963 resultados para Martí


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Resumen tomado de la publicaci??n. La fecha, 2008, consta en la cub. de la revista, en la cabecera de los art??culos consta, por error, 2007

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Resumen tomado de la publicaci??n

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La Ley Orgánica de Educación (LOE) aporta un claro marco de referencia en relación con la inserción de las TIC, en el proceso de enseñanza-aprendizaje de las distintas etapas educativas. El tratamiento de la información y competencia digital es una de las ocho competencias básicas que se establecen en los Reales Decretos de enseñanzas mínimas de la Educación Primaria y Secundaria Obligatoria. Como consecuencia es necesario que el profesorado esté preparado para afrontar este reto. Aunque la LOE hace referencia a la necesidad de uso de recursos y formación permanente en TIC para el profesorado el tratamiento parece insuficiente. En este artículo se analizan los estándares en competencias TIC para docentes (ECDTIC) de la UNESCO y de la organización norteamericana International Society for technology in Education (ISTE) y se apela a las administraciones educativas y las universidades para que tomen en consideración la importancia de revisar en profundidad los currículos de la formación inicial y permanente del profesorado.

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Image segmentation of natural scenes constitutes a major problem in machine vision. This paper presents a new proposal for the image segmentation problem which has been based on the integration of edge and region information. This approach begins by detecting the main contours of the scene which are later used to guide a concurrent set of growing processes. A previous analysis of the seed pixels permits adjustment of the homogeneity criterion to the region's characteristics during the growing process. Since the high variability of regions representing outdoor scenes makes the classical homogeneity criteria useless, a new homogeneity criterion based on clustering analysis and convex hull construction is proposed. Experimental results have proven the reliability of the proposed approach

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Resumen tomado de la publicaci??n

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A new method for the automated selection of colour features is described. The algorithm consists of two stages of processing. In the first, a complete set of colour features is calculated for every object of interest in an image. In the second stage, each object is mapped into several n-dimensional feature spaces in order to select the feature set with the smallest variables able to discriminate the remaining objects. The evaluation of the discrimination power for each concrete subset of features is performed by means of decision trees composed of linear discrimination functions. This method can provide valuable help in outdoor scene analysis where no colour space has been demonstrated as being the most suitable. Experiment results recognizing objects in outdoor scenes are reported

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Resumen en ingl??s y castellano

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Resumen del autor. Res??menes en espa??ol e ingl??s

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Changes in the angle of illumination incident upon a 3D surface texture can significantly alter its appearance, implying variations in the image texture. These texture variations produce displacements of class members in the feature space, increasing the failure rates of texture classifiers. To avoid this problem, a model-based texture recognition system which classifies textures seen from different distances and under different illumination directions is presented in this paper. The system works on the basis of a surface model obtained by means of 4-source colour photometric stereo, used to generate 2D image textures under different illumination directions. The recognition system combines coocurrence matrices for feature extraction with a Nearest Neighbour classifier. Moreover, the recognition allows one to guess the approximate direction of the illumination used to capture the test image