Texture-Based Leaf Identification

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F68407700%3A21230%2F14%3A00218862" target="_blank" >RIV/68407700:21230/14:00218862 - isvavai.cz</a>

Výsledek na webu

<a href="http://cmp.felk.cvut.cz/~sulcmila/papers/Sulc-TR-2014-10.pdf" target="_blank" >http://cmp.felk.cvut.cz/~sulcmila/papers/Sulc-TR-2014-10.pdf</a>

DOI - Digital Object Identifier

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Jazyk výsledku

angličtina

Název v původním jazyce

Texture-Based Leaf Identification

Popis výsledku v původním jazyce

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 recentlyproposed 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. Therepresentation 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 P

Název v anglickém jazyce

Texture-Based Leaf Identification

Popis výsledku anglicky

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 recentlyproposed 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. Therepresentation 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 P

Druh

V_souhrn - Souhrnná výzkumná zpráva

CEP obor

JD - Využití počítačů, robotika a její aplikace

OECD FORD obor

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Projekt

<a href="/cs/project/GBP103%2F12%2FG084" target="_blank" >GBP103/12/G084: Centrum pro multi-modální interpretaci dat velkého rozsahu</a><br>

Návaznosti

S - Specificky vyzkum na vysokych skolach

Rok uplatnění

2014

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Počet stran výsledku

20

Místo vydání

Praha

Název nakladatele resp. objednatele

Center for Machine Perception, K13133 FEE Czech Technical University

Verze

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