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Statements

Subject Item
n2:RIV%2F68407700%3A21230%2F14%3A00218862%21RIV15-MSM-21230___
rdf:type
skos:Concept n17:Vysledek
rdfs:seeAlso
http://cmp.felk.cvut.cz/~sulcmila/papers/Sulc-TR-2014-10.pdf
dcterms:description
A novel approach to visual leaf identification is proposed. A leaf is represented by a pair of local feature histograms, one computed from the leaf interior, the other from the border. The histogrammed local features are an improved version of a recently proposed rotation and scale invariant descriptor based on local binary patterns (LBPs). Describing the leaf with multi-scale histograms of rotationally invariant features derived from sign- and magnitude-LBP provides a desirable level of invariance. The representation does not use colour. Using the same parameter settings in all experiments and standard evaluation protocols, the method outperforms the state-of-the-art on all tested leaf sets - the Austrian Federal Forests d ataset, the Flavia dataset, the Foliage dataset, the Swedish dataset and the Midd le European Woods dataset - achieving excellent recognition rates above 99% . Preliminary results on images from the jnorth and south regions of Franc e obtained from the LifeCLEF'14 Plant task dataset indicate that the propos ed method is also applicable to recognizing the environmental conditions the plant has been exposed to. A novel approach to visual leaf identification is proposed. A leaf is represented by a pair of local feature histograms, one computed from the leaf interior, the other from the border. The histogrammed local features are an improved version of a recently proposed rotation and scale invariant descriptor based on local binary patterns (LBPs). Describing the leaf with multi-scale histograms of rotationally invariant features derived from sign- and magnitude-LBP provides a desirable level of invariance. The representation does not use colour. Using the same parameter settings in all experiments and standard evaluation protocols, the method outperforms the state-of-the-art on all tested leaf sets - the Austrian Federal Forests d ataset, the Flavia dataset, the Foliage dataset, the Swedish dataset and the Midd le European Woods dataset - achieving excellent recognition rates above 99% . Preliminary results on images from the jnorth and south regions of Franc e obtained from the LifeCLEF'14 Plant task dataset indicate that the propos ed method is also applicable to recognizing the environmental conditions the plant has been exposed to.
dcterms:title
Texture-Based Leaf Identification Texture-Based Leaf Identification
skos:prefLabel
Texture-Based Leaf Identification Texture-Based Leaf Identification
skos:notation
RIV/68407700:21230/14:00218862!RIV15-MSM-21230___
n3:aktivita
n9:S n9:P
n3:aktivity
P(GBP103/12/G084), S
n3:dodaniDat
n16:2015
n3:domaciTvurceVysledku
n13:3455157 n13:1711326
n3:druhVysledku
n8:V%2FS
n3:duvernostUdaju
n5:S
n3:entitaPredkladatele
n19:predkladatel
n3:idSjednocenehoVysledku
50195
n3:idVysledku
RIV/68407700:21230/14:00218862
n3:jazykVysledku
n15:eng
n3:klicovaSlova
Computer Vision; Recognition; Leaf; Leaves; Ffirst; Texture
n3:klicoveSlovo
n10:Leaves n10:Computer%20Vision n10:Leaf n10:Recognition n10:Texture n10:Ffirst
n3:kontrolniKodProRIV
[9BE847EF1A30]
n3:mistoVydani
Praha
n3:objednatelVyzkumneZpravy
Center for Machine Perception, K13133 FEE Czech Technical University
n3:obor
n6:JD
n3:pocetDomacichTvurcuVysledku
2
n3:pocetTvurcuVysledku
2
n3:projekt
n4:GBP103%2F12%2FG084
n3:rokUplatneniVysledku
n16:2014
n3:tvurceVysledku
Šulc, Milan Matas, Jiří
s:numberOfPages
20
n18:organizacniJednotka
21230