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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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W.Win, B.Bao, Q.Xu, Luis Herranz, & Shuqiang Jiang. (2019). Editorial Note: Efficient Multimedia Processing Methods and Applications (Vol. 78).
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B. Moghaddam, David Guillamet, & Jordi Vitria. (2003). Local Appearance-Based Models using High-Order Statistics of Image Features.
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Misael Rosales, Petia Radeva, J. Mauri, & Oriol Pujol. (2004). Simulation Model of Intravascular Ultrasound Images.
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David Rotger, Cristina Cañero, Petia Radeva, J. Mauri, E. Fernandez, A. Tovar, et al. (2001). Advanced Visualization of 3D data of Intravascular Ultrasound Images..
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Petia Radeva. (1993). Segmentacion de Imagenes Radiograficas con Snakes. Aplicacion a la Determinacion de la Madurez Osea..
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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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Petia Radeva, A.F. Sole, Antonio Lopez, & Joan Serrat. (1999). Detecting Nets of Linear Structures in Satellite Images..
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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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Josep Llados, & Young-Bin Kwon. (2004). Graphics Recognition. Recent Advances and Perspectives.
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Anna Salvatella, Maria Vanrell, & Ramon Baldrich. (2003). Subtexture Components for Texture Description.
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X. Binefa, J.M. Sanchez, Petia Radeva, & Jordi Vitria. (2000). Linking Visual Cues and Semantic Terms Under Specific Digital Video Domains..
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Jordi Vitria, X. Binefa, & Juan J. Villanueva. (1992). Morphological Algorithms for Visual Analysis of Integrated Circuits..
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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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A. Martinez, & Jordi Vitria. (1997). Using Low-Dimensional Spaces for Face Recognition..
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