Close range mini Uavs photogrammetry for architecture survey

The survey of historical façades contains several bottlenecks, mainly related to the geometrical structure, the decorative framework, the presence of natural or artificial obstacles, the environment limitations. Urban context presents additional restrictions, binding by ground acquisition activity and leading to building data loss. The integration of TLS and close-range photogrammetry allows to go over such stuff, not overcoming the shadows effect due to the ground point of view. In the last year the massive use of UAVs in survey activity has permitted to enlarge survey capabilities, reaching a deeper knowledge in the architecture analysis. In the meanwhile, several behaviour rules have been introduced in different countries, regulating the UAVs use in different field, strongly restricting their application in urban areas. Recently very small and light platforms have been presented, which can partially overcome these rules restrictions, opening to very interesting future scenarios. This article presents the application of one of these very small RPAS (less than 300 g), equipped with a low-cost camera, in a close range photogrammetric survey of an historical building façade in Bologna (Italy). The suggested analysis tries to point out the system accuracy and details acquisition capacity. The final aim of the paper is to validate the application of this new platform in an architectonic survey pipeline, widening the future application of close-range photogrammetry in the architecture acquisition process

A deep representation for depth images from synthetic data

Convolutional Neural Networks (CNNs) trained on large scale RGB databases have become the secret sauce in the majority of recent approaches for object categorization from RGB-D data. Thanks to colorization techniques, these methods exploit the filters learned from 2D images to extract meaningful representations in 2.5D. Still, the perceptual signature of these two kind of images is very different, with the first usually strongly characterized by textures, and the second mostly by silhouettes of objects. Ideally, one would like to have two CNNs, one for RGB and one for depth, each trained on a suitable data collection, able to capture the perceptual properties of each channel for the task at hand. This has not been possible so far, due to the lack of a suitable depth database. This paper addresses this issue, proposing to opt for synthetically generated images rather than collecting by hand a 2.5D large scale database. While being clearly a proxy for real data, synthetic images allow to trade quality for quantity, making it possible to generate a virtually infinite amount of data. We show that the filters learned from such data collection, using the very same architecture typically used on visual data, learns very different filters, resulting in depth features (a) able to better characterize the different facets of depth images, and (b) complementary with respect to those derived from CNNs pre-trained on 2D datasets. Experiments on two publicly available databases show the power of our approach

Dalla molteplicità alla meta: l'Errante, la Scomposizione, la Durata

1nonenoneRUSSO F.Russo, Fabi

From source to target and back: symmetric bi-directional adaptive GAN

The effectiveness of generative adversarial approaches in producing images according to a specific style or visual domain has recently opened new directions to solve the unsupervised domain adaptation problem. It has been shown that source labeled images can be modified to mimic target samples making it possible to train directly a classifier in the target domain, despite the original lack of annotated data. Inverse mappings from the target to the source domain have also been evaluated but only passing through adapted feature spaces, thus without new image generation. In this paper we propose to better exploit the potential of generative adversarial networks for adaptation by introducing a novel symmetric mapping among domains. We jointly optimize bi-directional image transformations combining them with target self-labeling. Moreover we define a new class consistency loss that aligns the generators in the two directions imposing to conserve the class identity of an image passing through both domain mappings. A detailed qualitative and quantitative analysis of the reconstructed images confirm the power of our approach. By integrating the two domain specific classifiers obtained with our bi-directional network we exceed previous state-of-the-art unsupervised adaptation results on four different benchmark datasets

Emotional Reactions to the Perception of Risk in the Pompeii Archaeological Park

The assessment of perceived risk by people is extremely important for safety and security management. Each person is based on the opinion of others to make a choice and the Internet represents the place where these opinions are mostly researched, found and reviewed. Social networks have a decisive impact: 92% of consumers say they have more trust in social media reviews than in any other form of advertising. For this reason, Opinion Mining and Sentiment Analysis have found interesting applications in the most diverse context, among which the most innovative is certainly represented by public safety and security. Security managers can use the perceptions expressed by people to discover the unexpected and potential weaknesses of a controlled environment or otherwise the risk and security perception of people that sometimes can be very different from real level of risk and security of a given site. Since the perceptions are the result of mostly unconscious elaborations, it is necessary to go deeper and to search for the emotions, triggered by the sensorial stimuli, that determine them. The objective of this paper is to study the perception of risk within the Pompeii Archaeological Park, giving emphasis to the emotional components, using the semantic analysis of the textual contents present in Twitter.Peer reviewe

The ancient Roman gate along Appian way: San Sebastiano Gate

[EN] The application of integrated survey techniques and 3D modelling methodologies for Cultural Heritage analysis is now considered a consolidated process, while preserving and suggesting continuous research lines related from one side to the evolution of acquisition and restitution instruments, from the other to the problems linked to the specific case study and the goal of the research. This article describes the analysis of one of the largest and best-preserved gates of Rome, located within the Aurelian walls: Porta San Sebastiano. The original name of this gate was Porta Appia, transformed in the Middle Ages into San Sebastiano from the name of the Christian martyr buried in the Basilica on the Via Appia, located just outside the walls. The current gate aspect is the result of many architectural changes over the centuries, as well as a transformation happened in the twentieth century which has led it to a residential use, a unique example of its kind. Through the integration of different survey techniques, a geometric analysis of the complex building is completed, highlighting the construction complexity and the spatial articulation. A parametric model of a portion of the building is then suggested, aimed at understanding the logic underlying the definition of a HBIM model related to an existing complex artefact. Through an integrated analysis, the aim of the article is to provide an advancement in the knowledge of the specific Cultural Heritage through the integration of complementary methods of analysis and representation.Russo, M.; Lanfranchi, F.; Carnevali, L. (2020). The ancient Roman gate along Appian way: San Sebastiano Gate. Editorial Universitat Politècnica de València. 447-454. https://doi.org/10.4995/FORTMED2020.2020.11337OCS44745

Numerical simulation of magnetic nano drug targeting in a patient-specific coeliac trunk

Magnetic nano drug targeting, through the use of an external magnetic field, is a new technique for the treatment of several diseases, which can potentially avoid the dispersion of drugs in undesired locations of the body. Nevertheless, due to the limitations on the intensity of the magnetic field applied, the hydrodynamic forces can reduce the effectiveness of the procedure. This technique is studied in this paper with the Computational Fluid Dynamics (CFD), focusing on the influence of the magnetic probe position, and the direction of the circulating electric current. A single rectangular coil is used to generate the external magnetic field. A patient-specific geometry of the coeliac trunk is reconstructed from DICOM images, with the use of VMTK. A new solver, coupling the Lagrangian dynamics of the nanoparticles with the Eulerian dynamics of the blood, is implemented in OpenFOAM to perform the simulations. The resistive pressure, the Womersley’s profile for the inlet velocity and the magnetic field of a rectangular coil are implemented in the software as boundary conditions. The results show the influence of the position of the probe, as well as the limitations associated with the rectangular coil configuration