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The majority of automatic fingerprint matching systems depends on the comparison of the local ridge characteristics (bifurcation and termination), and a critical step in fingerprint matching is to extract minutiae from the input image. In this work we propose a novel ridge following algorithm based on a robust image enhancement filtering. Several experiments are carried out, showing the performances of the proposed approach.

In the context of a driven and collaborative on-line learning, multimedia resources, in particular videos, are more and more used. Due to their complex nature, there is the increasing need to manage the video contents in order to ensure a more fine-grained tracking on audio-video assets and obtain a continual feedback of student activities. In according to the ADL’s SCORM model, a video is usually assumed to be an atomic Learning Object: this assumption restricts the interaction between client and Learning Management System to a merely ON/OFF tracking process and limits the reusability of such type of contents. In our approach the video resources are considered as SCO or, more in details, as a SCO container; in this manner, each single component asset can be just a frame or a video segment in order to achieve the wanted tracking grain size. In according to the last hypothesis the main focus of the paper has been the design and implementation of an architecture for the tracking of video SCOs in web learning environment

The first step in a video indexing process is the segmentation of videos into meaningful parts called shots. In this paper we present a formal model of the video shot segmentation process. Starting from a mathematical characterization of the most common transition effects, a video segmentation algorithm capable to detect both abrupt and gradual transitions is proposed. The proposed algorithm is based on the computation of an arbitrary similarity measure between consecutive frames of a video. The algorithm has been tested adopting a similarity metric based on the Animate Vision theory and results have been reported.

The main focus of the paper is the design of a system which integrates data from heterogeneous multimedia repositories in a single, unified database view, in order to build a multimedia Information system capable of integrating data that resides in different data base systems as well as in a variety of non-database data servers. We describe the object oriented data model used as a common model from the wrapper/mediator system, so providing a simple mechanism for enabling the integration while maintaining the independence of the repositories and providing a semantic description of the extracted data.

In this paper we present a novel approach for video shot segmentation based on active vision techniques. A continuity function is introduced taking into account the results from biological vision and experiments and discussions are carried out.

In this paper, we describe a novel Semantic Content Management System (SCMS) able to handle multimedia contents of different kinds (e.g. texts and images) using the related semantics and capable of supporting e-gov applications in different scenarios. All the information is described using semantic metadata semiautomatically extracted from multimedia data, which enriches the browsing experience and enables semantic contents' authoring and queries. To this aim, several Semantic Web technologies have been exploited : RDF/ OWL for data modeling and representation, SPARQL as querying language, Multimedia Information Extraction techniques for content annotation, W3C standard models, vocabularies and micro-formats for resource description. In addition, we propose for entity annotation issue the LOD approach. As an application scenario of the platform, we report a system customization useful for managing the semantic matching between the required professional profiles by a Public Administration and the available skills in a set of curricula vitae with respect to a given call.

Recommender systems help people in retrieving information that match their preferences by recommending products or services from a large number of candidates, and support people in making decisions in various contexts: what items to buy, which movie to watch or even who they can invite to their social network. They are especially useful in environments characterized by a vast amount of information, since they can e.ectively select a small subset of items that appear to fit the user's needs. We present the main points related to recommender systems using multimedia data, especially for social networks applications. We also describe, as an example, a framework developed at the University of Naples Federico II. It provides customized recommendations by originally combining intrinsic features of multimedia objects (low-level and semantic similarity), past behavior of individual users and overall behavior of the entire community of users, and eventually considering users' preferences and social interests.

As manufacturing and commerce become ever more global, companies are dependent increasingly upon the efficient and effective sharing of information with their partners, wherever they may be. In this paper, we propose an architecture that, exploiting the Semantic Web technologies, has the objective of allowing semantic interoperability among software agents for logistic applications that preserves, not only the semantic of transmitted messages, but also the subjectivity of agent's world vision in the communication. Such an architecture takes advantage by a particular communication model called Semantic Triangle in which communication agents share the referents (real world objects) and not the references (mental image or impression of a real object from the sender agents point of view),thus ensuring an effective semantic interoperability in the information exchange process.

The aim of the IDEA (Instructional DEsigner Applications) is to enhance contents production process making available to the content owners very simple and useful authoring tools for the digitalization, organization and classification of their knowledge. The results of a such process can be seen as prototypes of multimedia courses, obtained with reduced costs and times, that can be directly imported into a generic Learning Management Systems (LMS) or used as startpoint in generating more complex multimedia products. .

In this paper we present our development experience of a Semantic Content Management System able to handle and manage uniformly heterogeneous contents of different kinds (texts, video and im- Ages) considering the related semantics. To this aim, we exploit several Semantic Web technologies: RDF/OWL for data modeling and repre- sentation, SPARQL as querying language, Multimedia Information Ex- Traction techniques, W3C standard models for creating taxonomies and resource annotation, vocabularies and microformats. We also follow the Best Practices and Issues for the Web of Data by reusing LOD information for the Entity annotation of the managed content, thus providing CMS with advanced capabilities such as contents' authoring and tagging.