TY  - CONF
N2  - 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.
SP  - 1.1
PB  - BMVA Press
M2  - Swansea, UK
A1  - Giuffrida, Mario Valerio
A1  - Minervini, Massimo
A1  - Tsaftaris, Sotirios A.
UR  - http://www.bmva.org/bmvc/2015/cvppp/papers/paper001/index.html
Y1  - 2016/09//
TI  - Learning to Count Leaves in Rosette Plants
AV  - public
EP  - 1.13
T2  - Proceedings of the Computer Vision Problems in Plant Phenotyping (CVPPP)
ID  - eprints2744
ER  -