Milan Šulc
Milan Šulc
Center for Machine Perception, Czech Technical University in Prague
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Kernel-mapped histograms of multi-scale LBPs for tree bark recognition
M Sulc, J Matas
Image and Vision Computing New Zealand (IVCNZ), 2013 28th International†…, 2013
Fine-grained recognition of plants from images
M Šulc, J Matas
Plant Methods 13 (1), 115, 2017
Texture-based leaf identification
M Sulc, J Matas
European Conference on Computer Vision, 185-200, 2014
Plant Identification: Experts vs. Machines in the Era of Deep Learning
P Bonnet, H GoŽau, ST Hang, M Lasseck, M Šulc, V Malťcot, P Jauzein, ...
Multimedia Tools and Applications for Environmental & Biodiversity†…, 2018
Fast features invariant to rotation and scale of texture
M Sulc, J Matas
European Conference on Computer Vision, 47-62, 2014
System and method for product identification
M Sulc, AG Soldevila, DL Larrondo, FC Perronnin
US Patent 9,443,164, 2016
Very deep residual networks with maxout for plant identification in the wild
M Šulc, D Mishkin, J Matas
Working notes of CLEF, 2016
Plant Recognition by Inception Networks with Test-time Class Prior Estimation
M Šulc, L Picek, J Matas
CLEF 2018 - Conference and Labs of the Evaluation Forum, 2018
Learning with Noisy and Trusted Labels for Fine-Grained Plant Recognition
M Šulc, J Matas
CLEF 2017 - Conference and Labs of the Evaluation Forum, 2017
Improving cnn classifiers by estimating test-time priors
M Sulc, J Matas
Proceedings of the IEEE International Conference on Computer Vision†…, 2019
Recognition of the Amazonian flora by inception networks with test-time class prior estimation
L Picek, M Šulc, J Matas
CLEF (Working Notes), 2019
Deep learning for plant identification: how the web can compete with human experts
H GoŽau, A Joly, P Bonnet, M Lasseck, M Šulc, ST Hang
Biodiversity Information Science and Standards 2, e25637, 2018
Fungi Recognition: A Practical Use Case
M Sulc, L Picek, J Matas, T Jeppesen, J Heilmann-Clausen
The IEEE Winter Conference on Applications of Computer Vision, 2316-2324, 2020
Significance of Colors in Texture Datasets
M Šulc, J Matas
Computer Vision Winter Workshop, 2016
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