4:projekt
n11:7E10044 n11:GPP103%2F11%2FP700 n11:GAP103%2F10%2F1585
n4:rokUplatneniVysledku
n5:2013
n4:tvurceVysledku
Hurych, David Svoboda, Tomáš Zimmermann, Karel
n4:typAkce
n14:WRD
n4:zahajeniAkce
2013-11-05+01:00
s:issn
0302-9743
s:numberOfPages
14
n18:doi
10.1007/978-3-642-37331-2_34
n13:hasPublisher
Springer-Verlag
n7:isbn
978-3-642-37330-5
n19:organizacniJednotka
21230