9 resultados para ERS

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


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En este trabajo se presentan algunos resultados obtenidos del análisis de la variabilidad de la altura de la superficie del mar a partir de las anomalías del nivel del mar proporcionadas por los datos del altímetro a bordo del satélite ERS-2. La finalidad del estudio has sido la determinación de la variación estacional que las estructuras oceanográficas mesoescalares presentan en las proximidades del archipiélago canario durante el año 1998. En esta zona, caracterizada por la generación de remolinos ciclónicos y anticiclónicos al sur de las islas debida a la perturbación que experimenta la corriente de Canarias a su paso por los canales entre las islas, y por los filamentos de agua fría procedente del afloramiento, el altímetro se muestra como una herramienta importante en la detección y posterior análisis de estas estructuras oceanográficas. Los resultados muestran que la variabilidad espacial y temporal del nivel del mar es máxima en el segundo semestre del año, y ésta se centra, fundamentalmente, en una estrecha banda situada al sudoeste del archipiélago. ABSTRACT: Some results obtained from the analysis of the sea surface height variability using sea level anomalies given by ERS-2 altimeter data are shown in this work. The aim of the study is to work out the seasonal variations of the mesoscale oceanographic features that appear in the vicinity of the Canary Archipelago during 1998 year. This area is characterized by cyclonic and anticyclonic eddies southward of the islands, which are generated by the interference suffered by the Canary Current through the canals between the islands, and also owing to cold water filaments coming from the Upwelling. The altimeter demonstrates to be an important tool in the detection and posterior analysis of these features. The results show that the temporal and spatial variability of the sea level is associated, fundamentally, to a narrow band located to the southwest of the archipelago, and which has been clearly seen with greater intensity during the periods of summer and autumn of 1998.

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[EN]This paper presents a study on the facial feature detection performance achieved using the Viola-Jones framework. A set of classi- ers using two di erent focuses to gather the training samples is created and tested on four di erent datasets covering a wide range of possibili- ties. The results achieved should serve researchers to choose the classi er that better ts their demands.

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[EN]OpenCV includes di erent object detectors based on the Viola-Jones framework. Most of them are specialized to deal with the frontal face pattern and its inner elements: eyes, nose, and mouth. In this paper, we focus on the ear pattern detection, particularly when a head pro le or almost pro le view is present in the image. We aim at creating real-time ear detectors based on the general object detection framework provided with OpenCV. After training classi ers to detect left ears, right ears, and ears in general, the performance achieved is valid to be used to feed not only a head pose estimation system but also other applications such as those based on ear biometrics.

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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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[EN]In this work an experimental study about the capability of the LBP, HOG descriptors and color for clothing attribute classification is presented. Two different variants of the LBP descriptor are considered, the original LBP and the uniform LBP. Two classifiers, Linear SVM and Random Forest, have been included in the comparison because they have been frequently used in clothing attributes classification. The experiments are carried out with a public available dataset, the clothing attribute dataset, that has 26 attributes in total. The obtained accuracies are over 75% in most cases, reaching 80% for the necktie or sleeve length attributes.

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[EN]This paper does not propose a new technique for face representationorclassification. Insteadtheworkdescribed here investigates the evolution of an automatic system which, based on a currently common framework, and starting from an empty memory, modifies its classifiers according to experience. In the experiments we reproduce up to a certain extent the process of successive meetings. The results achieved, even when the number of different individuals is still reduced compared to off-line classifiers, are promising.

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[EN]Facial image processing is becoming widespread in human-computer applications, despite its complexity. High-level processes such as face recognition or gender determination rely on low-level routines that must e ectively detect and normalize the faces that appear in the input image. In this paper, a face detection and normalization system is described. The approach taken is based on a cascade of fast, weak classi ers that together try to determine whether a frontal face is present in the image.