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J.L. Pech-Pacheco, J. Alvarez-Borrego, Gabriel Cristobal, & Matthias S. Keil. (2003). Automatic object identification irrespective to geometric changes. Optical Engineering, 42(2): 551–559 (IF: 0.877).
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Matthias S. Keil. (2006). Smooth Gradient Representations as a Unifying Account of Chevreul’s Illusion, Mach Bands, and a Variant of the Ehrenstein Disk. NEURALCOMPUT - Neural Computation, 871–903.
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David Masip, & Jordi Vitria. (2006). Boosted discriminant projections for nearest neighbor classification. Pattern Recognition, 39(2): 164–170.
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Oriol Ramos Terrades, & Ernest Valveny. (2006). A new use of the ridgelets transform for describing linear singularities in images. PRL - Pattern Recognition Letters, 27(6), 587–596.
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Fadi Dornaika, & Angel Sappa. (2006). Improving Appearance-Based 3D Face Tracking Using Sparse Stereo Data.
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Angel Sappa. (2006). Unsupervised Contour Closure Algorithm for Range Image Edge-Based Segmentation. IEEE Transactions on Image Processing, 15(2):377–384.
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Carme Julia, Joan Serrat, Antonio Lopez, Felipe Lumbreras, & Daniel Ponsa. (2006). Motion segmentation through factorization. Application to night driving assistance.
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Oriol Ramos Terrades, & Ernest Valveny. (2005). Local Norm Features based on ridgelets Transform.
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Jaume Amores, N. Sebe, & Petia Radeva. (2006). Boosting the distance estimation: Application to the K-Nearest Neighbor Classifier. PRL - Pattern Recognition Letters, 27(3), 201–209.
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Joaquin Salas, Wendy Avalos, Rafael Castañeda, & Mario Maya. (2006). A machine-vision system to measure the parameters describing the performance of a Foucault pendulum. Machine Vision and Applications, 133–138.
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J. Nuñez, Xavier Otazu, & M.T. Merino. (2005). A Multiresolution-Based Method for the Determination of the Relative Resolution between Images. First Application to Remote Sensing and Medical Images. International Journal of Imaging Systems and Technology, 15(5): 225–235 (IF: 0.439).
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Oriol Pujol, Petia Radeva, & Jordi Vitria. (2006). Discriminant ECOC: A Heuristic Method for Application Dependent Design of Error Correcting Output Codes. IEEE Transactions on Pattern Analysis and Machine Intelligence, 28(6): 1007–1012.
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Fernando Vilariño, Ludmila I. Kuncheva, & Petia Radeva. (2006). ROC curves and video analysis optimization in intestinal capsule endoscopy. PRL - Pattern Recognition Letters, 27(8), 875–881.
Abstract: Wireless capsule endoscopy involves inspection of hours of video material by a highly qualified professional. Time episodes corresponding to intestinal contractions, which are of interest to the physician constitute about 1% of the video. The problem is to label automatically time episodes containing contractions so that only a fraction of the video needs inspection. As the classes of contraction and non-contraction images in the video are largely imbalanced, ROC curves are used to optimize the trade-off between false positive and false negative rates. Classifier ensemble methods and simple classifiers were examined. Our results reinforce the claims from recent literature that classifier ensemble methods specifically designed for imbalanced problems have substantial advantages over simple classifiers and standard classifier ensembles. By using ROC curves with the bagging ensemble method the inspection time can be drastically reduced at the expense of a small fraction of missed contractions.
Keywords: ROC curves; Classification; Classifiers ensemble; Detection of intestinal contractions; Imbalanced classes; Wireless capsule endoscopy
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