publication . Conference object . Contribution for newspaper or weekly magazine . Preprint . 2017

leveraging multiple datasets for deep leaf counting

Mario Valerio Giuffrida;
Open Access
  • Published: 06 Sep 2017
  • Publisher: IEEE
  • Country: United Kingdom
Abstract
Comment: 8 pages, 3 figures, 3 tables
Subjects
free text keywords: Mean difference, Computer science, Regression problems, Segmentation, Annotation, Deep learning, Artificial intelligence, business.industry, business, Image segmentation, Artificial neural network, Pattern recognition, Genetics, Biology, Plant biology, Computer Science - Computer Vision and Pattern Recognition
Funded by
EC| PHIDIAS
Project
PHIDIAS
PHIDIAS: Phenotyping with a High-throughput, Intelligent, Distributed, and Interactive Analysis System
  • Funder: European Commission (EC)
  • Project Code: 256534
  • Funding stream: FP7 | SP3 | PEOPLE
32 references, page 1 of 3

[1] C. Arteta, V. Lempitsky, J. A. Noble, and A. Zisserman. Interactive Object Counting. pages 504-518, 2014. [OpenAIRE]

[2] C. Arteta, V. Lempitsky, and A. Zisserman. Counting in the Wild. 1:483-498, 2016. [OpenAIRE]

[3] J. Bell and H. Dee. Aberystwyth Leaf Evaluation Dataset, 2016.

[4] A. Chayeb, N. Ouadah, Z. Tobal, M. Lakrouf, and O. Azouaoui. Hog based multi-object detection for urban navigation. In 17th International IEEE Conference on Intelligent Transportation Systems (ITSC), pages 2962-2967, 2014. [OpenAIRE]

[5] J. A. Cruz, X. Yin, X. Liu, S. M. Imran, D. D. Morris, D. M. Kramer, and J. Chen. Multi-modality imagery database for plant phenotyping. Machine Vision and Applications, 27(5):735-749, 2016.

[6] N. Dalal and B. Triggs. Histograms of oriented gradients for human detection. In 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05), volume 1, pages 886-893 vol. 1, June 2005.

[7] J. Donahue, L. Anne Hendricks, S. Guadarrama, M. Rohrbach, S. Venugopalan, K. Saenko, and T. Darrell. Long-term recurrent convolutional networks for visual recognition and description. In The IEEE Conference on Computer Vision and Pattern Recognition (CVPR), June 2015.

[8] L. Fiaschi, R. Nair, U. Koethe, and F. A. Hamprecht. Learning to Count with Regression Forest and Structured Labels. In 21st International Conference on Pattern Recognition (ICPR 2012), pages 2685-2688, 2012. [OpenAIRE]

[9] R. Girshick. Fast r-cnn. In The IEEE International Conference on Computer Vision (ICCV), December 2015.

[10] R. Girshick, J. Donahue, T. Darrell, and J. Malik. Rich feature hierarchies for accurate object detection and semantic segmentation. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2014.

[11] M. V. Giuffrida, M. Minervini, and S. Tsaftaris. Learning to Count Leaves in Rosette Plants. In CVPPP workshop - BMVC, page 13. British Machine Vision Association, 2015. [OpenAIRE]

[12] M. Hall, E. Frank, G. Holmes, B. Pfahringer, P. Reutemann, and I. H. Witten. The WEKA Data Mining Software: An Update. SIGKDD Explor. Newsl., 11(1):10-18, Nov. 2009.

[13] K. He, X. Zhang, S. Ren, and J. Sun. Spatial Pyramid Pooling in Deep Convolutional Networks for Visual Recognition, pages 346-361. Springer International Publishing, Cham, 2014.

[14] K. He, X. Zhang, S. Ren, and J. Sun. Deep residual learning for image recognition. arXiv preprint arXiv:1512.03385, 2015.

[15] K. He, X. Zhang, S. Ren, and J. Sun. Identity mappings in deep residual networks. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 9908 LNCS:630-645, 2016.

32 references, page 1 of 3
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