A Novel Framework for Highlight Reflectance Transformation Imaging

We propose a novel pipeline and related software tools for processing the multi-light image collections (MLICs) acquired in different application contexts to obtain shape and appearance information of captured surfaces, as well as to derive compact relightable representations of them. Our pipeline extends the popular Highlight Reflectance Transformation Imaging (H-RTI) framework, which is widely used in the Cultural Heritage domain. We support, in particular, perspective camera modeling, per-pixel interpolated light direction estimation, as well as light normalization correcting vignetting and uneven non-directional illumination. Furthermore, we propose two novel easy-to-use software tools to simplify all processing steps. The tools, in addition to support easy processing and encoding of pixel data, implement a variety of visualizations, as well as multiple reflectance-model-fitting options. Experimental tests on synthetic and real-world MLICs demonstrate the usefulness of the novel algorithmic framework and the potential benefits of the proposed tools for end-user applications.Terms: "European Union (EU)" & "Horizon 2020" / Action: H2020-EU.3.6.3. - Reflective societies - cultural heritage and European identity / Acronym: Scan4Reco / Grant number: 665091DSURF project (PRIN 2015) funded by the Italian Ministry of University and ResearchSardinian Regional Authorities under projects VIGEC and Vis&VideoLa

Shape enhancement for rapid prototyping

Many applications, for instance in the reverse engineering and cultural heritage field, require to build a physical replica of 3D digital models. Recent 3D printers can easily perform this task in a relatively short time and using color to reproduce object textures. However, the finite resolution of printers and, most of all, some peculiar optical and physical properties of the used materials reduce their perceptual quality. The contribution of this paper is a shape enhancing technique, which allows users to increase readability of the tiniest details in physical replicas, without requiring manual post-reproduction interventions.831-840Pubblicat

A steganalytic algorithm for 3D polygonal meshes

We propose a steganalytic algorithm for watermarks embedded by Cho et al.'s mean-based algorithm [1]. The main observation is that while in a clean model the means of Cho et al.'s normalized histogram bins are expected to follow a Gaussian distribution, in a marked model their distribution will be bimodal. The proposed algorithm estimates the number of bins through an exhaustive search and then the presence of a watermark is decided by a tailor made normality test. We also propose a modification of Cho et al.'s algorithm which is more resistant to the steganalytic attack and offers an improved robustness/capacity trade-off

Multispectral RTI Analysis of Heterogeneous Artworks

We propose a novel multi-spectral reflectance transformation imaging (MS-RTI) framework for the acquisition and direct analysis of the reflectance behavior of heterogeneous artworks. Starting from free-form acquisitions, we compute per-pixel calibrated multi-spectral appearance profiles, which associate a reflectance value to each sampled light direction and frequency. Visualization, relighting, and feature extraction is performed directly on appearance profile data, applying scattered data interpolation based on Radial Basis Functions to estimate per-pixel reflectance from novel lighting directions. We demonstrate how the proposed solution can convey more insights on the object materials and geometric details compared to classical multi-light methods that rely on low-frequency analytical model fitting eventually mixed with a separate handling of high-frequency components, hence requiring constraining priors on material behavior. The flexibility of our approach is illustrated on two heterogeneous case studies, a painting and a dark shiny metallic sculpture, that showcase feature extraction, visualization, and analysis of high-frequency properties of artworks using multi-light, multi-spectral (Visible, UV and IR) acquisitions.Terms: "European Union (EU)" & "Horizon 2020" / Action: H2020-EU.3.6.3. - Reflective societies - cultural heritage and European identity / Acronym: Scan4Reco / Grant number: 665091the DSURF (PRIN 2015) project funded by the Italian Ministry of University and ResearchSardinian Regional Authorities under projects VIGEC and Vis&VideoLa

A Practical Reflectance Transformation Imaging Pipeline for Surface Characterization in Cultural Heritage

We present a practical acquisition and processing pipeline to characterize the surface structure of cultural heritage objects. Using a free-form Reflectance Transformation Imaging (RTI) approach, we acquire multiple digital photographs of the studied object shot from a stationary camera. In each photograph, a light is freely positioned around the object in order to cover a wide variety of illumination directions. Multiple reflective spheres and white Lambertian surfaces are added to the scene to automatically recover light positions and to compensate for non-uniform illumination. An estimation of geometry and reflectance parameters (e.g., albedo, normals, polynomial texture maps coefficients) is then performed to locally characterize surface properties. The resulting object description is stable and representative enough of surface features to reliably provide a characterization of measured surfaces. We validate our approach by comparing RTI-acquired data with data acquired with a high-resolution microprofilometer.Terms: "European Union (EU)" & "Horizon 2020" / Action: H2020-EU.3.6.3. - Reflective societies - cultural heritage and European identity / Acronym: Scan4Reco / Grant number: 66509

