. . "Praha" . "[9BE847EF1A30]" . . . . . . . "50195" . "20"^^ . "Texture-Based Leaf Identification"@en . "Center for Machine Perception, K13133 FEE Czech Technical University" . "P(GBP103/12/G084), S" . . "\u0160ulc, Milan" . "Computer Vision; Recognition; Leaf; Leaves; Ffirst; Texture"@en . "2"^^ . . "Texture-Based Leaf Identification" . "2"^^ . "RIV/68407700:21230/14:00218862" . "http://cmp.felk.cvut.cz/~sulcmila/papers/Sulc-TR-2014-10.pdf" . . "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 . . . . "Texture-Based Leaf Identification"@en . "Matas, Ji\u0159\u00ED" . . . "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." . . "Texture-Based Leaf Identification" . "21230" . . . . "RIV/68407700:21230/14:00218862!RIV15-MSM-21230___" .