967 resultados para Lindahl-Raittila, Iris


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La historia, basada en un cuento popular irlandés, es una versión de la Cenicienta. Una niña supera la maldad de sus hermanas mayores para convertirse en la novia de un príncipe. Todos los domingos, las hermanas mayores van a la iglesia dejando a su hermana pequeña, en casa cocinando y barriendo. Un domingo por la mañana mientras sus hermanas están en la iglesia una anciana hada madrina convierte sus harapos en un vestido de equitación de seda blanca y va cabalgando hacia la iglesia en una yegua blanca.

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Programa emitido el 28 de septiembre de 1994

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Entre los materiales se incluye un DVD con un vídeo en el que se contemplan y explican las sesiones y actividades que se llevan a cabo y un CD con canciones.

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Experiencia educativa que nos relata la realizaci??n de un diccionario en las tres lenguas de los pa??ses participantes: castellano, rumano y estonio, usando el ingl??s como v??nculo entre ellas. El objetivo es el de fomentar la comunicaci??n entre las distintas culturas participantes con el nexo de la tecnolog??a. El nombre del proyecto es 'Nuevas tecnolog??as: un arco iris entre la gente'.

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Proyecto de educación ambiental cuyos objetivos son conocer la naturaleza del entorno, amar los jardines, cuidar las plantas del colegio, limpiar las zonas verdes, trabajar para conseguir un ambiente adecuado, conseguir un colegio y un pueblo limpio, acercar a los alumnos a su ambiente natural, concienciar a la Comunidad Educativa de la problemática de la degradación medioambiental y desarrollar entre todos el espíritu de cooperación en el trabajo.

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Resumen basado en el de la publicaci??n

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This is an analysis of Iris Murdoch's plays, including The Italian Girl, The Severed Head, The Black Prince, The Three Arrows and The Servants and the Snow. It also assesses Murdoch's significance for theatre in the early 1960s and 70s, as Women's Theatre was beginning to make its mark.

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Multispectral iris recognition uses information from multiple bands of the electromagnetic spectrum to better represent certain physiological characteristics of the iris texture and enhance obtained recognition accuracy. This paper addresses the questions of single versus cross spectral performance and compares score-level fusion accuracy for different feature types, combining different wavelengths to overcome limitations in less constrained recording environments. Further it is investigated whether Doddington's “goats” (users who are particularly difficult to recognize) in one spectrum also extend to other spectra. Focusing on the question of feature stability at different wavelengths, this work uses manual ground truth segmentation, avoiding bias by segmentation impact. Experiments on the public UTIRIS multispectral iris dataset using 4 feature extraction techniques reveal a significant enhancement when combining NIR + Red for 2-channel and NIR + Red + Blue for 3-channel fusion, across different feature types. Selective feature-level fusion is investigated and shown to improve overall and especially cross-spectral performance without increasing the overall length of the iris code.

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This paper investigates the potential of fusion at normalisation/segmentation level prior to feature extraction. While there are several biometric fusion methods at data/feature level, score level and rank/decision level combining raw biometric signals, scores, or ranks/decisions, this type of fusion is still in its infancy. However, the increasing demand to allow for more relaxed and less invasive recording conditions, especially for on-the-move iris recognition, suggests to further investigate fusion at this very low level. This paper focuses on the approach of multi-segmentation fusion for iris biometric systems investigating the benefit of combining the segmentation result of multiple normalisation algorithms, using four methods from two different public iris toolkits (USIT, OSIRIS) on the public CASIA and IITD iris datasets. Evaluations based on recognition accuracy and ground truth segmentation data indicate high sensitivity with regards to the type of errors made by segmentation algorithms.