Search results for " image processing"
showing 10 items of 2323 documents
An Adaptive Bayesian System for Context-Aware Data Fusion in Smart Environments
2017
The adoption of multi-sensor data fusion techniques is essential to effectively merge and analyze heterogeneous data collected by multiple sensors, pervasively deployed in a smart environment. Existing literature leverages contextual information in the fusion process, to increase the accuracy of inference and hence decision making in a dynamically changing environment. In this paper, we propose a context-aware, self-optimizing, adaptive system for sensor data fusion, based on a three-tier architecture. Heterogeneous data collected by sensors at the lowest tier are combined by a dynamic Bayesian network at the intermediate tier, which also integrates contextual information to refine the infe…
Modular Middleware for Gestural Data and Devices Management
2017
In the last few years, the use of gestural data has become a key enabler for human-computer interaction (HCI) applications. The growing diffusion of low-cost acquisition devices has thus led to the development of a class of middleware aimed at ensuring a fast and easy integration of such devices within the actual HCI applications. The purpose of this paper is to present a modular middleware for gestural data and devices management. First, we describe a brief review of the state of the art of similar middleware. Then, we discuss the proposed architecture and the motivation behind its design choices. Finally, we present a use case aimed at demonstrating the potential uses as well as the limit…
Multidirectional Scratch Detection and Restoration in Digitized Old Images
2010
Line scratches are common defects in old archived videos, but similar imperfections may occur in printed images, in most cases by reason of improper handling or inaccurate preservation of the support. Once an image is digitized, its defects become part of that image. Many state-of-the-art papers deal with long, thin, vertical lines in old movie frames, by exploiting both spatial and temporal information. In this paper we aim to face with a more challenging and general problem: the analysis of line scratches in still images, regardless of their orientation, color, and shape. We present a detection/restoration method to process this defect.
A knowledge based architecture for the virtual restoration of ancient photos
2017
Abstract Historical images are essential documents of the recent past. Nevertheless, time and bad preservation corrupt their physical supports. Digitization can be the solution to extend their “lives”, and digital techniques can be used to recover lost information. This task is often difficult and time-consuming, if commercial restoration tools are used for the purpose. A new solution is proposed to help non-expert users in restoring their damaged photos. First, we defined a dual taxonomy for the defects in printed and digitized photos. We represented our restoration domain with an ontology and we created some rules to suggest actions to perform in case of some specific events. Classes and …
Exploiting Visual Saliency Algorithms for Object-Based Attention: A New Color and Scale-Based Approach
2017
Visual Saliency aims to detect the most important regions of an image from a perceptual point of view. More in detail, the goal of Visual Saliency is to build a Saliency Map revealing the salient subset of a given image by analyzing bottom-up and top-down factors of Visual Attention. In this paper we proposed a new method for Saliency detection based on colour and scale analysis, extending our previous work based on SIFT spatial density inspection. We conducted several experiments to study the relationships between saliency methods and the object attention processes and we collected experimental data by tracking the eye movements of thirty viewers in the first three seconds of observation o…
A hybrid system for malware detection on big data
2018
In recent years, the increasing diffusion of malicious software has encouraged the adoption of advanced machine learning algorithms to timely detect new threats. A cloud-based approach allows to exploit the big data produced by client agents to train such algorithms, but on the other hand, poses severe challenges on their scalability and performance. We propose a hybrid cloud-based malware detection system in which static and dynamic analyses are combined in order to find a good trade-off between response time and detection accuracy. Our system performs a continuous learning process of its models, based on deep networks, by exploiting the growing amount of data provided by clients. The prel…
State of the art in passive digital image forgery detection: copy-move image forgery
2017
Authenticating digital images is increasingly becoming important because digital images carry important information and due to their use in different areas such as courts of law as essential pieces of evidence. Nowadays, authenticating digital images is difficult because manipulating them has become easy as a result of powerful image processing software and human knowledge. The importance and relevance of digital image forensics has attracted various researchers to establish different techniques for detection in image forensics. The core category of image forensics is passive image forgery detection. One of the most important passive forgeries that affect the originality of the image is cop…
A Framework for Parallel Assessment of Reputation Management Systems
2016
Several distributed applications running over the Internet use Reputation Management Systems (RMSs) to guarantee reliable interactions among unknown agents. Because of the heterogeneity of the existing RMSs, their assessment in terms of correctness and resistance to security attacks is not a trivial task. This work addresses this issue by presenting a novel parallel simulator aimed to support researchers in evaluating the performances of a RMS since the design phase. Preliminary results obtained by simulating two different attacks confirm the suitability of the proposed framework to evaluate different RMSs.
A Novel Time Series Kernel for Sequences Generated by LTI Systems
2017
The recent introduction of Hankelets to describe time series relies on the assumption that the time series has been generated by a vector autoregressive model (VAR) of order p. The success of Hankelet-based time series representations prevalently in nearest neighbor classifiers poses questions about if and how this representation can be used in kernel machines without the usual adoption of mid-level representations (such as codebook-based representations). It is also of interest to investigate how this representation relates to probabilistic approaches for time series modeling, and which characteristics of the VAR model a Hankelet can capture. This paper aims at filling these gaps by: deriv…
A Fog-Based Application for Human Activity Recognition Using Personal Smart Devices
2019
The diffusion of heterogeneous smart devices capable of capturing and analysing data about users, and/or the environment, has encouraged the growth of novel sensing methodologies. One of the most attractive scenarios in which such devices, such as smartphones, tablet computers, or activity trackers, can be exploited to infer relevant information is human activity recognition (HAR). Even though some simple HAR techniques can be directly implemented on mobile devices, in some cases, such as when complex activities need to be analysed timely, users’ smart devices can operate as part of a more complex architecture. In this article, we propose a multi-device HAR framework that exploits the fog c…