996 resultados para Whitehead, Oliver
Resumo:
Given a set of images of scenes containing different object categories (e.g. grass, roads) our objective is to discover these objects in each image, and to use this object occurrences to perform a scene classification (e.g. beach scene, mountain scene). We achieve this by using a supervised learning algorithm able to learn with few images to facilitate the user task. We use a probabilistic model to recognise the objects and further we classify the scene based on their object occurrences. Experimental results are shown and evaluated to prove the validity of our proposal. Object recognition performance is compared to the approaches of He et al. (2004) and Marti et al. (2001) using their own datasets. Furthermore an unsupervised method is implemented in order to evaluate the advantages and disadvantages of our supervised classification approach versus an unsupervised one
Resumo:
Resumen tomado de la publicaci??n
Resumo:
It has been shown that the accuracy of mammographic abnormality detection methods is strongly dependent on the breast tissue characteristics, where a dense breast drastically reduces detection sensitivity. In addition, breast tissue density is widely accepted to be an important risk indicator for the development of breast cancer. Here, we describe the development of an automatic breast tissue classification methodology, which can be summarized in a number of distinct steps: 1) the segmentation of the breast area into fatty versus dense mammographic tissue; 2) the extraction of morphological and texture features from the segmented breast areas; and 3) the use of a Bayesian combination of a number of classifiers. The evaluation, based on a large number of cases from two different mammographic data sets, shows a strong correlation ( and 0.67 for the two data sets) between automatic and expert-based Breast Imaging Reporting and Data System mammographic density assessment
Resumo:
A recent trend in digital mammography is computer-aided diagnosis systems, which are computerised tools designed to assist radiologists. Most of these systems are used for the automatic detection of abnormalities. However, recent studies have shown that their sensitivity is significantly decreased as the density of the breast increases. This dependence is method specific. In this paper we propose a new approach to the classification of mammographic images according to their breast parenchymal density. Our classification uses information extracted from segmentation results and is based on the underlying breast tissue texture. Classification performance was based on a large set of digitised mammograms. Evaluation involves different classifiers and uses a leave-one-out methodology. Results demonstrate the feasibility of estimating breast density using image processing and analysis techniques
Resumo:
A new approach to mammographic mass detection is presented in this paper. Although different algorithms have been proposed for such a task, most of them are application dependent. In contrast, our approach makes use of a kindred topic in computer vision adapted to our particular problem. In this sense, we translate the eigenfaces approach for face detection/classification problems to a mass detection. Two different databases were used to show the robustness of the approach. The first one consisted on a set of 160 regions of interest (RoIs) extracted from the MIAS database, being 40 of them with confirmed masses and the rest normal tissue. The second set of RoIs was extracted from the DDSM database, and contained 196 RoIs containing masses and 392 with normal, but suspicious regions. Initial results demonstrate the feasibility of using such approach with performances comparable to other algorithms, with the advantage of being a more general, simple and cost-effective approach
Resumo:
In image segmentation, clustering algorithms are very popular because they are intuitive and, some of them, easy to implement. For instance, the k-means is one of the most used in the literature, and many authors successfully compare their new proposal with the results achieved by the k-means. However, it is well known that clustering image segmentation has many problems. For instance, the number of regions of the image has to be known a priori, as well as different initial seed placement (initial clusters) could produce different segmentation results. Most of these algorithms could be slightly improved by considering the coordinates of the image as features in the clustering process (to take spatial region information into account). In this paper we propose a significant improvement of clustering algorithms for image segmentation. The method is qualitatively and quantitative evaluated over a set of synthetic and real images, and compared with classical clustering approaches. Results demonstrate the validity of this new approach
Resumo:
Las creencias que se han mantenido en nuestro entorno cultural sobre las personas con discapacidad han contribuido a construir una identidad ambigua de la discapacidad tanto desde el mundo real como desde el ficticio de la literatura. En la literatura, el modo en que tradicionalmente se ha abordado la discapacidad, la caracterización de los personajes con alguna discapacidad, ha reforzado los prejuicios, provocando hacia estos personajes, en los lectores, sentimientos de rechazo o burla, o bien, de sobreprotección y de piedad o un rancio sentimentalismo. Se reflexiona sobre la necesidad de un cambio a la hora de abordar la discapacidad, una nueva sensibilidad que trate y la represente teniendo en cuenta los contextos culturales en los que vive la persona discapacitada. Se cita la obra del neurólogo Oliver Sacks como ejemplo de este nuevo enfoque que tendría que incorporarse a la literatura sobre la discapacidad; una visión que nos permita reconocer que con las personas discapacitadas son más las semejanzas que las diferencias lo que compartimos como seres humanos .
Resumo:
Las Termas son uno de los aspectos que más caracterizan la civilización romana. El baño en termas podía ser una ocupación fija de un romano durante todo el dia, donde encontraban lo necesario para el culto al cuerpo y el espíritu. Se hace una aproximación pedagógica a esta civilización.
Resumo:
La última página del artículo contiene referencias bibliográficas
Resumo:
Resumen tomado de la publicación
Resumo:
Innovacions.com es una base de datos disponible a través de la web que facilita la difusión y el intercambio de buenas prácticas educativas en las aulas con el objetivo de que este hecho ayude a la mejora profesional de los educadores. En la presente comunicación, además de ubicar el proyecto en los fundamentos teóricos que lo sustentan, se describe el proceso de creación de la base de datos y de su página web, se explica su funcionamiento y se plantean algunas conclusiones y propuestas de futuro.
Resumo:
El simposio internacional de documentación educativa, es una iniciativa de la red de bases de datos de información educativa redined y cuenta con la colaboración de varias instituciones Españolas y otras internacionales. El sidoc, surge con la finalidad de establecer relaciones de cooperación cultural y científica entre distintas iniciativas y proyectos de documentación y redes documentales educativas iberoamericanas y europeas, así como generar nuevas iniciativas coordinadas que faciliten la expansión de la red de documentación educativa redined, principalmente en países de America del Sud y de Centroamérica.
Resumo:
Resumen tomado de la publicación. Incluye tablas de datos
Resumo:
Resumen tomado de la publicaci??n. Incluye tablas de datos
Resumo:
Resumen tomado del autor. Incluye gráficos y tablas de datos