Spatial Prediction in the H.264/AVC FRExt Coder and its Optimization

The chapter presents a review of the fast spatial prediction strategy that were designed for the Intra coding mode of the video coding standard H.264/AVC. At the end, the author presents an effective strategy based on belief propagation message passing

Creation of Controlled Defects Inside Colloidal Crystal Arrays with a Focused Ion Beam

In this work the reliability of the focused-ion-beam (FIB) patterning on polystyrene (PS) colloidal crystals at different scales is determined. Ordered arrays of PS spheres (465 nm) are successfully modified by selectively removing a single sphere. The water-vapor assisted FIB milling is crucial to obtain this result. Furthermore, isolated PS spheres are FIB drilled with or without chemically enhanced milling aiming at the exploration of the limits of such a technique. These controlled defects created using the FIB-assisted techniques may be helpful in preparing mockups of photonic crystals, sensors or as colloidal masks for diverse lithographic processes

Continual Learning for LiDAR Semantic Segmentation: Class-Incremental and Coarse-to-Fine strategies on Sparse Data

During the last few years, continual learning (CL) strategies for image classification and segmentation have been widely investigated designing innovative solutions to tackle catastrophic forgetting, like knowledge distillation and self-inpainting. However, the application of continual learning paradigms to point clouds is still unexplored and investigation is required, especially using architectures that capture the sparsity and uneven distribution of LiDAR data. The current paper analyzes the problem of class incremental learning applied to point cloud semantic segmentation, comparing approaches and state-of-the-art architectures. To the best of our knowledge, this is the first example of class-incremental continual learning for LiDAR point cloud semantic segmentation. Different CL strategies were adapted to LiDAR point clouds and tested, tackling both classic fine-tuning scenarios and the Coarse-to-Fine learning paradigm. The framework has been evaluated through two different architectures on SemanticKITTI, obtaining results in line with state-of-the-art CL strategies and standard offline learning

Nanomanufacturing of titania interfaces with controlled structural and functional properties by supersonic cluster beam deposition

Great emphasis is placed on the development of integrated approaches for the synthesis and the characterization of ad hoc nanostructured platforms, to be used as templates with controlled morphology and chemical properties for the investigation of specific phenomena of great relevance for technological applications in interdisciplinary fields such as biotechnology, medicine and advanced materials. Here we discuss the crucial role and the advantages of thin film deposition strategies based on cluster-assembling from supersonic cluster beams. We select cluster-assembled nanostructured titania (ns-TiO2) as a case study to demonstrate that accurate control over morphological parameters can be routinely achieved, and consequently over several relevant interfacial properties and phenomena, like surface charging in a liquid electrolyte, and proteins and nanoparticles adsorption

Discriminating multiple JPEG compression using first digit features

The analysis of JPEG double-compressed images is a problem largely studied by the multimedia forensics community, as it might be exploited, e.g., for tampering localization or source device identification. In many practical scenarios, like photos uploaded on blogs, on-line albums, and photo sharing web sites, images might be JPEG compressed several times. However, the identification of the number of compression stages applied to an image remains an open issue. We proposes a forensic method based on the analysis of the distribution of the first significant digits of the discrete cosine transform coefficients, which follow Benford's law in images compressed just once. Then, the detector is optimized and extended in order to identify accurately the number of compression stages applied to an image. The experimental validation considers up to four consecutive compression stages and shows that the proposed approach extends and outperforms the previously-published algorithms for double JPEG compression detection

All-for-One and One-For-All: Deep learning-based feature fusion for Synthetic Speech Detection

Recent advances in deep learning and computer vision have made the synthesis and counterfeiting of multimedia content more accessible than ever, leading to possible threats and dangers from malicious users. In the audio field, we are witnessing the growth of speech deepfake generation techniques, which solicit the development of synthetic speech detection algorithms to counter possible mischievous uses such as frauds or identity thefts. In this paper, we consider three different feature sets proposed in the literature for the synthetic speech detection task and present a model that fuses them, achieving overall better performances with respect to the state-of-the-art solutions. The system was tested on different scenarios and datasets to prove its robustness to anti-forensic attacks and its generalization capabilities.Comment: Accepted at ECML-PKDD 2023 Workshop "Deep Learning and Multimedia Forensics. Combating fake media and misinformation

