14 resultados para Hickok, Wild Bill--1837-1876.

em Acceda, el repositorio institucional de la Universidad de Las Palmas de Gran Canaria. España


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[EN] Octopus vulgaris is a suitable candidate to diversify marine aquaculture (Iglesias et al., 2000; Vaz Pires et al. 2004). Actually, wild sub-adults are on-growing in floating cages showing promising results (Chapela et al., 2006; Rodríguez et al., 2006). Even though octopus industrial development is still limited, mainly associated to the dependence of wild catch individuals for ongrowing (Iglesias et al., 2007) and a lack of an appropriate formulated diet (García García and Cerezo, 2006). In addition, essential macronutrient requirements for this species are still not well known. Used of discarded bogue as single food for Octopus on-growth results in similar growth than co-fed diets with the crab (Portunus pelagic). FA content of Muscle and DG showed important ARA content, suggesting the important functions of this FA in this specie.

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[ES] El camarón de charco (Palaemon elegans) soporta altos rangos de temperatura, pero a medida que ésta baja (hasta 5 ºC) comienza a reducir sus movimientos hasta quedar completamente inmóvil, casi en letargo. A este cambio de comportamiento va unido un cambio en la coloración del cuerpo del animal, que pasa de ser completamente transparente con tonos vivos azules y amarillos a ser translúcido, con azules y amarillos mucho más pálidos

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[ES] Se estudia el comportamiento de apoyo social entre individuos de dos especies de peces territoriales (Parablennius parvicornis y Mauligobius maderensis) ante una situación de estrés. Se observa que a medida que la densidad de individuos aumentaba el estrés respiratorio en los individuos disminuía.

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[EN]In this paper, we focus on gender recognition in challenging large scale scenarios. Firstly, we review the literature results achieved for the problem in large datasets, and select the currently hardest dataset: The Images of Groups. Secondly, we study the extraction of features from the face and its local context to improve the recognition accuracy. Diff erent descriptors, resolutions and classfii ers are studied, overcoming previous literature results, reaching an accuracy of 89.8%.

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In this paper, we present the Kinship Verification in the Wild Competition: the first kinship verification competition which is held in conjunction with the International Joint Conference on Biometrics 2014, Clearwater, Florida, USA. The key goal of this competition is to compare the performance of different methods on a new-collected dataset with the same evaluation protocol and develop the first standardized benchmark for kinship verification in the wild.

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[ES]The aim of the Kinship Verification in the Wild Evaluation (held in conjunction with the 2015 IEEE International Conference on Automatic Face and Gesture Recognition, Ljubljana, Slovenia) was to evaluate different kinship verification algorithms. For this task, two datasets were made available and three possible experimental protocols (unsupervised, image-restricted, and image-unrestricted) were designed. Five institutions submitted their results to the evaluation: (i) Politecnico di Torino, Italy; (ii) LIRIS-University of Lyon, France; (iii) Universidad de Las Palmas de Gran Canaria, Spain; (iv) Nanjing University of Aeronautics and Astronautics, China; and (v) Bar Ilan University, Israel. Most of the participants tackled the image-restricted challenge and experimental results demonstrated better kinship verification performance than the baseline methods provided by the organizers.

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[EN]Gender information may serve to automatically modulate interaction to the user needs, among other applications. Within the Computer Vision community, gender classification (GC) has mainly been accomplished with the facial pattern. Periocular biometrics has recently attracted researchers attention with successful results in the context of identity recognition. But, there is a lack of experimental evaluation of the periocular pattern for GC in the wild. The aim of this paper is to study the performance of this specific facial area in the currently most challenging large dataset for the problem.