TECHNART 2017. Non-destructive and microanalytical techniques in art and cultural heritage. Book of abstracts

440 p.TECHNART2017 is the international biannual congress on the application of Analytical Techniques in Art and Cultural Heritage. The aim of this European conference is to provide a scientific forum to present and promote the use of analytical spectroscopic techniques in cultural heritage on a worldwide scale to stimulate contacts and exchange experiences, making a bridge between science and art. This conference builds on the momentum of the previous TECHNART editions of Lisbon, Athens, Berlin, Amsterdam and Catania, offering an outstanding and unique opportunity for exchanging knowledge on leading edge developments. Cultural heritage studies are interpreted in a broad sense, including pigments, stones, metal, glass, ceramics, chemometrics on artwork studies, resins, fibers, forensic applications in art, history, archaeology and conservation science. The meeting is focused in different aspects: - X-ray analysis (XRF, PIXE, XRD, SEM-EDX). - Confocal X-ray microscopy (3D Micro-XRF, 3D Micro-PIXE). - Synchrotron, ion beam and neutron based techniques/instrumentation. - FT-IR and Raman spectroscopy. - UV-Vis and NIR absorption/reflectance and fluorescence. - Laser-based analytical techniques (LIBS, etc.). - Magnetic resonance techniques. - Chromatography (GC, HPLC) and mass spectrometry. - Optical imaging and coherence techniques. - Mobile spectrometry and remote sensing

Gaze-Based Human-Robot Interaction by the Brunswick Model

We present a new paradigm for human-robot interaction based on social signal processing, and in particular on the Brunswick model. Originally, the Brunswick model copes with face-to-face dyadic interaction, assuming that the interactants are communicating through a continuous exchange of non verbal social signals, in addition to the spoken messages. Social signals have to be interpreted, thanks to a proper recognition phase that considers visual and audio information. The Brunswick model allows to quantitatively evaluate the quality of the interaction using statistical tools which measure how effective is the recognition phase. In this paper we cast this theory when one of the interactants is a robot; in this case, the recognition phase performed by the robot and the human have to be revised w.r.t. the original model. The model is applied to Berrick, a recent open-source low-cost robotic head platform, where the gazing is the social signal to be considered