Xavier Roca, X. Binefa, & Jordi Vitria. (1998). A New Autofocus Algorithm for Cytological Tissue in a Microscopy Environment..
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X. Binefa, Petia Radeva, J.A. Cortijo, & J. Garcia. (1998). Contour detection and color influence in defocused environtments..
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Petia Radeva, Ricardo Toledo, Craig Von Land, & Juan J. Villanueva. (1998). 3D Vessel Reconstruction from Biplane Angiograms using Snakes..
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Petia Radeva, Ricardo Toledo, Craig Von Land, & Juan J. Villanueva. (1998). 3D Dynamic Model of the Coronary Tree..
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Petia Radeva, J. Guerrero, & C. Molina. (1998). A Physics-Based Kohonen Ring..
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Petia Radeva, A.F. Sole, Antonio Lopez, & Joan Serrat. (1998). Detecting Nets of Linear Structures in Satellite Images..
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Josep Llados, Gemma Sanchez, & Enric Marti. (1998). A string based method to recognize symbols and structural textures in architectural plans. In Graphics Recognition Algorithms and Systems Second International Workshop, GREC' 97 Nancy, France, August 22–23, 1997 Selected Papers (Vol. 1389, pp. 91–103). LNCS. Springer Link.
Abstract: This paper deals with the recognition of symbols and structural textures in architectural plans using string matching techniques. A plan is represented by an attributed graph whose nodes represent characteristic points and whose edges represent segments. Symbols and textures can be seen as a set of regions, i.e. closed loops in the graph, with a particular arrangement. The search for a symbol involves a graph matching between the regions of a model graph and the regions of the graph representing the document. Discriminating a texture means a clustering of neighbouring regions of this graph. Both procedures involve a similarity measure between graph regions. A string codification is used to represent the sequence of outlining edges of a region. Thus, the similarity between two regions is defined in terms of the string edit distance between their boundary strings. The use of string matching allows the recognition method to work also under presence of distortion.
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Enric Marti, Jordi Vitria, & Alberto Sanfeliu. (1998). Reconocimiento de Formas y Análisis de Imágenes. AERFAI.
Abstract: Los sistemas actuales de reconocimiento automático del lenguaje oral se basan en dos etapas básicas de procesado: la parametrización, que extrae la evolución temporal de los parámetros que caracterizan la voz, y el reconocimiento propiamente dicho, que identifica la cadena de palabras de la elocución recibida con ayuda de los modelos que representan el conocimiento adquirido en la etapa de aprendizaje. Tomando como línea divisoria la palabra, dichos modelos son de tipo acústicofonético o gramatical. Los primeros caracterizan las palabras incluidas en el vocabulario de la aplicación o tarea a la que está orientado el sistema de reconocimiento, usando a menudo para ello modelos de unidades de habla de extensión inferior a la palabra, es decir, de unidades subléxicas. Por otro lado, la gramática incluye el conocimiento acerca de las combinaciones permitidas de palabras para formar las frases o su probabilidad. Queda fuera del esquema la denominada comprensión del habla, que utiliza adicionalmente el conocimiento semántico y pragmático para captar el significado de la elocución de entrada al sistema a partir de la cadena (o cadenas alternativas) de palabras que suministra el reconocedor.
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Dimosthenis Karatzas, & Ch. Lioutas. (1998). Software Package Development for Electron Diffraction Image Analysis. In Proceedings of the XIV Solid State Physics National Conference.
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David Lloret, Antonio Lopez, & Joan Serrat. (1998). 3-D image Processing and Modeling, workshop on non-linear model-based image analysis..
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David Lloret, Antonio Lopez, & Joan Serrat. (1998). Precise registration of CT and MR volumes based on a new creaseness measure.
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C. Molina, G.P. Prause, Petia Radeva, & M. Sonka. (1998). Catheter Path Reconstruction from Biplane Angiography using 3D Snakes..
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Antonio Lopez, & Joan Serrat. (1998). Ridges and Valleys in Image Analysis.
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Antonio Lopez, Felipe Lumbreras, & Joan Serrat. (1998). Creaseness form level set extrinsec curvature..
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Antonio Lopez, David Lloret, & Joan Serrat. (1998). Creaseness measures for CT and MR image registration..
Abstract: Creases are a type of ridge/valley structures that can be characterized by local conditions. Therefore, creaseness refers to local ridgeness and valleyness. The curvature K of the level curves and the mean curvature kM of the level surfaces are good measures of creaseness for 2-d and 3-d images, respectively. However, the way they are computed gives rise to discontinuities, reducing their usefulness in many applications. We propose a new creaseness measure, based on these curvatures, that avoids the discontinuities. We demonstrate its usefulness in the registration of CT and MR brain volumes, from the same patient, by searching the maximum in the correlation of their creaseness responses (ridgeness from the CT and valleyness from the MR). Due to the high dimensionality of the space of transforms, the search is performed by a hierarchical approach combined with an optimization method at each level of the hierarchy
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