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Statements

Subject Item
n2:RIV%2F67985556%3A_____%2F09%3A00331807%21RIV10-MSM-67985556
rdf:type
n17:Vysledek skos:Concept
dcterms:description
A novel illumination invariant unsupervised multispectral texture segmentation method with unknown number of classes is presented. Multispectral texture mosaics are locally represented by illumination invariants derived from four directional causal multispectral Markovian models recursively evaluated for each pixel. Resulted parametric space is segmented using a Gaussian mixture model based unsupervised segmenter. The segmentation algorithm 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 large illumination invariant benchmark from the Prague Segmentation Benchmark using 21 segmentation criteria and compares favourably with an alternative segmentation method. A novel illumination invariant unsupervised multispectral texture segmentation method with unknown number of classes is presented. Multispectral texture mosaics are locally represented by illumination invariants derived from four directional causal multispectral Markovian models recursively evaluated for each pixel. Resulted parametric space is segmented using a Gaussian mixture model based unsupervised segmenter. The segmentation algorithm 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 large illumination invariant benchmark from the Prague Segmentation Benchmark using 21 segmentation criteria and compares favourably with an alternative segmentation method.
dcterms:title
Illumination Invariant Unsupervised Segmenter Illumination Invariant Unsupervised Segmenter
skos:prefLabel
Illumination Invariant Unsupervised Segmenter Illumination Invariant Unsupervised Segmenter
skos:notation
RIV/67985556:_____/09:00331807!RIV10-MSM-67985556
n3:aktivita
n19:P n19:Z
n3:aktivity
P(1M0572), P(GA102/08/0593), Z(AV0Z10750506)
n3:dodaniDat
n10:2010
n3:domaciTvurceVysledku
n11:2812495 n11:1555170 n11:2890542
n3:druhVysledku
n9:D
n3:duvernostUdaju
n20:S
n3:entitaPredkladatele
n21:predkladatel
n3:idSjednocenehoVysledku
318505
n3:idVysledku
RIV/67985556:_____/09:00331807
n3:jazykVysledku
n13:eng
n3:klicovaSlova
unsupervised image segmentation; Illumination Invariants
n3:klicoveSlovo
n4:unsupervised%20image%20segmentation n4:Illumination%20Invariants
n3:kontrolniKodProRIV
[6E7FF1EA4449]
n3:mistoKonaniAkce
Cairo
n3:mistoVydani
Los Alamitos
n3:nazevZdroje
Proceedings of the 16th International Conference on Image Processing, ICIP 2009
n3:obor
n7:BD
n3:pocetDomacichTvurcuVysledku
3
n3:pocetTvurcuVysledku
3
n3:projekt
n6:GA102%2F08%2F0593 n6:1M0572
n3:rokUplatneniVysledku
n10:2009
n3:tvurceVysledku
Haindl, Michal Vácha, Pavel Mikeš, Stanislav
n3:typAkce
n12:WRD
n3:zahajeniAkce
2009-11-07+01:00
n3:zamer
n14:AV0Z10750506
s:numberOfPages
4
n8:hasPublisher
IEEE
n15:isbn
978-1-4244-5655-0