About: Texture-Based Leaf Identification

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An Entity of Type : http://linked.opendata.cz/ontology/domain/vavai/Vysledek, within Data Space : linked.opendata.cz associated with source document(s)

Attributes	Values
rdf:type	skos:Concept http://linked.opendata.cz/ontology/domain/vavai/Vysledek
rdfs:seeAlso	http://cmp.felk.cvut.cz/~sulcmila/papers/Sulc-TR-2014-10.pdf
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. (en)
Title	Texture-Based Leaf Identification Texture-Based Leaf Identification (en)
skos:prefLabel	Texture-Based Leaf Identification Texture-Based Leaf Identification (en)
skos:notation	RIV/68407700:21230/14:00218862!RIV15-MSM-21230___
http://linked.open...avai/riv/aktivita	P S
http://linked.open...avai/riv/aktivity	P(GBP103/12/G084), S
http://linked.open...vai/riv/dodaniDat	2015
http://linked.open...aciTvurceVysledku	Šulc, Milan Matas, Jiří
http://linked.open.../riv/druhVysledku	V/S - Souhrnná výzkumná zpráva
http://linked.open...iv/duvernostUdaju	S - Úplné a pravdivé údaje nepodléhající ochraně podle zvláštních právních předpisů
http://linked.open...titaPredkladatele	České vysoké učení technické v Praze / Fakulta elektrotechnická
http://linked.open...dnocenehoVysledku	50195
http://linked.open...ai/riv/idVysledku	RIV/68407700:21230/14:00218862
http://linked.open...riv/jazykVysledku	eng - angličtina
http://linked.open.../riv/klicovaSlova	Computer Vision; Recognition; Leaf; Leaves; Ffirst; Texture (en)
http://linked.open.../riv/klicoveSlovo	Texture Computer Vision Leaf Recognition Leaves Ffirst
http://linked.open...ontrolniKodProRIV	[9BE847EF1A30]
http://linked.open...i/riv/mistoVydani	Praha
http://linked.open...telVyzkumneZpravy	Center for Machine Perception, K13133 FEE Czech Technical University
http://linked.open...in/vavai/riv/obor	JD
http://linked.open...ichTvurcuVysledku	2 (xsd:int)
http://linked.open...cetTvurcuVysledku	2 (xsd:int)
http://linked.open...vavai/riv/projekt	Center for Large Scale Multi-modal Data Interpretation
http://linked.open...UplatneniVysledku	2014
http://linked.open...iv/tvurceVysledku	Matas, Jiří Šulc, Milan
number of pages	20 (xsd:int)
http://localhost/t...ganizacniJednotka	21230

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