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Joan Mas, Gemma Sanchez, Josep Llados, & B. Lamiroy. (2007). An Incremental On-line Parsing Algorithm for Recognizing Sketching Diagrams. In 9th IEEE International Conference on Document Analysis and Recognition (Vol. 1, 452–456).
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Josep Llados, & Gemma Sanchez. (2007). Indexing Historical Documents by Word Shape Signatures. In 9th International Conference on Document Analysis and Recognition (Vol. 1, 362–366).
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Oriol Ramos Terrades, Salvatore Tabbone, & Ernest Valveny. (2007). A Review of Shape Descriptors for Document Analysis. In 9th International Conference on Document Analysis and Recognition (Vol. 1, 227–231).
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S. Chanda, Oriol Ramos Terrades, & Umapada Pal. (2007). SVM Based Scheme for Thai and English Script Identification. In 9th International Conference on Document Analysis and Recognition (Vol. 1, 551–555).
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Enric Marti, Debora Gil, & Carme Julia. (2006). Una experiencia de PBL en la docencia de la asignatura de Graficos por Computador en Ingenieria Informatica (Vol. 1).
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David Masip, Agata Lapedriza, & Jordi Vitria. (2008). Multitask Learning: An Application to Incremental Face Recognition. In 3rd International Conference on Computer Vision Theory and Applications (Vol. 1, 585–590).
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Ignasi Rius, Jordi Gonzalez, Mikhail Mozerov, & Xavier Roca. (2008). Automatic Learning of 3D Pose Variability in Walking Performances for Gait Analysis. International Journal for Computational Vision and Biomechanics, 33–43.
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Josep Llados, Ernest Valveny, & Enric Marti. (2000). Symbol Recognition in Document Image Analysis: Methods and Challenges. Recent Research Developments in Pattern Recognition, Transworld Research Network,, 1, 151–178.
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Enric Marti, Debora Gil, & Carme Julia. (2008). Experiencia d aplicació de la metodología d aprenentatge per proyectes en assignatures d Enginyeria Informàtica per a una millor adaptació als crèdits ECTS i EEES (IDES-UAB, & E. A. M.Enric Martinez, Eds.) (Vol. 1). UAB.
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Enric Marti, Carme Julia, & Debora Gil. (2007). PBL en la docencia de gráficos por computador (Vol. 1). Valladolid.
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Sandra Pujades, Francesc Carreras, Manuel Ballester, Jaume Garcia, & Debora Gil. (2008). A Normalized Parametric Domain for the Analysis of the Left Ventricular Function. In Proceedings of the Third International Conference on Computer Vision Theory and Applications (VISAPP’08) (Vol. 1, pp. 267–274).
Abstract: Impairment of left ventricular (LV) contractility due to cardiovascular diseases is reflected in LV motion patterns. The mechanics of any muscle strongly depends on the spatial orientation of its muscular fibers since the motion that the muscle undergoes mainly takes place along the fiber. The helical ventricular myocardial band (HVMB) concept describes the myocardial muscle as a unique muscular band that twists in space in a non homogeneous fashion. The 3D anisotropy of the ventricular band fibers suggests a regional analysis of the heart motion. Computation of normality models of such motion can help in the detection and localization of any cardiac disorder. In this paper we introduce, for the first time, a normalized parametric domain that allows comparison of the left ventricle motion across patients. We address, both, extraction of the LV motion from Tagged Magnetic Resonance images, as well as, defining a mapping of the LV to a common normalized domain. Extraction of normality motion patterns from 17 healthy volunteers shows the clinical potential of our LV parametrization.
Keywords: Helical Ventricular Myocardial Band; Myocardial Fiber; Tagged Magnetic Resonance; HARP; Optical Flow Variational Framework; Gabor Filters; B-Splines.
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Jorge Bernal, Fernando Vilariño, & F. Javier Sanchez. (2011). Towards Intelligent Systems for Colonoscopy. In Paul Miskovitz (Ed.), Colonoscopy (Vol. 1, pp. 257–282). Intech.
Abstract: In this chapter we present tools that can be used to build intelligent systems for colonoscopy.
The idea is, by using methods based on computer vision and artificial intelligence, add significant value to the colonoscopy procedure. Intelligent systems are being used to assist in other medical interventions
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Jorge Bernal, F. Javier Sanchez, & Fernando Vilariño. (2011). Depth of Valleys Accumulation Algorithm for Object Detection. In 14th Congrès Català en Intel·ligencia Artificial (Vol. 1, pp. 71–80).
