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R. Xandri. (2002). Un metode de vectoritzacio basat en l’aprimament.
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Cristina Cañero, & Petia Radeva. (2002). Predictive (un)distortion model for 3D reconstruction purpouses.
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Lluis Barcelo. (2002). Accurate video mosaicing with moving objects.
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Paramveer S. Dhillon, Francisco Javier Orozco, & Jordi Gonzalez. (2008). Real-Time Monocular Face Tracking Using and Active Camera.
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Eduard Vazquez. (2007). Distribution Characterization using Topological Features. Application to Colour Image Processing. Master's thesis, , .
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David Vazquez, David Geronimo, & Antonio Lopez. (2009). The effect of the distance in pedestrian detection (Vol. 149). Master's thesis, , .
Abstract: Pedestrian accidents are one of the leading preventable causes of death. In order to reduce the number of accidents, in the last decade the pedestrian protection systems have been introduced, a special type of advanced driver assistance systems, in witch an on-board camera explores the road ahead for possible collisions with pedestrians in order to warn the driver or perform braking actions. As a result of the variability of the appearance, pose and size, pedestrian detection is a very challenging task. So many techniques, models and features have been proposed to solve the problem. As the appearance of pedestrians varies signi cantly as a function of distance, a system based on multiple classi ers specialized on diferent depths is likely to improve the overall performance with respect to a typical system based on a general detector. Accordingly, the main aim of this work is to explore the e ect of the distance in pedestrian detection. We have evaluated three pedestrian detectors (HOG, HAAR and EOH) in two di erent databases (INRIA and Daimler09) for two di erent sizes (small and big). By a extensive set of experiments we answer to questions like which datasets and evaluation methods are the most adequate, which is the best method for each size of the pedestrians and why or how do the method optimum parameters vary with respect to the distance
Keywords: Pedestrian Detection
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Albert Andaluz. (2009). LV Contour Segmentation in TMR images using Semantic Description of Tissue and Prior Knowledge Correction (Vol. 142). Master's thesis, , Bellaterra 08193, Barcelona, Spain.
Abstract: The Diagnosis of Left Ventricle (LV) pathologies is related to regional wall motion analysis. Health indicator scores such as the rotation and the torsion are useful for the diagnose of the Left Ventricle (LV) function. However, this requires proper identification of LV segments. On one hand, manual segmentation is robust, but it is slow and requires medical expertise. On the other hand, the tag pattern in Tagged Magnetic Resonance (TMR) sequences is a problem for the automatic segmentation of the LV boundaries. Consequently, we propose a method based in the classical formulation of parametric Snakes, combined with Active Shape models. Our semantic definition of the LV is tagged tissue that experiences motion in the systolic cycle. This defines two energy potentials for the Snake convergence. Additionally, the mean shape corrects excessive deviation from the anatomical shape. We have validated our approach in 15 healthy volunteers and two short axis cuts. In this way, we have compared the automatic segmentations to manual shapes outlined by medical experts. Also, we have explored the accuracy of clinical scores computed using automatic contours. The results show minor divergence in the approximation and the manual segmentations as well as robust computation of clinical scores in all cases. From this we conclude that the proposed method is a promising support tool for clinical analysis.
Keywords: Active Contour Models; Snakes; Active Shape Models; Deformable Templates; Left Ventricle Segmentation; Generalized Orthogonal Procrustes Analysis; Harmonic Phase Flow; Principal Component Analysis; Tagged Magnetic Resonance
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Antonio Lopez, Felipe Lumbreras, & Joan Serrat. (1997). Efficient computation of local creaseness. CVC, Bellaterra (Spain).
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Mohammad Rouhani. (2009). 3D Data Fitting and Tracking for Real Time Applications (Vol. 138). Master's thesis, , .
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Marc Serra. (2010). Estimating Intrinsic Images from Physical and Categorical Color Cues (Vol. 151). Master's thesis, , .
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Ahmed Mounir Gad. (2010). Object Localization Enhancement by Multiple Segmentation Fusion (Vol. 152). Master's thesis, , .
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Antonio Hernandez. (2010). Pose and Face Recovery via Spatio-temporal GrabCut Human Segmentation (Vol. 153). Master's thesis, , .
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Jorge Bernal, Fernando Vilariño, & F. Javier Sanchez. (2010). Feature Detectors and Feature Descriptors: Where We Are Now (Vol. 154).
Abstract: Feature Detection and Feature Description are clearly nowadays topics. Many Computer Vision applications rely on the use of several of these techniques in order to extract the most significant aspects of an image so they can help in some tasks such as image retrieval, image registration, object recognition, object categorization and texture classification, among others. In this paper we define what Feature Detection and Description are and then we present an extensive collection of several methods in order to show the different techniques that are being used right now. The aim of this report is to provide a glimpse of what is being used currently in these fields and to serve as a starting point for future endeavours.
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Lluis Pere de las Heras. (2010). Syntactic Model for Semantic Document Analysis (Vol. 158).
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Anjan Dutta. (2010). Symbol Spotting in Graphical Documents by Serialized Subgraph Matching (Vol. 159). Master's thesis, , .
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