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Learning to Count Leaves in Rosette Plants

Giuffrida, Mario Valerio and Minervini, Massimo and Tsaftaris, Sotirios A. Learning to Count Leaves in Rosette Plants. In: Proceedings of the Computer Vision Problems in Plant Phenotyping (CVPPP), 7-10 September, 2015, Swansea, UK 1.1-1.13. (2016)

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Counting the number of leaves in plants is important for plant phenotyping, since it can be used to assess plant growth stages. We propose a learning-based approach for counting leaves in rosette (model) plants. We relate image-based descriptors learned in an unsupervised fashion to leaf counts using a supervised regression model. To take advantage of the circular and coplanar arrangement of leaves and also to introduce scale and rotation invariance, we learn features in a log-polar representation. Image patches extracted in this log-polar domain are provided to K-means, which builds a codebook in a unsupervised manner. Feature codes are obtained by projecting patches on the codebook using the triangle encoding, introducing both sparsity and specifically designed representation. A global, per-plant image descriptor is obtained by pooling local features in specific regions of the image. Finally, we provide the global descriptors to a support vector regression framework to estimate the number of leaves in a plant. We evaluate our method on datasets of the \textit{Leaf Counting Challenge} (LCC), containing images of Arabidopsis and tobacco plants. Experimental results show that on average we reduce absolute counting error by 40% w.r.t. the winner of the 2014 edition of the challenge -a counting via segmentation method. When compared to state-of-the-art density-based approaches to counting, on Arabidopsis image data ~75% less counting errors are observed. Our findings suggest that it is possible to treat leaf counting as a regression problem, requiring as input only the total leaf count per training image.

Item Type: Conference or Workshop Item (Paper)
Identification Number: 10.5244/C.29.CVPPP.1
Subjects: Q Science > QA Mathematics > QA75 Electronic computers. Computer science
Research Area: Computer Science and Applications
Depositing User: Caterina Tangheroni
Date Deposited: 04 Sep 2015 10:24
Last Modified: 05 May 2016 13:48
URI: http://eprints.imtlucca.it/id/eprint/2744

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