Abstract: This work aims at detecting in which regions the objects in the image are by using information about the intensity of valleys, which appear to surround ob- jects in images where the source of light is in the line of direction than the camera. We present our depth of valleys accumulation method, which consists of two stages: first, the definition of the depth of valleys image which combines the output of a ridges and valleys detector with the morphological gradient to measure how deep is a point inside a valley and second, an algorithm that denotes points of the image as interior to objects those which are inside complete or incomplete boundaries in the depth of valleys image. To evaluate the performance of our method we have tested it on several application domains. Our results on object region identification are promising, specially in the field of polyp detection in colonoscopy videos, and we also show its applicability in different areas.
Keywords: Object Recognition, Object Region Identification, Image Analysis, Image Processing
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David Vazquez. (2013). Domain Adaptation of Virtual and Real Worlds for Pedestrian Detection (Antonio Lopez, & Daniel Ponsa, Eds.) (Vol. 1). Ph.D. thesis, Ediciones Graficas Rey, Barcelona.
Abstract: Pedestrian detection is of paramount interest for many applications, e.g. Advanced Driver Assistance Systems, Intelligent Video Surveillance and Multimedia systems. Most promising pedestrian detectors rely on appearance-based classifiers trained with annotated data. However, the required annotation step represents an intensive and subjective task for humans, what makes worth to minimize their intervention in this process by using computational tools like realistic virtual worlds. The reason to use these kind of tools relies in the fact that they allow the automatic generation of precise and rich annotations of visual information. Nevertheless, the use of this kind of data comes with the following question: can a pedestrian appearance model learnt with virtual-world data work successfully for pedestrian detection in real-world scenarios?. To answer this question, we conduct different experiments that suggest a positive answer. However, the pedestrian classifiers trained with virtual-world data can suffer the so called dataset shift problem as real-world based classifiers does. Accordingly, we have designed different domain adaptation techniques to face this problem, all of them integrated in a same framework (V-AYLA). We have explored different methods to train a domain adapted pedestrian classifiers by collecting a few pedestrian samples from the target domain (real world) and combining them with many samples of the source domain (virtual world). The extensive experiments we present show that pedestrian detectors developed within the V-AYLA framework do achieve domain adaptation. Ideally, we would like to adapt our system without any human intervention. Therefore, as a first proof of concept we also propose an unsupervised domain adaptation technique that avoids human intervention during the adaptation process. To the best of our knowledge, this Thesis work is the first demonstrating adaptation of virtual and real worlds for developing an object detector. Last but not least, we also assessed a different strategy to avoid the dataset shift that consists in collecting real-world samples and retrain with them in such a way that no bounding boxes of real-world pedestrians have to be provided. We show that the generated classifier is competitive with respect to the counterpart trained with samples collected by manually annotating pedestrian bounding boxes. The results presented on this Thesis not only end with a proposal for adapting a virtual-world pedestrian detector to the real world, but also it goes further by pointing out a new methodology that would allow the system to adapt to different situations, which we hope will provide the foundations for future research in this unexplored area.
Keywords: Pedestrian Detection; Domain Adaptation
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Carles Sanchez, Debora Gil, Antoni Rosell, Albert Andaluz, & F. Javier Sanchez. (2013). Segmentation of Tracheal Rings in Videobronchoscopy combining Geometry and Appearance. In Sebastiano Battiato and José Braz (Ed.), Proceedings of the International Conference on Computer Vision Theory and Applications (Vol. 1, pp. 153–161). LNCS. Portugal: SciTePress.
Abstract: Videobronchoscopy is a medical imaging technique that allows interactive navigation inside the respiratory pathways and minimal invasive interventions. Tracheal procedures are ordinary interventions that require measurement of the percentage of obstructed pathway for injury (stenosis) assessment. Visual assessment of stenosis in videobronchoscopic sequences requires high expertise of trachea anatomy and is prone to human error. Accurate detection of tracheal rings is the basis for automated estimation of the size of stenosed trachea. Processing of videobronchoscopic images acquired at the operating room is a challenging task due to the wide range of artifacts and acquisition conditions. We present a model of the geometric-appearance of tracheal rings for its detection in videobronchoscopic videos. Experiments on sequences acquired at the operating room, show a performance close to inter-observer variability
Keywords: Video-bronchoscopy, tracheal ring segmentation, trachea geometric and appearance model
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