@inproceedings{eprints2744,
           title = {Learning to Count Leaves in Rosette Plants},
       booktitle = {Proceedings of the Computer Vision Problems in Plant Phenotyping (CVPPP)},
            year = {2016},
           month = {September},
       publisher = {BMVA Press},
           pages = {1.1--1.13},
          author = {Mario Valerio Giuffrida and Massimo Minervini and Sotirios A. Tsaftaris},
        abstract = {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 {$\backslash$}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 {\texttt{\char126}}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.},
             url = {http://eprints.imtlucca.it/2744/}
}