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Statements

Subject Item
n2:RIV%2F67985556%3A_____%2F09%3A00327029%21RIV10-MSM-67985556
rdf:type
n13:Vysledek skos:Concept
dcterms:description
An unsupervised multi-spectral, multi-resolution, multiple-segmenter for textured images with unknown number of classes is presented. The segmenter is based on a weighted combination of several unsupervised segmentation results, each in different resolution, using the modified sum rule. Multi-spectral textured image mosaics are locally represented by four causal directional multi-spectral random field models recursively evaluated for each pixel. The single-resolution segmentation part of the algorithm is based on the underlying Gaussian mixture model and starts with an over segmented initial estimation which is adaptively modified until the optimal number of homogeneous texture segments is reached. The performance of the presented method is extensively tested on the Prague segmentation benchmark using the commonest segmentation criteria and compares favourably with several leading alternative image segmentation methods. An unsupervised multi-spectral, multi-resolution, multiple-segmenter for textured images with unknown number of classes is presented. The segmenter is based on a weighted combination of several unsupervised segmentation results, each in different resolution, using the modified sum rule. Multi-spectral textured image mosaics are locally represented by four causal directional multi-spectral random field models recursively evaluated for each pixel. The single-resolution segmentation part of the algorithm is based on the underlying Gaussian mixture model and starts with an over segmented initial estimation which is adaptively modified until the optimal number of homogeneous texture segments is reached. The performance of the presented method is extensively tested on the Prague segmentation benchmark using the commonest segmentation criteria and compares favourably with several leading alternative image segmentation methods.
dcterms:title
Unsupervised Hierarchical Weighted Multi-Segmenter Unsupervised Hierarchical Weighted Multi-Segmenter
skos:prefLabel
Unsupervised Hierarchical Weighted Multi-Segmenter Unsupervised Hierarchical Weighted Multi-Segmenter
skos:notation
RIV/67985556:_____/09:00327029!RIV10-MSM-67985556
n3:aktivita
n6:Z n6:P
n3:aktivity
P(1M0572), P(2C06019), P(GA102/08/0593), Z(AV0Z10750506)
n3:dodaniDat
n21:2010
n3:domaciTvurceVysledku
n19:4788575 n19:2890542 n19:2812495
n3:druhVysledku
n17:D
n3:duvernostUdaju
n8:S
n3:entitaPredkladatele
n9:predkladatel
n3:idSjednocenehoVysledku
347674
n3:idVysledku
RIV/67985556:_____/09:00327029
n3:jazykVysledku
n11:eng
n3:klicovaSlova
unsupervised image segmentation
n3:klicoveSlovo
n15:unsupervised%20image%20segmentation
n3:kontrolniKodProRIV
[6124C8388BDA]
n3:mistoKonaniAkce
Reykjavik
n3:mistoVydani
Berlin Heidelberg
n3:nazevZdroje
Multiple Classifier Systems, LNCS 5519
n3:obor
n5:BD
n3:pocetDomacichTvurcuVysledku
3
n3:pocetTvurcuVysledku
3
n3:projekt
n12:1M0572 n12:GA102%2F08%2F0593 n12:2C06019
n3:rokUplatneniVysledku
n21:2009
n3:tvurceVysledku
Pudil, Pavel Haindl, Michal Mikeš, Stanislav
n3:typAkce
n14:WRD
n3:zahajeniAkce
2009-06-10+02:00
n3:zamer
n16:AV0Z10750506
s:numberOfPages
11
n20:hasPublisher
Springer-Verlag
n7:isbn
3-642-02325-8