A multimedia access control language for virtual and ambient intelligence environments

Access control models are becoming increasingly important in several application domains especially in distributed environments like those addressed by Web Services. Established approaches such as DAC [16] , MAC [16] RBAC [11, 12, 22] and others [6, 5, 15, 1] suggest representing users in different ways (labels, roles, credentials, etc.) in order to facilitate the association of authorization and access control policies. In intelligent and virtual ambient applications, users exist in a controlled environment equipped with multimedia sensors such as cameras and microphones, and use their terminals in several application environments. In this paper, we study the problem of integrating multimedia objects into access control models and particularly role-based ones. Here, we describe a Multimedia Access Control Language (M 2ACL) in which users and roles are described by using sets of mul- timedia objects,greatly increasing the flexibility of access control policies and their applicability to virtual and ambient intelligence (AmI) environments. We address potential risks related to the use of multimedia objects by defining the concept of filter functions used to aggregate a set of values into a relevant one.Finally,we present a set of functional specification and the experiments conducted to validate the proposed approach

The State of the Art of Medical Imaging Technology: from Creation to Archive and Back

Medical imaging has learnt itself well into modern medicine and revolutionized medical industry in the last 30 years. Stemming from the discovery of X-ray by Nobel laureate Wilhelm Roentgen, radiology was born, leading to the creation of large quantities of digital images as opposed to film-based medium. While this rich supply of images provides immeasurable information that would otherwise not be possible to obtain, medical images pose great challenges in archiving them safe from corrupted, lost and misuse, retrievable from databases of huge sizes with varying forms of metadata, and reusable when new tools for data mining and new media for data storing become available. This paper provides a summative account on the creation of medical imaging tomography, the development of image archiving systems and the innovation from the existing acquired image data pools. The focus of this paper is on content-based image retrieval (CBIR), in particular, for 3D images, which is exemplified by our developed online e-learning system, MIRAGE, home to a repository of medical images with variety of domains and different dimensions. In terms of novelties, the facilities of CBIR for 3D images coupled with image annotation in a fully automatic fashion have been developed and implemented in the system, resonating with future versatile, flexible and sustainable medical image databases that can reap new innovations

Salient-Object-Based Image Query by Visual Content.

La recherche d\'image par le contenu (en anglais Content-Based Image Retrieval, CBIR) est un domaine de recherche très actif depuis plusieurs années. L\'appariement exact n\'étant ni possible ni souhaitable avec des images, l\'approche la plus utilisée consiste à calculer un score de similarité entre les images via une comparaison de leurs caractéristiques physiques. Pour certaines applications, une fonction additionnelle importante est celle qui permet de définir des \"objets d\'intérêt\" à l\'intérieur des images, les calculs de similarité prenant alors en compte la comparaison entre ces objets. Les problèmes liés aux objets d\'intérêt ne sont que peu voire pas du tout pris en compte par les systèmes actuels de CBIR. Dans cet article, nous proposons un modèle de stockage de données relatives à des images qui permet de représenter les propriétés spatiales d\'objets d\'intérêt. De plus, nous définissons une extension de l\'opérateur de sélection basé sur la similarité présenté dans de précédents travaux qui rend possible la sélection basée sur les objets d\'intérêt. Nous proposons également des opérateurs spécifiques qui permettent de calculer les relations spatiales entre une image et les objets d\'intérêt qu\'elle contient. Ces propositions sont concrètement implémentées dans une extension de notre système de recherche d\'image par le contenu EMIMS qui définit le système EMIMS-S (Extended Medical Image Management System to support Salient objects). Nous présentons enfin une évaluation expérimentale de l\'efficacité du traitement de requêtes basées sur les objets d\'intérêt telles que nous les avons définies.Content-Based Image Retrieval (CBIR) has attracted much attention of the research community. As exact matching is not possible with image retrieval, the approach is to use similarity-based matching using the global features of the entire image to compute a similarity score between two images. Equally important is the use of salient-objects: objects in an image that are of particular interest, as the basis of similarity-based computation. However, the current works on CBIR do not address very well the issues related to salient-objects. In this work, we propose a data repository model so that spatial features of salient objects are captured. Moreover, we propose an extension to the similarity-based selection operator defined earlier to allow salient object based selection. We also propose spatial operators that can be used to compute spatial relations between an image and its contained salient objects. To demonstrate the viability of our proposals, we extend a previous system named EMIMS, to develop EMIMS-S (Extended Medical Image Management System to support Salient objects). We also experimentally evaluate the retrieval effectiveness of salient-objects-based image queries. Keywords: Salient-object-based image retrieval, image database, image data model, similarity-based algebra, spatial relation of salient-objects; Recherche d\'image basée sur les objets d\'intérêt, bases de données d\'image, modèle de données d\'image, algèbre basée sur la similarité, relations spatiales entre objets d\'intérêt, EMIMS-S. Journal des Sciences Pour l\'Ingénieur. Vol. 7 2006: pp. 79-8

