Evaluation of different chrominance models in the detection and reconstruction of faces and hands using the growing neural gas network

Physical traits such as the shape of the hand and face can be used for human recognition and identifcation in video surveillance systems and in biometric authentication smart card systems, as well as in personal health care. However, the accuracy of such systems sufers from illumination changes, unpredictability, and variability in appearance (e.g. occluded faces or hands, cluttered backgrounds, etc.). This work evaluates diferent statistical and chrominance models in diferent environments with increasingly cluttered backgrounds where changes in lighting are common and with no occlusions applied, in order to get a reliable neural network reconstruction of faces and hands, without taking into account the structural and temporal kinematics of the hands. First a statistical model is used for skin colour segmentation to roughly locate hands and faces. Then a neural network is used to reconstruct in 3D the hands and faces. For the fltering and the reconstruction we have used the growing neural gas algorithm which can preserve the topology of an object without restarting the learning process. Experiments conducted on our own database but also on four benchmark databases (Stirling’s, Alicante, Essex, and Stegmann’s) and on deaf individuals from normal 2D videos are freely available on the BSL signbank dataset. Results demonstrate the validity of our system to solve problems of face and hand segmentation and reconstruction under diferent environmental conditions

Fast 2D/3D object representation with growing neural gas

This work presents the design of a real-time system to model visual objects with the use of self-organising networks. The architecture of the system addresses multiple computer vision tasks such as image segmentation, optimal parameter estimation and object representation. We first develop a framework for building non-rigid shapes using the growth mechanism of the self-organising maps, and then we define an optimal number of nodes without overfitting or underfitting the network based on the knowledge obtained from information-theoretic considerations. We present experimental results for hands and faces, and we quantitatively evaluate the matching capabilities of the proposed method with the topographic product. The proposed method is easily extensible to 3D objects, as it offers similar features for efficient mesh reconstruction

Formalizing enrichment mechanisms for bibliographic ontologies in the Semantic Web

This paper presents an analysis of current limitations to the reuse of bibliographic data in the Semantic Web and a research proposal towards solutions to overcome them. The limitations identified derive from the insufficient convergence between existing bibliographic ontologies and the principles and techniques of linked open data (LOD); lack of a common conceptual framework for a diversity of standards often used together; reduced use of links to external vocabularies and absence of Semantic Web mechanisms to formalize relationships between vocabularies, as well as limitations of Semantic Web languages for the requirements of bibliographic data interoperability. A proposal is advanced to investigate the hypothesis of creating a reference model and specifying a superontology to overcome the misalignments found, as well as the use of SHACL (Shapes Constraint Language) to solve current limitations of RDF languages.info:eu-repo/semantics/acceptedVersio

Effectiveness and safety of first-generation protease inhibitors in clinical practice: Hepatitis C virus patients with advanced fibrosis

AIM: To evaluates the effectiveness and safety of the first generation, NS3/4A protease inhibitors (PIs) in clinical practice against chronic C virus, especially in patients with advanced fibrosis. METHODS: Prospective study and non-experimental analysis of a multicentre cohort of 38 Spanish hospitals that includes patients with chronic hepatitis C genotype 1, treatment-nai¨ve (TN) or treatment-experienced (TE), who underwent triple therapy with the first generation NS3/4A protease inhibitors, boceprevir (BOC) and telaprevir (TVR), in combination with pegylated interferon and ribavirin. The patients were treatment in routine practice settings. Data on the study population and on adverse clinical and virologic effects were compiled during the treatment period and during follow up. RESULTS: One thousand and fifty seven patients were included, 405 (38%) were treated with BOC and 652 (62%) with TVR. Of this total, 30% (n = 319) were TN and the remaining were TE: 28% (n = 298) relapsers, 12% (n = 123) partial responders (PR), 25% (n = 260) null-responders (NR) and for 5% (n = 57) with prior response unknown. The rate of sustained virologic response (SVR) by intention-to-treatment (ITT) was greater in those treated with TVR (65%) than in those treated with BOC (52%) (P < 0.0001), whereas by modified intention-to-treatment (mITT) no were found significant differences. By degree of fibrosis, 56% of patients were F4 and the highest SVR rates were recorded in the non-F4 patients, both TN and TE. In the analysis by groups, the TN patients treated with TVR by ITT showed a higher SVR (P = 0.005). However, by mITT there were no significant differences between BOC and TVR. In the multivariate analysis by mITT, the significant SVR factors were relapsers, IL28B CC and non-F4; the type of treatment (BOC or TVR) was not significant. The lowest SVR values were presented by the F4-NR patients, treated with BOC (46%) or with TVR (45%). 28% of the patients interrupted the treatment, mainly by non-viral response (51%): this outcome was more frequent in the TE than in the TN patients (57% vs 40%, P = 0.01). With respect to severe haematological disorders, neutropaenia was more likely to affect the patients treated with BOC (33% vs 20%, P = 0.0001), and thrombocytopaenia and anaemia, the F4 patients (P = 0.000, P = 0.025, respectively). CONCLUSION: In a real clinical practice setting with a high proportion of patients with advanced fibrosis, effectiveness of first-generation PIs was high except for NR patients, with similar SVR rates being achieved by BOC and TVR

3D hand pose estimation with neural networks

We propose the design of a real-time system to recognize and interprethand gestures. The acquisition devices are low cost 3D sensors. 3D hand pose will be segmented, characterized and track using growing neural gas (GNG) structure.The capacity of the system to obtain information with a high degree of freedom allows the encoding of many gestures and a very accurate motion capture. The use of hand pose models combined with motion information provide with GNG permits to deal with the problem of the hand motion representation. A natural interface applied to a virtual mirrorwriting system and to a system to estimate hand pose will be designed to demonstrate the validity of the system