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Joan Mas. (2005). Syntactic approaches to recognize bi-dimensional shapes in graphics recognition. Application to sketching interfaces.
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Anton Cervantes. (2005). Biometric Newborn Identification.
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Alicia Fornes. (2005). Analysis of Old Handwritten Musical Scores.
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Ignasi Rius. (2005). Articulated 3D Human Motion Moldeling for Tracking and Reconstruction.
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Dani Rowe. (2005). Probabilistic Image-based Tracking in Complex Human Environments.
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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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Bogdan Raducanu, & Jordi Vitria. (2005). A Robust Particle Filter-based Face Tracker Using a Combination of Color and Geometric Information.
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Jaume Rodriguez, S. Yacoub, Gemma Sanchez, & Josep Llados. (2006). Performance Evaluation, Comparison and Combination of Commercial Handwriting Recognition Engines.
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Marçal Rusiñol. (2006). A Model of Vectorial Signatures in Terms of Expressive Sub-Shapes: Symbol Indexation in Technical Documents.
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Jose Antonio Rodriguez. (2006). Pen-based Interfaces and Recognition: Application to Proofreading Interpretation.
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Miquel Ferrer. (2006). Spectral Median Graphs and its Application to Graphical Symbol Recognition.
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David Aldavert. (2006). Visual Simultaneous Localization and Mapping.
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David Geronimo. (2006). Model Features and Horizon Line Estimation for Pedestrian Detection in Advanced Driver Assistance Systems. Master's thesis, , .
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Fernando Vilariño. (2006). A Machine Learning Approach for Intestinal Motility Assessment with Capsule Endoscopy (Petia Radeva, Ed.). Ph.D. thesis, , .
Abstract: Intestinal motility assessment with video capsule endoscopy arises as a novel and challenging clinical fieldwork. This technique is based on the analysis of the patterns of intestinal contractions obtained by labelling all the motility events present in a video provided by a capsule with a wireless micro-camera, which is ingested by the patient. However, the visual analysis of these video sequences presents several im- portant drawbacks, mainly related to both the large amount of time needed for the visualization process, and the low prevalence of intestinal contractions in video.
In this work we propose a machine learning system to automatically detect the intestinal contractions in video capsule endoscopy, driving a very useful but not fea- sible clinical routine into a feasible clinical procedure. Our proposal is divided into two different parts: The first part tackles the problem of the automatic detection of phasic contractions in capsule endoscopy videos. Phasic contractions are dynamic events spanning about 4-5 seconds, which show visual patterns with a high variability. Our proposal is based on a sequential design which involves the analysis of textural, color and blob features with powerful classifiers such as SVM. This approach appears to cope with two basic aims: the reduction of the imbalance rate of the data set, and the modular construction of the system, which adds the capability of including domain knowledge as new stages in the cascade. The second part of the current work tackles the problem of the automatic detection of tonic contractions. Tonic contrac- tions manifest in capsule endoscopy as a sustained pattern of the folds and wrinkles of the intestine, which may be prolonged for an undetermined span of time. Our proposal is based on the analysis of the wrinkle patterns, presenting a comparative study of diverse features and classification methods, and providing a set of appro- priate descriptors for their characterization. We provide a detailed analysis of the performance achieved by our system both in a qualitative and a quantitative way.
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