Abstract
In this paper, a digital image correlation (DIC) method coupling cross-correlation with spatio-temporal differential techniques was proposed for assessing discontinuous displacement fields. The accuracy and robustness of the algorithm was assessed on a set of numerical tests by processing computer generated speckled-pattern images. Fracture mechanical tests in mode I were considered, in which both in-plane and out-of-plane rigid-body movements were taken into account. The ability for recovering the analytical asymptotic displacement field in mode I was analysed, and stress intensity factor, crack opening displacement and crack tip location were used as quantitative parameters for validation purposes. Throughout these tests, the results obtained with the proposed method were systematically compared to the ones from Aramis DIC-2D commercial code. Globally, the results computed from both methods are in good agreement with reference values. However, due to the high spatial resolution (point-wise characteristic), a better matching of the displacements in the neighbour of discontinuities could be obtained by the proposed method.

Abstract
Assistive technology enables people to achieve independence in the accomplishment of their daily tasks and enhance their quality of life. Visual information is the basis for most navigational tasks, so visually impaired individuals are at disadvantage due to the lack of information or given insufficient information about their surrounding environment. With the recent advances in inclusive technology it is possible to extend the support given to people with visual disabilities during their mobility. In this context we propose a new algorithm to recognize landmarks suitably placed on sidewalks. The proposed algorithm uses a combination of Peano-Hilbert Space Filling Curves for dimension reduction of image data and Ensemble Empirical Mode Decomposition (EEMD) to pre-process the image, resulting on a fast and efficient recognition method and revealing a promising solution.

Abstract

Abstract
Wireless capsule endoscopy (WCE) is a revolutionary, patient-friendly imaging technique that enables non-invasive visual inspection of the patient's digestive tract, especially small intestine. However, the time-consuming task of reviewing the endoscopic data a burden for the physicians. This limitation was the propose a novel strategy for automatic discrimination of WCE images related to ulcer, the most common finding of digestive tract Towards this direction, WCE data are processed with Bidimensional Ensemble Empirical Mode Decomposition to reveal their inherent structural components, and also to reconstruct a new refined image. Then, texture information is extracted by analyzing the intrinsic second/higher-order correlation of the original image and by calculating the lacunarity index of the refined image. Experimental results demonstrated promising classification accuracy (97%) exhibiting high potential towards a complete computer-aided diagnosis system.

Abstract
High-resolution computed tomography (HRCT) became an essential tool in detection, characterization and follow-up of lung diseases. In this paper we focus on lung emphysema, a long-term and progressive disease characterized by the destruction of lung tissue. The lung patterns are represented by different features vectors, extracted from statistical texture analysis methods (spatial gray level dependence, gray level run length method and gray level difference method). Support vector machine (SVM) was trained to discriminate regions of healthy lung tissue from emphysematous regions. The SVM model optimization was performed in the training dataset through a cross validation methodology, along a grid search. Three usual kernel functions were tested in each of the features sets. This study highlights the importance of the kernel choice and parameters tuning to obtain models that allow high level performance of the SVM classifier.

Abstract
In this article we will discuss that the Intelligent or Smart Meeting Rooms (SMR) spaces will have to understand what is going in the meetings and even what participants are discussing. In this way we believe it is possible to supply intelligent feedback. In order to fill this gap we came to propose an extensible Meeting Task Ontology (MTO) that is able to represent in detail what knowledge is present in Meetings for group decision-making. We also propose an extension to this ontology, the IGMTO ontology, that specifically focuses on the knowledge present in the Idea Generation (IG) Process. We believe that the Idea Generation process modeled with these two ontologies, who provide a link between the ideas and problems of a meeting to a domain ontology, it will be possible to give awareness of meeting contents to a SMR space.