Maurizio Lucenteforte

The representation of binary data in a compact, printable, efficient, and often human-readable format is essential in numerous computing applications, mainly driven by the limitations of systems and communication protocols not designed to handle arbitrary 8-bit binary data. This paper provides a comprehensive survey and an extensive characterization of printable encoding schemes, tracing their evolution from historical methods to contemporary solutions for representing, storing, and transmitting binary data using restricted character sets. The review includes a foundational analysis of fundamental character encodings, proposes a layered model for the classification of printable encodings, and examines various schemes based on their numerical bases, alphabets, and functional characteristics. Algorithms, key design trade-offs, the impact of relevant standards, security implications, performance considerations, and human factors are systematically discussed, aiming to offer a detailed understanding of the current context and open challenges.

This paper extends and improves the performance of a digital reversible watermarking algorithm based on histogram shifting presented in previous works. The considered algorithm exploits the property of image histograms of some kinds of medical images which present many contiguous 0-runs, i.e., a comb structure in the gray level frequencies. In particular, radiographic images exhibit this structure after contrast enhancement during the acquisition process. The previous work suggested performing gray-level histogram shifting according to a local optimization technique. In this paper, we apply combinatorial optimization techniques to entire blocks of contiguous 0-runs using a non-linear objective function transformed to fit a linear optimization algorithm. The obtained results show a meaningful improvement in the payload capacity of the original data-hiding method. A mild Peak Signal-to-Noise Ratio (PSNR) reduction is still acceptable for a qualitative preview of the images, which can be completely restored to their original cover form thanks to the reversibility of the method.

This paper extends and improves the performance of a digital reversible watermarking algorithm presented in a previous work. The considered algorithm exploits the property of image histograms of some kinds of medical images which present many contiguous 0-runs, i.e., a comb structure in the gray level frequencies. The previous work suggested to perform some gray level histogram shifting according to a local optimization technique. In this paper we apply combinatorial optimization techniques to entire blocks of contiguous 0-runs, improving the payload capacity of the reversible data hiding method.

The accurate and consistent border segmentation plays an important role in the tumor volume estimation and its treatment in the field of Medical Image Segmentation. Globally, Lung cancer is one of the leading causes of death and the early detection of lung nodules is essential for the early cancer diagnosis and survival rate of patients. The goal of this study was to demonstrate the feasibility of Deephealth toolkit including PyECVL and PyEDDL libraries to precisely segment lung nodules. Experiments for lung nodules segmentation has been carried out on UniToChest using PyECVL and PyEDDL, for data pre-processing as well as neural network training. The results depict accurate segmentation of lung nodules across a wide diameter range and better accuracy over a traditional detection approach. The datasets and the code used in this paper are publicly available as a baseline reference.

This demo paper describes the semantic query interpretation model adopted in the OnToMap Participatory GIS and presents its benefits to information retrieval and personalized information presentation.

We present the information retrieval model adopted in the OnToMap Participatory GIS. The model addresses the limitations of keyword-based and category-based search by semantically interpreting the information needs specified in free-text search queries. The model is based on an ontological representation of linguistic and encyclopaedic knowledge, which makes it possible to exploit terms and synonyms occurring in the definitions of concepts to flexibly match the user's and system's terminologies. This feature enables users to query the application using their own vocabulary.

Multiview video plus depth (MVD) is the most popular 3D video format where the texture images contain the color information and the depth maps represent the geometry of the scene. The depth maps are exploited to obtain intermediate views to enable 3D-TV and free-viewpoint applications using the depth image based rendering (DIBR) techniques. DIBR is used to get an estimate of the intermediate views but has to cope with depth errors, occlusions, imprecise camera parameters, re-interpolation, to mention a few issues. Therefore, being able to evaluate the true perceptual quality of synthesized images is of paramount importance for a high quality 3D experience. In this paper, we present a novel algorithm to assess the quality of the synthesized images in the absence of the corresponding references. The algorithm uses the original views from which the virtual image is generated to estimate the distortion induced by the DIBR process. In particular, a block-based perceptual feature matching based on signal phase congruency metric is devised to estimate the synthesis distortion. The experiments worked out on standard DIBR synthesized database show that the proposed algorithm achieves high correlation with the subjective ratings and outperforms the existing 3D quality assessment algorithms.

With the convergence of geographical information systems (GIS) and internet technology, the public administration is starting to use online maps as a web-based bidirectional communication channel with the population: maps are used: i) in public portals, for publishing and crowdsourcing information about the territory; ii) in policy-making, for defining a community vision of the territory and for involving people in public choices. Both cases raise challenges related to the large amount of data handled in the maps, and to their lack of group collaboration support. We attempted to address these issues by developing an information-sharing model, and a testbed software application, that support group management and the generation of persistent, custom community maps focused on the user's interests. Our model builds on tag-based user profiles and on information filtering. This paper describes our model and the results of an evaluation of the GroupMapping application, based on it.