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Agata Lapedriza, David Masip, & Jordi Vitria. (2005). The contribution of external features to face recognition. In Pattern Recognition and Image Analysis (IbPRIA 2005), LNCS 3523: 537–544.
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Miquel Ferrer, F. Serratosa, & A. Sanfeliu. (2005). Synthesis of median spectral graph. In Pattern Recognition and Image Analysis (IbPRIA´05), LNCS, 3523: 139 146.
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Jaume Amores, N. Sebe, & Petia Radeva. (2005). Efficient Object-Class Recognition by Boosting Contextual Information.
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Fernando Lopez, J.M. Valiente, Ramon Baldrich, & Maria Vanrell. (2005). Fast surface grading using color statistics in the CIELab space. In Pattern Recognition and Image Analysis. IbPRIA 2005 (Vol. LNCS 3523, pp. 66–673).
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A. Sanfeliu, & Juan J. Villanueva. (2005). An approach of visual motion analysis. PRL - Pattern Recognition Letters, 26(3), 355–368.
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Jaume Amores, & Petia Radeva. (2005). Registration and Retrieval of Highly Elastic Bodies using Contextual Information. PRL - Pattern Recognition Letters, 26(11), 1720–1731.
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Matthias S. Keil, & Jordi Vitria. (2005). Does the brain generate representations of smooth brightness gradients? A novel account for Mach bands, Chevreul’s illusion, and a variant of the Ehrenstein disk. Perception 34:209–210 Suppl. S (IF: 1.391).
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Xavier Otazu, & Maria Vanrell. (2005). A surround-induction function to unify assimilation and contrast in a computational model of color apearance.
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David Masip. (2005). Face Classification Using Discriminative Features and Classifier Combination (Jordi Vitria, Ed.). Ph.D. thesis, , .
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Misael Rosales. (2005). A Physics-Based Image Modelling of IVUS as a Geometric and Kinematic System (Petia Radeva, Ed.). Ph.D. thesis, , .
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Oriol Pujol, & Petia Radeva. (2005). On the assessment of texture descriptors in intravascular ultrasound images: A boosting approach to a feasible plaque classification. In Plaque Imaging: Pixel to Molecular Level, IOS Press, J. Suri et al. (Eds.), 113: 276–299, ISBN: 1–58603–516–9.
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Joel Barajas, Jaume Garcia, Francesc Carreras, Sandra Pujades, & Petia Radeva. (2005). Angle Images Using Gabor Filters in Cardiac Tagged MRI. In Proceeding of the 2005 conference on Artificial Intelligence Research and Development (pp. 107–114). Amsterdam, The Netherlands: IOS Press.
Abstract: Tagged Magnetic Resonance Imaging (MRI) is a non-invasive technique used to examine cardiac deformation in vivo. An Angle Image is a representation of a Tagged MRI which recovers the relative position of the tissue respect to the distorted tags. Thus cardiac deformation can be estimated. This paper describes a new approach to generate Angle Images using a bank of Gabor filters in short axis cardiac Tagged MRI. Our method improves the Angle Images obtained by global techniques, like HARP, with a local frequency analysis. We propose to use the phase response of a combination of a Gabor filters bank, and use it to find a more precise deformation of the left ventricle. We demonstrate the accuracy of our method over HARP by several experimental results.
Keywords: Angle Images, Gabor Filters, Harp, Tagged Mri
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Aura Hernandez-Sabate, Debora Gil, & Petia Radeva. (2005). On the usefulness of supervised learning for vessel border detection in IntraVascular Imaging. In Proceeding of the 2005 conference on Artificial Intelligence Research and Development (pp. 67–74). Amsterdam, The Netherlands: IOS Press.
Abstract: IntraVascular UltraSound (IVUS) imaging is a useful tool in diagnosis of cardiac diseases since sequences completely show the morphology of coronary vessels. Vessel borders detection, especially the external adventitia layer, plays a central role in morphological measures and, thus, their segmentation feeds development of medical imaging techniques. Deterministic approaches fail to yield optimal results due to the large amount of IVUS artifacts and vessel borders descriptors. We propose using classification techniques to learn the set of descriptors and parameters that best detect vessel borders. Statistical hypothesis test on the error between automated detections and manually traced borders by 4 experts show that our detections keep within inter-observer variability.
Keywords: classification; vessel border modelling; IVUS
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J. Filipe, Juan Andrade, & J.L. Ferrier. (2005). FAF 2005.
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Daniel Ponsa, Antonio Lopez, Felipe Lumbreras, Joan Serrat, & T. Graf. (2005). 3D Vehicle Sensor based on Monocular Vision.
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