Aeroporto Sanzio: quale futuro?

Contributo pubblicato sulla rivista Realt\ue0 industriale, n. 10 (ottobre), Confindustria March

An interactive tool for semi-automated leaf annotation

High throughput plant phenotyping is emerging as a necessary step towards meeting agricultural demands of the future. Central to its success is the development of robust computer vision algorithms that analyze images and extract phenotyping information to be associated with genotypes and environmental conditions for identifying traits suitable for further development. Obtaining leaf level quantitative data is important towards understanding better this interaction. While certain efforts have been made to obtain such information in an automated fashion, further innovations are necessary. In this paper we present an annotation tool that can be used to semi-automatically segment leaves in images of rosette plants. This tool, which is designed to exist in a stand-alone fashion but also in cloud based environments, can be used to annotate data directly for the study of plant and leaf growth or to provide annotated datasets for learning-based approaches to extracting phenotypes from images. It relies on an interactive graph-based segmentation algorithm to propagate expert provided priors (in the form of pixels) to the rest of the image, using the random walk formulation to find a good per-leaf segmentation. To evaluate the tool we use standardized datasets available from the LSC and LCC 2015 challenges, achieving an average leaf segmentation accuracy of almost 97% using scribbles as annotations. The tool and source code are publicly available at http://www.phenotiki.com and as a GitHub repository at https://github.com/phenotiki/LeafAnnotationTool

Learning to Count Leaves in Rosette Plants

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

Age-related changes in the primary motor cortex of newborn to adult domestic pig sus scrofa domesticus

The pig has been increasingly used as a suitable animal model in translational neuroscience. However, several features of the fast-growing, immediately motor-competent cerebral cortex of this species have been adequately described. This study analyzes the cytoarchitecture of the primary motor cortex (M1) of newborn, young and adult pigs (Sus scrofa domesticus). Moreover, we investigated the distribution of the neural cells expressing the calcium-binding proteins (CaBPs) (calretinin, CR; parvalbumin, PV) throughout M1. The primary motor cortex of newborn piglets was characterized by a dense neuronal arrangement that made the discrimination of the cell layers difficult, except for layer one. The absence of a clearly recognizable layer four, typical of the agranular cortex, was noted in young and adult pigs. The morphometric and immunohistochemical analy-ses revealed age-associated changes characterized by (1) thickness increase and neuronal density (number of cells/mm2 of M1) reduction during the first year of life; (2) morphological changes of CR-immunoreactive neurons in the first months of life; (3) higher density of CR-and PV-immunopositive neurons in newborns when compared to young and adult pigs. Since most of the present findings match with those of the human M1, this study strengthens the growing evidence that the brain of the pig can be used as a potentially valuable translational animal model during growth and development