On the use of Benford's law to detect GAN-generated images

The advent of Generative Adversarial Network (GAN) architectures has given anyone the ability of generating incredibly realistic synthetic imagery. The malicious diffusion of GAN-generated images may lead to serious social and political consequences (e.g., fake news spreading, opinion formation, etc.). It is therefore important to regulate the widespread distribution of synthetic imagery by developing solutions able to detect them. In this paper, we study the possibility of using Benford's law to discriminate GAN-generated images from natural photographs. Benford's law describes the distribution of the most significant digit for quantized Discrete Cosine Transform (DCT) coefficients. Extending and generalizing this property, we show that it is possible to extract a compact feature vector from an image. This feature vector can be fed to an extremely simple classifier for GAN-generated image detection purpose

Stay True to the Sound of History: Philology, Phylogenetics and Information Engineering in Musicology

This work investigates computational musicology for the study of tape music works tackling the problems concerning stemmatics. These philological problems have been analyzed with an innovative approach considering the peculiarities of audio tape recordings. The paper presents a phylogenetic reconstruction strategy that relies on digitizing the analyzed tapes and then converting each audio track into a two-dimensional spectrogram. This conversion allows adopting a set of computer vision tools to align and equalize different tracks in order to infer the most likely transformation that converts one track into another. In the presented approach, the main editing techniques, intentional and unintentional alterations and different configurations of a tape recorded are estimated in phylogeny analysis. The proposed solution presents a satisfying robustness to the adoption of the wrong reading setup together with a good reconstruction accuracy of the phylogenetic tree. The reconstructed dependencies proved to be correct or plausible in 90% of the experimental cases

Historic city centers after destructive seismic events, the case of finale Emilia during the 2012 Emilia-Romagna earthquake: Advanced numerical modelling on four case studies

Introduction: The recent wave of seismic shocks in Central Italy (2016) had once more disastrous consequences for the local monuments, which consisted of old masonry churches and towers. The permanent, seismic-induced damage to cultural heritage has become a serious issue that can no longer be downsized, and questions have been raised about how to satisfactorily assess the vulnerability of such heritage in advance. This paper deals with the investigations into the actual condition of a historic city center partially destroyed by the seismic sequence occurred in May 2012 in Emilia-Romagna. Namely, the case of Finale Emilia â\u80\u93 a small to medium-sized village located at the very center of the stricken area â\u80\u93 is considered. Methods: Three important heritage masterpieces were numerically analyzed using Finite Element meshes to deepen the knowledge of their seismic vulnerability and try to avoid similar disasters in the future. The first structure is a masonry castle known as â\u80\u9cCastello delle Roccheâ\u80\u9d, which underwent severe damages during the seismic sequence. The second and third examples deal with the structural analysis of two towers, both collapsed due to the quakes: the Fortified Tower of the castle and the Clock Tower of the village. The last analysis is devoted to study the seismic behavior of a medium-sized masonry church (Santa Maria del Rosario), heavily damaged by the seismic sequence and whose bell tower collapsed due to the formation of a hinge at mid-height. Results and Conclusion: Numerical models were created for all the buildings involved, and a variety of advanced analyses were carried out, including nonlinear static and dynamic ones, to have a deep insight into their expected vulnerability, also finding reasonable correspondence between the numerical results and the actual state of damage observed during the surveys made in the aftermath of the seismic events

Joint denoising and interpolation of depth maps for MS Kinect sensors

ABSTRACT Infrared structured light sensors are widely employed for control applications, gaming, acquisition of dynamic and static 3D scenes. Recent developments have lead to the availability on the market of low-cost sensors which prove to be extremely sensitive to noise, light conditions, materials, the surface nature of the objects, and their distance from the camera. As a matter of fact, accurate denoising and interpolation strategies are needed. The paper presents a quality enhancement strategy for depth maps targeting low-cost IR structured light sensors. The approach has been tested using the MS Xbox Kinect device in both indoor and outdoor scenarios under different light conditions