We provide key facts about the TRADR project deployment of ground and aerial robots in Amatrice, Italy, after the major earthquake in August 2016. The robots were used to collect data for 3D textured models of the interior and exterior of two badly damaged churches of high national heritage value.

In this paper, we present a novel method for vision based plants phenotyping in indoor vertical farming under artificial lighting. The method combines 3D plants modeling and deep segmentation of the higher leaves, during a period of 25–30 days, related to their growth. The novelty of our approach is in providing 3D reconstruction, leaf segmentation, geometric surface modeling, and deep network estimation for weight prediction to effectively measure plant growth, under three relevant phenotype features: height, weight and leaf area. Together with the vision based measurements, to verify the soundness of our proposed method, we also harvested the plants at specific time periods to take manual measurements, collecting a great amount of data. In particular, we manually collected 2592 data points related to the plant phenotype and 1728 images of the plants. This allowed us to show with a good number of experiments that the vision based methods ensure a quite accurate prediction of the considered features, providing a way to predict plant behavior, under specific conditions, without any need to resort to human measurements.

Given the recent increase in the availability of multispectral multibeam echosounder data, this work aims to identify suitable processing and classification methodologies for seabed classification based on such data. We propose a complete processing and classification pipeline and investigate the adequacy of state-of-the-art classification algorithms to perform seabed classification based on multispectral backscatter data alone, and when additional data sources are considered. Starting from raw acquisition data, we generate region-wide multispectral backscatter composite images through noise removal, inpainting/gap-filling and mosaicking. Ground truth data from in situ seabed samples are used. We have tried different classification methods, including random forests, support vector machines, and multilayer perceptrons, with the latter providing the best results. Quantitative and qualitative evaluation on five surveys indicate high classification performance based only on multispectral backscatter data, while additional features, like bathymetry, bathymetric positional index (BPI), or positional encoding, offer limited gains. We offer a web service for seabed classification from multispectral multibeam echosounder data to further support and increase interest in the topic.

We introduce a novel framework for modeling articulated objects based on the aspects of their components. By decomposing the object into components, we divide the problem in smaller modeling tasks. After obtaining 3D models for each component aspect by employing a shape deformation paradigm, we merge them together, forming the object components. The final model is obtained by assembling the components using an optimization scheme which fits the respective 3D models to the corresponding apparent contours in a reference pose. The results suggest that our approach can produce realistic 3D models of articulated objects in reasonable time.

In the coherence theory of attention, introduced by Rensink, O’Regan, and Clark (2000), a coherence field is defined by a hierarchy of structures supporting the activities taking place across the different stages of visual attention. At the interface between low level and mid-level attention processing stages are the proto-objects; these are generated in parallel and collect features of the scene at specific location and time. These structures fade away if the region is no further attended by attention. We introduce a method to computationally model these structures. Our model is based experimentally on data collected in dynamic 3D environments via the Gaze Machine, a gaze measurement framework. This framework allows to record pupil motion at the required speed and projects the point of regard in the 3D space (Pirri, Pizzoli, & Rudi, 2011; Pizzoli, Rigato, Shabani, & Pirri, 2011). To generate proto-objects the model is extended to vibrating circular membranes whose initial displacement is generated by the features that have been selected by classification. The energy of the vibrating membranes is used to predict saliency in visual search tasks