Image-based recognition framework for robotic weed control systems-Reference-Cited by-同舟云学术

Image-based recognition framework for robotic weed control systems

Published:2017-11-09 Issue:8 Volume:77 Page:9567-9594
ISSN:1380-7501
Container-title:Multimedia Tools and Applications
language:en
Short-container-title:Multimed Tools Appl

Author:

Kounalakis Tsampikos^ORCID,Triantafyllidis Georgios A.,Nalpantidis Lazaros

Publisher

Springer Science and Business Media LLC

Subject

Computer Networks and Communications,Hardware and Architecture,Media Technology,Software

Link

http://link.springer.com/article/10.1007/s11042-017-5337-y/fulltext.html

Reference59 articles.

1. Aharon M, Elad M, Bruckstein A (2006) K-SVD: An algorithm for designing overcomplete dictionaries for sparse representation. IEEE Trans Signal Process 54(11):4311–4322. https://doi.org/10.1109/TSP.2006.881199 . http://ieeexplore.ieee.org/document/1710377/ , 59749104367

2. Alahi A, Ortiz R, Vandergheynst P (2012) FREAK: Fast retina keypoint. In: Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, IEEE, pp 510–517. https://doi.org/10.1109/CVPR.2012.6247715 . http://ieeexplore.ieee.org/document/6247715/

3. Bay H, Ess A, Tuytelaars T, Van Gool L (2008) Speeded-Up Robust Features (SURF). Comput Vis Image Underst 110(3):346–359. https://doi.org/10.1016/j.cviu.2007.09.014 . http://linkinghub.elsevier.com/retrieve/pii/S1077314207001555

4. Belongie S, Malik J, Puzicha J (2000) Shape context: A new descriptor for shape matching and object recognition. In: Advances in Neural Information Processing Systems 13: Proceedings of the 2000 Conference, pp 831–837. https://doi.org/10.1109/34.993558

5. Bengio Y (2011) Deep Learning of Representations for Unsupervised and Transfer Learning. Workshop on Unsupervised and Transfer Learn 2011(7):1–20. http://www.iro.umontreal.ca/lisa/pointeurs/DL_tutorial.pdf

Cited by 20 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. Detection of Insect-Damaged Maize Seed Using Hyperspectral Imaging and Hybrid 1D-CNN-BiLSTM Model;Infrared Physics & Technology;2024-03

2. Ensemble Learning for Weed Detection in Soybean Crop;2024 11th International Conference on Computing for Sustainable Global Development (INDIACom);2024-02-28

3. Developing an extreme learning machine based approach to weed segmentation in pastures;Smart Agricultural Technology;2023-10

4. Multi-Class Weed Recognition Using Hybrid CNN-SVM Classifier;Sensors;2023-08-13

5. Pseudo-label semi-supervised learning for soybean monitoring;Smart Agricultural Technology;2023-08