Fine-grained recognition of plants from images M Šulc, J Matas Plant Methods 13, 1-14, 2017 | 55 | 2017 |
Plant identification: Experts vs. machines in the era of deep learning: deep learning techniques challenge flora experts P Bonnet, H Goëau, ST Hang, M Lasseck, M Šulc, V Malécot, P Jauzein, ... Multimedia tools and applications for environmental & biodiversity …, 2018 | 51 | 2018 |
Overview of LifeCLEF 2022: an evaluation of Machine-Learning based Species Identification and Species Distribution Prediction A Joly, H Goëau, S Kahl, L Picek, T Lorieul, E Cole, B Deneu, ... Lecture Notes in Computer Science, 2022 | 49 | 2022 |
System and method for product identification M Sulc, AG Soldevila, DL Larrondo, FC Perronnin US Patent 9,443,164, 2016 | 47 | 2016 |
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 | 35 | 2013 |
Danish Fungi 2020-Not Just Another Image Recognition Dataset L Picek, M Šulc, J Matas, TS Jeppesen, J Heilmann-Clausen, T Læssøe, ... Proceedings of the IEEE/CVF Winter Conference on Applications of Computer …, 2022 | 34 | 2022 |
Texture-based leaf identification M Sulc, J Matas European Conference on Computer Vision, 185-200, 2014 | 28 | 2014 |
Fungi recognition: A practical use case M Sulc, L Picek, J Matas, T Jeppesen, J Heilmann-Clausen Proceedings of the IEEE/CVF Winter Conference on Applications of Computer …, 2020 | 26 | 2020 |
Improving cnn classifiers by estimating test-time priors M Sulc, J Matas Proceedings of the IEEE/CVF International Conference on Computer Vision …, 2019 | 25 | 2019 |
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 | 24 | 2018 |
Very deep residual networks with maxout for plant identification in the wild M Šulc, D Mishkin, J Matas Working notes of CLEF, 2016 | 19 | 2016 |
Automatic Fungi Recognition: Deep Learning Meets Mycology L Picek, M Šulc, J Matas, J Heilmann-Clausen, TS Jeppesen, E Lind Sensors 22 (633), 2022 | 16 | 2022 |
Fast features invariant to rotation and scale of texture M Sulc, J Matas European Conference on Computer Vision, 47-62, 2014 | 16 | 2014 |
Recognition of the Amazonian flora by inception networks with test-time class prior estimation L Picek, M Šulc, J Matas CLEF (Working Notes), 2019 | 15 | 2019 |
The Hitchhiker's Guide to Prior-Shift Adaptation T Šipka, M Šulc, J Matas Proceedings of the IEEE/CVF Winter Conference on Applications of Computer …, 2022 | 13 | 2022 |
Overview of FungiCLEF 2022: Fungi recognition as an open set classification problem L Picek, M Šulc, J Matas, J Heilmann-Clausen CEUR-WS, 2022 | 12 | 2022 |
Deep learning for plant identification: how the web can compete with human experts H Goëau, A Joly, P Bonnet, M Lasseck, M Šulc, ST Hang Biodiversity Information Science and Standards 2, e25637, 2018 | 12 | 2018 |
Overview of lifeclef 2023: evaluation of ai models for the identification and prediction of birds, plants, snakes and fungi A Joly, C Botella, L Picek, S Kahl, H Goëau, B Deneu, D Marcos, ... International Conference of the Cross-Language Evaluation Forum for European …, 2023 | 11 | 2023 |
Lifeclef 2022 teaser: An evaluation of machine-learning based species identification and species distribution prediction A Joly, H Goëau, S Kahl, L Picek, T Lorieul, E Cole, B Deneu, ... European Conference on Information Retrieval, 390-399, 2022 | 10 | 2022 |
DocILE Benchmark for Document Information Localization and Extraction Š Šimsa, M Šulc, M Uřičář, Y Patel, A Hamdi, M Kocián, M Skalický, ... arXiv preprint arXiv:2301.12394, 2023 | 8 | 2023 |