X. Binefa, Xavier Roca, & Jordi Vitria. (1997). A Contrast Approach to Depth from Focus..
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Craig Von Land, Ricardo Toledo, & Juan J. Villanueva. (1997). TeleRegions: Application of Telematics in Cardiac Care..
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V. Valev, B. Sankur, & Petia Radeva. (1997). Generalized Non-Reducible Descriptors..
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W. Niessen, Antonio Lopez, W. Van Enk, P. Van Roermund, Bart M. Ter Haar Romeny, & M. Viergever. (1997). Multiscale Trabecular Bone Orientation Analysis..
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W. Niessen, Antonio Lopez, W. Van Enk, P. Van Roermund, Bart M. Ter Haar Romeny, & M. Viergever. (1997). In Vivo Analysis of Trabecular Bone Architecture..
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Craig Von Land, V. Lashin, A. Oriol, & Juan J. Villanueva. (1997). Object-oriented Design of the DICOM Standard and its Application to Cardiovascular Imaging..
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S. Gonzalez, & A. Martinez. (1997). Fundamentos de la Vision aplicada a la Robotica Autonoma..
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Javier Varona, & Juan J. Villanueva. (1997). NeuroFilters: Neural Networks for image Processing..
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J. Weickert, Bart M. Ter Haar Romeny, Antonio Lopez, & W. Van Enk. (1997). Orientation Analysis by Coherence-Enhancing Diffusion..
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J.R. Serra, & J.B. Subirana. (1997). Adaptive non-cartesian networks for vision..
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Antonio Lopez. (1997). Ridge/Valley-like structures: Creases, separatrices and drainage patterns.
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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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