Andrea Rosani

An Innovative Microwave-Imaging Technique for Nondestructive Evaluation: Applications to Civil Structures Monitoring and Biological Bodies Inspection

Industrial and biomedical applications of microwave-imaging techniques based on inverse scattering integral relations become more and more important. In order to reduce computational costs and hence allow a quasi real-time processing, an innovative inversion procedure based on the use of a genetic algorithm and on the Sherman-Morrison-Woodbury matrix inversion method is presented. Selected numerical results concerning various scenarios and scatterer dimensions are presented in order to give some indications on the effectiveness and also current limitations of the proposed approach

An innovative microwave imaging technique for non destructive evaluation: applications to civil structures monitoring and biological bodies inspection

Industrial and biomedical applications of microwave imaging techniques based on inverse scattering integral relations have become more and more important. In order to reduce the computational costs allowing a quasi real-time processing, an innovative inversion procedure based on the use of a genetic algorithm and on the Sherman-Morrison-Woodbury matrix inversion method is presented. Selected numerical results concerning various scenarios and scatterer dimensions, are presented in order to give some indications on the effectiveness but also current limitations of the proposed approach.

EventMask: A Game-Based Framework for Event-Saliency Identification in Images

The concept of “event” emerged in recent years as a key feature to efficiently index and retrieve media. Several approaches have been proposed to analyze the relationship between events and media, enable event discovery, and perform event-based media tagging, indexing, and retrieval. Despite the outstanding work done in this area, a major problem that remains open is how to infer the link between visual concepts and events. In particular, the possibility of understanding which perceptual elements allow a human recognizing the event depicted by an image would open new directions in event media discovery. In this paper we introduce the concept of event saliency to define the above event-revealing perceptual elements, and we propose an original method to detect it by exploiting crowd knowledge through gamification. We propose an adversarial game with a hidden purpose, where users are engaged in competitive roles: masking photos to prevent competitors recognizing the related event, and discovering events in photos masked by other players. Rules and incentives are defined to minimize cheating and force players to focus on details that really matter. A suitable algorithm composes the masks created by different players on the same media, thus producing a saliency map that, different from the traditional concept of saliency, does not focus on perceptual prominence but rather on event-related semantics of media. A thorough validation on public datasets is presented, and initial experiments to apply event saliency in detection tasks are proposed . Furthermore, an event-saliency dataset is disclosed to allow further research.

Human behavior recognition using a context-free grammar

Abstract. Automatic recognition of human activities and behaviors is still a challenging problem for many reasons, including limited accuracy of the data acquired by sensing devices, high variability of human behaviors, and gap between visual appearance and scene semantics. Symbolic approaches can significantly simplify the analysis and turn raw data into chains of meaningful patterns. This allows getting rid of most of the clutter produced by low-level processing operations, embedding significant contextual information into the data, as well as using simple syntactic approaches to perform the matching between incoming sequences and models. We propose a symbolic approach to learn and detect complex activities through the sequences of atomic actions. Compared to previous methods based on context-free grammars, we introduce several important novelties, such as the capability to learn actions based on both positive and negative samples, the possibility of efficiently retraining the system in the presence of misclassified or unrecognized events, and the use of a parsing procedure that allows correct detection of the activities also when they are concatenated and/or nested one with each other. An experimental validation on three datasets with different characteristics demonstrates the robustness of the approach in classifying complex human behaviors.

A hierarchical approach to event discovery from single images using MIL framework

In this paper we propose to face the problem of event detection from single images, by exploiting both background information often containing revealing contextual clues and details, which are salient for recognizing the event. Such details are visual objects critical to understand the underlying event depicted in the image and were recently defined in the literature as “event-saliency”. Adopting the Multiple-Instance Learning (MIL) paradigm we propose a hierarchical approach analyzing first the entire picture and then refining the decision on the basis of the event-salient objects. Validation of the proposed method is carried out on two benchmarking datasets and it demonstrates the effectiveness of the proposed hierarchical approach to event discovery from single images.

