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Bart M. Ter Haar Romeny, W. Niessen, J. Weickert, P. Van Roermund, W. Van Enk, Antonio Lopez, et al. (1996). Orientation detection of trabecular bone.
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Javier Jimenez, Antonio Lopez, & Joan Serrat. (2007). Un enfoque ABP aplicado a Ingenieria del Software.
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Carme Julia, Joan Serrat, Antonio Lopez, Felipe Lumbreras, & Daniel Ponsa. (2006). Motion segmentation through factorization. Application to night driving assistance.
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Felipe Lumbreras, Ramon Baldrich, Maria Vanrell, Joan Serrat, & Juan J. Villanueva. (1999). Multiresolution colour texture representations for tile classification.
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Antonio Lopez, Cristina Cañero, Joan Serrat, J. Saludes, Felipe Lumbreras, & T. Graf. (2005). Detection of lane markings based on ridgeness and RANSAC.
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Antonio Lopez, J. Hilgenstock, A. Busse, Ramon Baldrich, Felipe Lumbreras, & Joan Serrat. (2008). Temporal Coherence Analysis for Intelligent Headlight Control.
Keywords: Intelligent Headlights
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Josep Llados, Felipe Lumbreras, V. Chapaprieta, & J. Queralt. (2001). ICAR: Identity Card Automatic Reader..
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Antonio Lopez, Felipe Lumbreras, A. Martinez, Joan Serrat, Xavier Roca, Javier Varona, et al. (1997). Aplicaciones de la vision por computador a la industria..
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Antonio Lopez, David Lloret, Joan Serrat, & Juan J. Villanueva. (2000). Multilocal Creaseness Based on the Level-Set Extrinsic Curvarture..
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David Lloret, Antonio Lopez, & Joan Serrat. (1997). Rigid Registration of CT and MR volumes based on Rothes creases.
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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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Antonio Lopez, Felipe Lumbreras, & Joan Serrat. (1998). Creaseness form level set extrinsec curvature..
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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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David Lloret, & Joan Serrat. (1999). System for calibration of a stereotatic frame..
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