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Author |
Francesco Brughi |
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Title |
Artistic Heritage Motive Retrieval: an Explorative Study |
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2013 |
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CVC Technical Report |
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176 |
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Master's thesis |
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IAM |
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Admin @ si @ Bru2013 |
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2410 |
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Author |
Antonio Esteban Lansaque |
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Title |
3D reconstruction and recognition using structured ligth |
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Report |
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2014 |
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CVC Technical Report |
Abbreviated Journal |
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179 |
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This work covers the problem of 3D reconstruction, recognition and 6DOF pose estimation. The goal of this project is to reconstruct a 3D scene and to align an object model of the industrial pieces onto the reconstructed scene. The reconstruction algorithm is based on stereo techniques and the recognition algorithm is based on SHOT descriptors computed on a set of uniform keypoints. Correspondences are used to estimate a first 6DOF transformation that maps the model onto the scene and then ICP algorithm is used to refine the transformation. In order to check the effectiveness of the proposed algorithm, several experiments were performed. These experiments were conducted on a lab environment in order to get results under the same conditions in all of them. Although obtained results are not real time results, the proposed algorithm ends up with high rates of object recognition. |
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UAB; September 2014 |
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Master's thesis |
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IAM; 600.075 |
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Admin @ si @ Est2014 |
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2578 |
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Author |
Eric Amiel |
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Title |
Visualisation de vaisseaux sanguins |
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2005 |
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Rapport de Stage |
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Université Paul Sabatier Toulouse III |
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Bachelor's thesis |
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Université Paul Sabatier Toulouse III |
Place of Publication |
Toulouse |
Editor |
Enric Marti |
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French |
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French |
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IUP Systèmes Intelligents |
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IAM |
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IAM @ iam @ Ami2005 |
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1690 |
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Author |
Albert Andaluz |
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Title |
LV Contour Segmentation in TMR images using Semantic Description of Tissue and Prior Knowledge Correction |
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2009 |
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CVC Technical Report |
Abbreviated Journal |
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142 |
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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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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. |
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Master's thesis |
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Bellaterra 08193, Barcelona, Spain |
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IAM; |
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IAM @ iam @ And2009 |
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1667 |
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Author |
Jaume Garcia |
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Title |
Propagacio de fronts per a la segmentacio en imatges IVUS |
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2002 |
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Technical Report |
Abbreviated Journal |
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65 |
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CVC (UAB) |
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IAM |
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Call Number |
IAM @ iam @ Gar2002 |
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328 |
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Author |
Jaume Garcia |
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Title |
Generalized Active Shape Models Applied to Cardiac Function Analysis |
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Report |
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2004 |
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CVC Technical Report |
Abbreviated Journal |
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78 |
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Cardiac Analysis; Deformable Models; Active Contour Models; Active Shape Models; Tagged MRI; HARP; Contrast Echocardiography. |
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Abstract |
Medical imaging is very useful in the assessment and treatment of many diseases. To deal with the great amount of data provided by imaging scanners and extract quantitative information that physicians can interpret, many analysis algorithms have been developed. Any process of analysis always consists of a first step of segmenting some particular structure. In medical imaging, structures are not always well defined and suffer from noise artifacts thus, ordinary segmentation methods are not well suited. The ones that seem to give better results are those based on deformable models. Nevertheless, despite their capability of mixing image features together with smoothness constraints that may compensate for image irregularities, these are naturally local methods, i. e., each node of the active contour evolve taking into account information about its neighbors and some other weak constraints about flexibility and smoothness, but not about the global shape that they should find. Due to the fact that structures to be segmented are the same for all cases but with some inter and intra-patient variation, the incorporation of a priori knowledge about shape in the segmentation method will provide robustness to it. Active Shape Models is an algorithm based on the creation of a shape model called Point Distribution Model. It performs a segmentation using only shapes similar than those previously learned from a training set that capture most of the variation presented by the structure. This algorithm works by updating shape nodes along a normal segment which often can be too restrictive. For this reason we propose a generalization of this algorithm that we call Generalized Active Shape Models and fully integrates the a priori knowledge given by the Point Distribution Model with deformable models or any other appropriate segmentation method. Two different applications to cardiac imaging of this generalized method are developed and promising results are shown. |
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CVC (UAB) |
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IAM; |
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Call Number |
IAM @ iam @ Gar2004 |
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1513 |
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Author |
Debora Gil |
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Title |
Regularized Curvature Flow |
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Report |
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Year |
2002 |
Publication |
CVC Technical Report |
Abbreviated Journal |
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63 |
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Computer Vision Centre |
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IAM; |
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IAM @ iam @ Gil2002 |
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1518 |
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Author |
Debora Gil; Petia Radeva |
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Title |
Curvature based Distance Maps |
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2003 |
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CVC Technical Report |
Abbreviated Journal |
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70 |
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Computer Vision Center |
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IAM;MILAB |
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IAM @ iam @ GIR2003a |
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1534 |
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Author |
Aura Hernandez-Sabate |
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Title |
Automatic adventitia segmentation in IntraVascular UltraSound images |
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2005 |
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CVC Technical Report |
Abbreviated Journal |
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85 |
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A usual tool in cardiac disease diagnosis is vessel plaque assessment by analysis of IVUS sequences. Manual detection of lumen-intima, intima-media and media-adventitia vessel borders is the main activity of physicians in the process of plaque quantification. Large variety in vessel border descriptors, as well as, shades, artifacts and blurred response due to ultrasound physical properties troubles automated media-adventitia segmentation. This experimental work presents a solution to such a complex problem. The process blends advanced anisotropic filtering operators and statistic classification techniques, achieving an efficient vessel border modelling strategy. First of all, we introduce the theoretic base of the method. After that, we show the steps of the algorithm, validating the method with statistics that show that the media-adventitia border detection achieves an accuracy in the range of inter-observer variability regardless of plaque nature, vessel geometry and incomplete vessel borders. Finally, we present a little Matlab application to the automatic media-adventitia border. |
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Master's thesis |
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08193 Bellaterra, Barcelona (Spain) |
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IAM; |
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IAM @ iam @ Her2005 |
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1544 |
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Author |
Aura Hernandez-Sabate; Debora Gil; Petia Radeva |
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Title |
A Deterministic-Statistical Strategy for Adventitia Segmentation in IVUS images |
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2005 |
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CVC Technical Report |
Abbreviated Journal |
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89 |
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A useful tool for some specific studies in cardiac disease diagnosis is vessel plaque assessment by analysis of IVUS sequences. Manual detection of luminal (inner) and media-adventitia (external) vessel borders is the main activity of physicians in the process of lumen narrowing (plaque) quantification. Difficult definition of vessel border descriptors, as well as, shades, artifacts and blurred signal response due to ultrasound physical properties troubles automated adventitia segmentation. In order to efficiently approach such a complex problem, we propose blending advanced anisotropic filtering operators and statistical classification techniques into a vessel border modelling strategy. Our systematic statistical analysis shows that the reported adventitia detection achieves an accuracy in the range of inter-observer variability regardless of plaque nature, vessel geometry and incomplete vessel borders. |
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IAM; MILAB |
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IAM @ iam @ HGR2005a |
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1548 |
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