Photo repair and 3d structure from flatbed scanners

We introduce a technique that allows 3D information to be captured from a conventional flatbed scanner. The technique requires no hardware modification and allows untrained users to easily capture 3D datasets. Once captured, these datasets can be used for interactive relighting and enhancement of surface detail on physical objects. We have also found that the method can be used to scan and repair damaged photographs. Since the only 3D structure on these photographs will typically be surface tears and creases, our method provides an accurate procedure for automatically detecting these flaws without any user intervention. Once detected, automatic techniques, such as infilling and texture synthesis, can be leveraged to seamlessly repair such damaged areas. We first present a method that is able to repair damaged photographs with minimal user interaction and then show how we can achieve similar results using a fully automatic process

Guided Robust Matte-Model Fitting for Accelerating Multi-light Reflectance

The generation of a basic matte model is at the core of many multi-light reflectance processing approaches, such as Photometric Stereo or Reflectance Transformation Imag- ing. To recover information on objects\u2019 shape and appearance, the matte model is used directly or combined with specialized methods for modeling high-frequency behaviors. Multivariate robust regression offers a general solution to reliably extract the matte com- ponent when source data is heavily contaminated by shadows, inter-reflections, specular- ity, or noise. However, robust multivariate modeling is usually very slow. In this paper, we accelerate robust fitting by drastically reducing the number of tested candidate solu- tions using a guided approach. Our method propagates already known solutions to nearby pixels using a similarity-driven flood-fill strategy, and exploits this knowledge to order possible candidate solutions and to determine convergence conditions. The method has been tested on objects with a variety of reflectance behaviors, showing state-of-the-art accuracy with respect to current solutions, and a significant speed-up without accuracy reduction with respect to multivariate robust regression

Crack Detection in Single- and Multi-Light Images of Painted Surfaces using Convolutional Neural Networks

Cracks represent an imminent danger for painted surfaces that needs to be alerted before degenerating into more severe aging effects, such as color loss. Automatic detection of cracks from painted surfaces' images would be therefore extremely useful for art conservators; however, classical image processing solutions are not effective to detect them, distinguish them from other lines or surface characteristics. A possible solution to improve the quality of crack detection exploits Multi-Light Image Collections (MLIC), that are often acquired in the Cultural Heritage domain thanks to the diffusion of the Reflectance Transformation Imaging (RTI) technique, allowing a low cost and rich digitization of artworks' surfaces. In this paper, we propose a pipeline for the detection of crack on egg-tempera paintings from multi-light image acquisitions and that can be used as well on single images. The method is based on single or multi-light edge detection and on a custom Convolutional Neural Network able to classify image patches around edge points as crack or non-crack, trained on RTI data. The pipeline is able to classify regions with cracks with good accuracy when applied on MLIC. Used on single images, it can give still reasonable results. The analysis of the performances for different lighting directions also reveals optimal lighting directions

Autopsia di uno scarto elettronico

L'obiettivo del progetto era quello di mettere a punto una metodologia a basso impatto ambientale per la dissoluzione dei metalli nobili basata su potenti ed innovativi reagenti non tossici, e quindi alla messa a punto delle condizioni ottimali per il recupero dell'oro da rifiuti elettronici selezionati per reinserirlo nel mercato. Sono stati analizzati i seguenti rifiuti: simcard, cartucce per stampanti, schede madri. Le percentuali di recupero dell'oro stimate sulla base delle prove di laboratorio per le diverse tipologie di rifiuto sono le seguenti: 1. Cartucce di stampante: il recupero dell'oro è quantitativo (100%: mediamente 5 mg recuperati su 5 mg contenuti). 2. SIMCARD: il recupero dell'oro è dell'ordine del 75% (mediamente 0.3 mg recuperati su 0.4 mg contenuti).2007-03-05Sardegna Ricerche, Edificio 2, Località Piscinamanna 09010 Pula (CA) - ItaliaWorkshop - Recupero dell'oro da rifiuti elettrici ed elettronic