David Lloret, & Joan Serrat. (1999). System for calibration of a stereotatic frame..
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Daniel Ponsa, & Jordi Vitria. (1999). Mobile monitoring system using an agent-oriented approach.
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Xose M. Pardo, Petia Radeva, & Juan J. Villanueva. (1999). Self-Training Statistic Snake for Image Segmentation and Tracking..
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J.M. Sanchez, X. Binefa, Jordi Vitria, & Petia Radeva. (1999). Local Analysis for Scene Break Detection Applied to TV Commercials Recognition..
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Petia Radeva, A.F. Sole, Antonio Lopez, & Joan Serrat. (1999). Detecting Nets of Linear Structures in Satellite Images..
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A. Pujol, A.F. Sole, Daniel Ponsa, Javier Varona, & Juan J. Villanueva. (1999). Satellite Image Segmentation Trough Rotational Invariant Feature Eigenvector Projection..
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Xavier Roca, Jordi Vitria, Maria Vanrell, & Juan J. Villanueva. (1999). Gaze control in a binocular robot systems.
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Robert Benavente, M.C. Olive, Maria Vanrell, & Ramon Baldrich. (1999). Colour Perception: A Simple Method for Colour Naming..
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David Guillamet, & Jordi Vitria. (1999). Skin segmentation using non linear principal component analysis..
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J.M. Sanchez, & X. Binefa. (1999). Automatic digital TV commercial recognition..
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David Lloret, & Derek L.G. Hill. (1999). System for live fusion of 2-D ultrasound scans to pre-interventional MR volumes of a patient..
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Josep Llados, & Enric Marti. (1999). A graph-edit algorithm for hand-drawn graphical document recognition and their automatic introduction into CAD systems..
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David Lloret, Antonio Lopez, Joan Serrat, & Juan J. Villanueva. (1999). Creaseness-based computer tomography and magnetic resonance registration: Comparison with the mutual information method..
Abstract: This paper describes a method which uses the skull as a landmark for automatic registration of computer tomography to magnetic resonance (MR) images. First, the skull is extracted from both images using a new creaseness operator. Then, the resulting creaseness images are used to build a hierarchic structure which permits a robust and fast search. We have justified experimentally the performance of several choices of our algorithm, and we have thoroughly tested its accuracy and robustness against the well-known mutual information method for five different pairs of images. We have found both comparable, and for certain MR images the proposed method achieves better performance.
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Antonio Lopez, Felipe Lumbreras, Joan Serrat, & Juan J. Villanueva. (1999). Evaluation of Methods for Ridge and Valley Detection.
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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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