Signal processing for image enhancement and multimedia processing

Traditionally, signal processing techniques lay at the foundation of multimedia data processing and analysis. In the past few years, a new wave of advanced signal-processing techniques has delivered exciting results, increasing system's capabilities of efficiently exchanging image data and extracting useful knowledge. Signal Processing for Image Enhancement and Multimedia Processing is written by global experts who have extended the best papers presented at the SITIS 2006 International Conference to chapter versions. This edited book presents research results on the application of advanced signal processing techniques for improving the value of image and video data. In addition, this volume includes discussions on feature-based techniques for deep, feature-oriented analysis of images, plus new results on video coding on the time-honored topic of securing image information. Signal Processing for Image Enhancement and Multimedia Processing is designed for a professional audience of practitioners and researchers in industry. It is also suitable as a reference or secondary text for advanced-level students in computer science and engineering

Object 3D reconstruction based on photometric stereo and inverted rendering

Methods for 3D reconstruction such as Photometric stereo recover the shape and reflectance properties using multiple images of an object taken with variable lighting conditions from a fixed viewpoint. Photometric stereo assumes that a scene is illuminated only directly by the illumination source. As result, indirect illumination effects due to inter-reflections introduce strong biases in the recovered shape. Our suggested approach is to recover scene properties in the presence of indirect illumination. To this end, we proposed an iterative PS method combined with a reverted Monte-Carlo ray tracing algorithm to overcome the inter-reflection effects aiming to separate the direct and indirect lighting. This approach iteratively reconstructs a surface considering both the environment around the object and its concavities. We demonstrate and evaluate our approach using three datasets and the overall results illustrate improvement over the classic PS approaches.Comment: 8 pages, 11 figure, SITIS conferenc

A New Time Series Mining Approach Applied to Multitemporal Remote Sensing Imagery

Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)In this paper, we present a novel unsupervised algorithm, called CLimate and rEmote sensing Association patteRns Miner, for mining association patterns on heterogeneous time series from climate and remote sensing data integrated in a remote sensing information system developed to improve the monitoring of sugar cane fields. The system, called RemoteAgri, consists of a large database of climate data and low-resolution remote sensing images, an image preprocessing module, a time series extraction module, and time series mining methods. The preprocessing module was projected to perform accurate geometric correction, what is a requirement particularly for land and agriculture applications of satellite images. The time series extraction is accomplished through a graphical interface that allows easy interaction and high flexibility to users. The time series mining method transforms series to symbolic representation in order to identify patterns in a multitemporal satellite images and associate them with patterns in other series within a temporal sliding window. The validation process was achieved with agroclimatic data and NOAA-AVHRR images of sugar cane fields. Results show a correlation between agroclimatic time series and vegetation index images. Rules generated by our new algorithm show the association patterns in different periods of time in each time series, pointing to a time delay between the occurrences of patterns in the series analyzed, corroborating what specialists usually forecast without having the burden of dealing with many data charts.5111140150EmbrapaFundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)SticAmsudMicrosoft ResearchFundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES

RDF-GL : a SPARQL-based graphical query language for RDF

This chapter presents RDF-GL, a graphical query language (GQL) for RDF. The GQL is based on the textual query language SPARQL and mainly focuses on SPARQL SELECT queries. The advantage of a GQL over textual query languages is that complexity is hidden through the use of graphical symbols. RDF-GL is supported by a Java-based editor, SPARQLinG, which is presented as well. The editor does not only allow for RDF-GL query creation, but also converts RDF-GL queries to SPARQL queries and is able to subsequently execute these. Experiments show that using the GQL in combination with the editor makes RDF querying more accessible for end users