Smart Camera Reconfiguration in Assisted Home Environments for Elderly Care

Researchers of different fields have been involved in human behavior analysis during the last years. The successful recognition of human activities from video analysis is still a challenging problem. Within this context, applications targeting elderly care are of considerable interest both for public and industrial bodies, especially considering the aging society we are living in. Ambient intelligence (AmI) technologies, intended as the possibility of automatically detecting and reacting to the status of the environment and of the persons, is probably the major enabling factor. AmI technologies require suitable networks of sensors and actuators, as well as adequate processing and communication technologies. In this paper we propose an innovative solution based on a real time analysis of video with application in the field of elderly care. The system performs anomaly detection and proposes the automatic reconfiguration of the camera network for better monitoring of the ongoing event. The developed framework is tested on a publicly available dataset and has also been deployed and evaluated in a real environment.

Human behavior understanding for assisted living by means of hierarchical Context Free Grammars

Human behavior understanding has attracted the attention of researchers in various fields over the last years. Recognizing behaviors with sufficient accuracy from sensors analysis is still an unsolved problem, because of many reasons, including the low accuracy of the data, differences in the human behaviors as well as the gap between low-level sensors data and high-level scene semantics. In this context, an application that is attracting the interest of both public and industrial entities is the possibility to allow elderly or physically impaired people conducting a normal life at home. Ambient intelligence (AmI) technologies, intended as the possibility of automatically detecting and reacting to the status of the environment and of the persons, is probably the major enabling factor for the achievement of such an ambitious objective. AmI technologies require suitable networks of sensors and actuators, as well as adequate processing and communication technologies. In this paper we propose a solution based on context free grammars for human behavior understanding with an application to assisted living. First, the grammars of the different actions performed by a person in his/her daily life are discovered. Then, a longterm analysis of the behavior is used to generate a control grammar, taking care of the context when an action is performed, and adding semantics. The proposed framework is tested on a dataset acquired in a real environment and compared with state of the art methods already available for the problem considered.

Weighted symbolic analysis of human behavior for event detection

Automatic video analysis and understanding has become a high interest research topic, with applications to video browsing, content-based video indexing, and visual surveillance. However, the automation of this process is still a challenging task, due to clutters produced by low-level processing operations. This common problem can be solved by embedding signi cant contextual information into the data, as well as using simple syntactic approaches to perform the matching between actual sequences and models. In this context we propose a novel framework that employs a symbolic representation of complex activities through sequences of atomic actions based on a weighted Context-Free Grammar.

Morphological Processing of Electromagnetic Scattering Data for Enhancing the Reconstruction Accuracy of the Iterative Multi-Scaling Approach

This work presents a methodology to locate and characterize multiple unknown scatterers exploiting the scattered electromagnetic radiation collected on a measurement region outside the area under investigation. In many practical cases, an accurate quantitative imaging of the scenario under test is required and it can be reached by using a high resolution representation of the dielectric profile of the scatterers. Towards this aim, an enhanced iterative multi-resolution procedure that exploits a morphological processing for detecting and focusing on different non-connected regions-of-interest has been developed. A suitable set of representative numerical results will demonstrate that the proposed approach is able to efficiently detect the objects in the imaging scenario and to enhance the accuracy in reconstructing multiple scatterers. This is the authors version of the final version available at IEEE.

Improving the numerical effectiveness of a class of microwave imaging techniques for NDE/NDT with the SMW updating formula

This paper presents an innovative microwave imaging technique suitable for NDT/NDE applications. To reduce the number of problem unknowns as well a the computational load by improving the convergence rate of the process, the available a-priori information is exploited by introducing a computationally-effective procedure for the prediction of the electric field based on the updating Sherman-Morrison-Woodbury (SMW) formula. The numerical results as well as comparisons with state-of-the-art methods confirm the effectiveness, the feasibility, and the robustness of the proposed approach.