Deep Convolutional Neural Networks (DCNNs) have recently been applied successfully to a variety of vision and multimedia tasks, thus driving development of novel solutions in several application domains. Document analysis is a particularly promising area for DCNNs: indeed, the number of available digital documents has reached unprecedented levels, and humans are no longer able to discover and retrieve all the information contained in these documents without the help of automation. Under this scenario, DCNNs offers a viable solution to automate the information extraction process from digital documents. Within the realm of information extraction from documents, detection of tables and charts is particularly needed as they contain a visual summary of the most valuable information contained in a document. For a complete automation of visual information extraction process from tables and charts, it is necessary to develop techniques that localize them and identify precisely their boundaries. In this paper we aim at solving the table/chart detection task through an approach that combines deep convolutional neural networks, graphical models and saliency concepts. In particular, we propose a saliency-based fully-convolutional neural network performing multi-scale reasoning on visual cues followed by a fully-connected conditional random field (CRF) for localizing tables and charts in digital/digitized documents. Performance analysis carried out on an extended version of ICDAR 2013 (with annotated charts as well as tables) shows that our approach yields promising results, outperforming existing models.
Abstract-The growth of data produced by the medical and clinical community requires the introduction of advanced techniques and resources in terms of computational and storage capabilities. The different problems and needs that afflict the information technologies for health support conduce to adoption of new infrastructure and application based on cloud computing. The different problems concerning to one side to the managerial, administrative and management aspects, to the other side concern who works as a physician or researcher, that needs the infrastructure to process, store, manage patient data, analysis, diagnosis, and so on. Today, cloud computing represents an important alternative to ensure high performance data processing and easy management of the complex tools in different area and in health. Cloud computing can solve many of these problems providing several advantages in terms of resource management and computational capabilities. In this paper a survey concerning the current models of health that are switching to solutions based on cloud computing, is proposed. Different applications and services are explored and concluded that the use of cloud computing and in particular of hybrid cloud solution can represent a significant opportunity to increase the development of the health sector in all its aspects.
This paper proposes CulTO, a software tool relying on a computational ontology for Cultural Heritage domain modelling, with a specific focus on religious historical buildings, for supporting cultural heritage experts in their investigations. It is specifically thought to support annotation, automatic indexing, classification and curation of photographic data and text documents of historical buildings. CULTO also serves as a useful tool for Historical Building Information Modeling (H-BIM) by enabling semantic 3D data modeling and further enrichment with non-geometrical information of historical buildings through the inclusion of new concepts about historical documents, images, decay or deformation evidence as well as decorative elements into BIM platforms. CulTO is the result of a joint research effort between the Laboratory of Surveying and Architectural Photogrammetry “Luigi Andreozzi” and the PeRCeiVe Lab (Pattern Recognition and Computer Vision Lab) of the University of Catania,
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