| Fine-grained recognition of plants from images M Šulc, J Matas Plant Methods 13, 1-14, 2017 | 72 | 2017 |
| 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 | 69 | 2022 |
| 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 | 66 | 2018 |
| 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 | 56 | 2022 |
| System and method for product identification M Sulc, AG Soldevila, DL Larrondo, FC Perronnin US Patent 9,443,164, 2016 | 56 | 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 | 43 | 2022 |
| 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 | 37 | 2013 |
| Texture-based leaf identification M Sulc, J Matas European Conference on Computer Vision, 185-200, 2014 | 36 | 2014 |
| DocILE Benchmark for Document Information Localization and Extraction Š Šimsa, M Šulc, M Uřičář, Y Patel, A Hamdi, M Kocián, M Skalický, ... International Conference on Document Analysis and Recognition - ICDAR 2023 …, 2023 | 33 | 2023 |
| 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 | 31 | 2020 |
| Plant Recognition by AI: Deep Neural Nets, Transformers and kNN in Deep Embeddings L Picek, M Šulc, Y Patel, J Matas Frontiers in Plant Science, 2022 | 27 | 2022 |
| Improving cnn classifiers by estimating test-time priors M Sulc, J Matas Proceedings of the IEEE/CVF International Conference on Computer Vision …, 2019 | 27 | 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 | 26 | 2018 |
| Overview of lifeclef 2024: Challenges on species distribution prediction and identification A Joly, L Picek, S Kahl, H Goëau, V Espitalier, C Botella, D Marcos, ... International Conference of the Cross-Language Evaluation Forum for European …, 2024 | 21 | 2024 |
| Overview of FungiCLEF 2022: Fungi recognition as an open set classification problem L Picek, M Šulc, J Matas, J Heilmann-Clausen CEUR-WS, 2022 | 21 | 2022 |
| 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 | 20 | 2023 |
| 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 | 20 | 2022 |
| 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 |
| 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 |
Jiri MatasProfessor, Czech Technical University在 cmp.felk.cvut.cz 的電子郵件地址已通過驗證
