A new approach to segmentation based on fusing circumscribed contours, region growing and clustering

One of the major problems in machine vision is the segmentation of images of natural scenes. This paper presents a new proposal for the image segmentation problem which has been based on the integration of edge and region information. The main contours of the scene are detected and used to guide the posterior region growing process. The algorithm places a number of seeds at both sides of a contour allowing stating a set of concurrent growing processes. A previous analysis of the seeds permits to adjust the homogeneity criterion to the regions's characteristics. A new homogeneity criterion based on clustering analysis and convex hull construction is proposed

Active regions for colour texture segmentation integrating region and boundary information

In this paper a colour texture segmentation method, which unifies region and boundary information, is proposed. The algorithm uses a coarse detection of the perceptual (colour and texture) edges of the image to adequately place and initialise a set of active regions. Colour texture of regions is modelled by the conjunction of non-parametric techniques of kernel density estimation (which allow to estimate the colour behaviour) and classical co-occurrence matrix based texture features. Therefore, region information is defined and accurate boundary information can be extracted to guide the segmentation process. Regions concurrently compete for the image pixels in order to segment the whole image taking both information sources into account. Furthermore, experimental results are shown which prove the performance of the proposed method

Unsupervised active regions for multiresolution image segmentation

An unsupervised approach to image segmentation which fuses region and boundary information is presented. The proposed approach takes advantage of the combined use of 3 different strategies: the guidance of seed placement, the control of decision criterion, and the boundary refinement. The new algorithm uses the boundary information to initialize a set of active regions which compete for the pixels in order to segment the whole image. The method is implemented on a multiresolution representation which ensures noise robustness as well as computation efficiency. The accuracy of the segmentation results has been proven through an objective comparative evaluation of the method

A Novel Breast Tissue Density Classification Methodology

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

A new approach to the classification of mammographic masses and normal breast tissue

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

Strategies of psychological terrorism perpetrated by ETA's network : Delimitation and classification

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Cuerpos mejorables desde la educaci??n, cuerpos deteriorados por la fatalidad : an??lisis hist??rico hasta la modernidad)

Resumen del autor. Res??menes en castellano e ingl??s

Variables diferenciales de personalidad en los subtipos de TDAH en la edad adulta

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New media for the promotion of self-regulated learning in higher education

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An??lisis psicom??trico del Inventario de Apoyo Familiar para Trabajadores

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Sesgos cognitivos en el reconocimiento de expresiones emocionales de voz sint??tica en la alexitimia

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La elecci??n de fuentes de apoyo social entre inmigrantes

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El municipi de Barcelona i les parròquies del seu entorn al segle XIV

L'articulació política del pla de Barcelona en l'època medieval es va basar en l'existència de dos territoris: d'una banda, una gran àrea que va coincidir amb la plana entre les desembocadures del Llobregat i del Besòs, on les autoritats locals exerciren alguna funció de control judicial. D'altra banda, un territori molt més petit, anomenat Hort i Vinyet (Parròquia de Sants i Sarrià Sant Martí Provençals), on la jurisdicció civil i penal del Consell de Cent era absoluta. L'objectiu d'aquest treball és mostrar, en primer lloc, que el territori anomenat Hort i Vinyet no existia i, d'altra banda, que l'extens territori no era subjecte a la jurisdicció municipal

Evaluaci??n de un programa de desarrollo de la creatividad

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Efectos de la duraci??n del desempleo entre los desempleados

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