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Providing Geographical Information Systems with mechanisms for processing geo‐data based on their semantics may help to solve problems like heterogeneity. This is because GIS could process geo‐data focusing on their meaning and not on their syntax and/or structure. An important aspect for achieving these objectives is the establishment of an automatic means of correspondence between geo‐data and their conceptualization in Higher Levels Ontologies (HLO). In this article, a new type of Ontology is proposed (Data‐Representation Ontology (DRO)). This Ontology describes the semantic embedded in geo‐data, which cannot be represented in current types of Ontologies. Across this Ontology, heterogeneous geographical data can be integrated in the semantic space contributing positively to the development of solutions for the problems of interoperability between heterogeneous systems. Likewise, we propose a new method for the automatic generation of the DRO and its interrelationships with HLO, based on pattern classification techniques. The experiments show that once the DRO is generated, the classifier can classify all data correctly. Thus, these data are semantically enriched. Moreover, this article shows how the topological relationships can enrich the semantics in the generated Ontology and increase the effectiveness of spatial analysis.

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The prevalence of visual impairment around the world is rapidly increasing, causing large numbers of people to wear glasses. Glasses are generally considered an important noise source in iris recognition; under objective metrics, they have recently been shown to deteriorate the sample quality of nearinfrared (NIR) ocular images (consequently impairing the segmentation accuracy and biometric performance). Automatically and robustly detecting glasses in ocular images is therefore one of the prerequisites for the acquisition of high quality iris samples. While this issue has recently been addressed for NIR ocular images, it remains an open issue in the visible wavelength (VW) spectrum. As the popularity of VW iris recognition increases (due to e.g. deployment of iris recognition in consumer grade mobile devices and general improvements in VW recognition algorithms), it becomes a matter of interest to quantitatively evaluate the impact of glasses on such systems, as well as develop methods for automatic detection of glasses in VW ocular images.In this paper, the impact of glasses on VW iris segmentation performance is investigated using the UBIRISv2 and MobBIO iris databases. It is shown that the presence of glasses significantly degrades the accuracy of iris segmentation. In addition, a state-ofthe-art iris segmentation method which can perform a semantic segmentation of ocular images (including the segmentation of glasses) is employed for the purpose of glasses detection. On the used databases, correct classification rates (CCRs) of 98.57% and 83.62% are obtained, respectively.

<p>El objetivo del trabajo fue identificar el número y situación espacial de los fascículos que conforman los nervios tibial y peroneo común en el perro, permitiendo de esta manera su clasificación acorde a modelos de agrupación fascicular. Se trabajó con 20 miembros posteriores de caninos. Los nervios tibial y peroneo común fueron disecados. A partir de cortes transversales de dichos nervios se realizaron preparados histológicos coloreados con hematoxilina y eosina, determinándose por microscopía óptica el número y la distribución de los fascículos, agrupándose las fibras en tres categorías acorde a su diámetro (hasta 4, de 4 a 8 y de 8 a 12 mm). Para el nervio tibial fueron identificados trece fascículos, el de mayor diámetro ubicado caudal y medialmente del nervio, resultando que el 40% de sus fibras correspondió a la primera categoría, 52% para la segunda y 8% para la tercera. Para el nervio peroneo común se identificaron tres fascículos, el de mayor diámetro ubicado caudalmente, no diferenciando su posición lateral o medial por tratarse de un nervio aplanado, con una distribución lineal de los fascículos; del análisis de las fibras surgió que el 50% perteneció a la primera categoría, 35% a la segunda y 15% a la tercera.</p>