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David Guillamet, & Jordi Vitria. (2002). Determining a Suitable Metric when using Non-negative Matrix Factorization..
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David Guillamet, & Jordi Vitria. (2002). Classifying Faces with Non-negative Matrix Factorization..
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David Guillamet, & Jordi Vitria. (2003). An Experimental Evaluation of K-nn for Linear Transforms of Positive Data. In In Pattern Recognition and Image Analysis, Lecture Notes in Computer Science. 2652:317–325.
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David Guillamet, & Jordi Vitria. (2003). Evaluation of distance metrics for recognition based on non-negative matrix factorization. PRL - Pattern Recognition Letters, 24(9-10), 1599 –1605.
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David Guillamet, Jordi Vitria, & B. Shiele. (2003). Introducing a weighted non-negative matrix factorization for image classification. PRL - Pattern Recognition Letters, 24(14), 2447–2454.
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David Guillamet, M. Bressan, & Jordi Vitria. (2001). Weighted Non-negative Matrix Factorization for Local Representations..
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David Lloret. (2002). Medical Image Registration Based on a Creaseress Measure. (Joan Serrat, Ed.). Ph.D. thesis, , .
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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, Antonio Lopez, & Joan Serrat. (1997). Rigid Registration of CT and MR volumes based on Rothes creases.
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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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David Lloret, C. Mariño, Joan Serrat, Antonio Lopez, & Juan J. Villanueva. (2001). Landmark-based registration of full SLO video sequences..
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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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David Lloret, & Joan Serrat. (1999). System for calibration of a stereotatic frame..
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David Lloret, Joan Serrat, Antonio Lopez, A. Soler, & Juan J. Villanueva. (2000). Retinal image registration using creases as anatomical landmarks..
Abstract: Retinal images are routinely used in ophthalmology to study the optical nerve head and the retina. To assess objectively the evolution of an illness, images taken at different times must be registered. Most methods so far have been designed specifically for a single image modality, like temporal series or stereo pairs of angiographies, fluorescein angiographies or scanning laser ophthalmoscope (SLO) images, which makes them prone to fail when conditions vary. In contrast, the method we propose has shown to be accurate and reliable on all the former modalities. It has been adapted from the 3D registration of CT and MR image to 2D. Relevant features (also known as landmarks) are extracted by means of a robust creaseness operator, and resulting images are iteratively transformed until a maximum in their correlation is achieved. Our method has succeeded in more than 100 pairs tried so far, in all cases including also the scaling as a parameter to be optimized
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