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Author |
Carles Sanchez; Oriol Ramos Terrades; Patricia Marquez; Enric Marti; J.Roncaries; Debora Gil |
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Title  |
Automatic evaluation of practices in Moodle for Self Learning in Engineering |
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Year |
2015 |
Publication |
Journal of Technology and Science Education |
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JOTSE |
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5 |
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2 |
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97-106 |
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IAM; DAG; 600.075; 600.077 |
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Admin @ si @ SRM2015 |
Serial |
2610 |
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Author |
Angel Sappa |
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Title |
Automatic Extraction of Planar Projections from Panoramic Range Images |
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Miscellaneous |
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2004 |
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IEEE Int. Symp. on 3D Data Processing, Visualization and Transmission |
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Thessaloniki (Greece) |
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ADAS @ adas @ Sap2004a |
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454 |
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Author |
Murad Al Haj; Francisco Javier Orozco; Jordi Gonzalez; Juan J. Villanueva |
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Title |
Automatic Face and Facial Features Initialization for Robust and Accurate Tracking |
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Conference Article |
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Year |
2008 |
Publication |
19th International Conference on Pattern Recognition. |
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1– 4 |
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Tampa (Florida) |
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ICPR |
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ISE |
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Call Number |
ISE @ ise @ AOG2008 |
Serial |
1072 |
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Author |
Egils Avots; M. Daneshmanda; Andres Traumann; Sergio Escalera; G. Anbarjafaria |
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Title |
Automatic garment retexturing based on infrared information |
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Journal Article |
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Year |
2016 |
Publication |
Computers & Graphics |
Abbreviated Journal |
CG |
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Volume |
59 |
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28-38 |
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Keywords |
Garment Retexturing; Texture Mapping; Infrared Images; RGB-D Acquisition Devices; Shading |
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Abstract |
This paper introduces a new automatic technique for garment retexturing using a single static image along with the depth and infrared information obtained using the Microsoft Kinect II as the RGB-D acquisition device. First, the garment is segmented out from the image using either the Breadth-First Search algorithm or the semi-automatic procedure provided by the GrabCut method. Then texture domain coordinates are computed for each pixel belonging to the garment using normalised 3D information. Afterwards, shading is applied to the new colours from the texture image. As the main contribution of the proposed method, the latter information is obtained based on extracting a linear map transforming the colour present on the infrared image to that of the RGB colour channels. One of the most important impacts of this strategy is that the resulting retexturing algorithm is colour-, pattern- and lighting-invariant. The experimental results show that it can be used to produce realistic representations, which is substantiated through implementing it under various experimentation scenarios, involving varying lighting intensities and directions. Successful results are accomplished also on video sequences, as well as on images of subjects taking different poses. Based on the Mean Opinion Score analysis conducted on many randomly chosen users, it has been shown to produce more realistic-looking results compared to the existing state-of-the-art methods suggested in the literature. From a wide perspective, the proposed method can be used for retexturing all sorts of segmented surfaces, although the focus of this study is on garment retexturing, and the investigation of the configurations is steered accordingly, since the experiments target an application in the context of virtual fitting rooms. |
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Elsevier |
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HuPBA;MILAB; |
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no |
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Call Number |
Admin @ si @ ADT2016 |
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2759 |
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Author |
Ernest Valveny; B. Lamiroy |
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Title |
Automatic Generation of Browsable Technical Documents. |
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Miscellaneous |
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Year |
2002 |
Publication |
Proceedings of the Sixteenth International Conference on Pattern Recognition ICPR 2002: 188–191. |
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Quebec, Canada |
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DAG |
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DAG @ dag @ VaL2002 |
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301 |
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Author |
Pau Baiget; Joan Soto; Xavier Roca; Jordi Gonzalez |
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Title |
Automatic Generation of Computer-Animated Sequences based on Human Behaviour Modelling |
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Conference Article |
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2007 |
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10th International Conference on Computer Graphics and Artificial Intelligence |
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Address |
Athens (Greece) |
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3IA |
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ISE |
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Call Number |
ISE @ ise @ BSR2007 |
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808 |
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Author |
Jose Manuel Alvarez; Ferran Diego; Joan Serrat; Antonio Lopez |
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Title |
Automatic Ground-truthing using video registration for on-board detection algorithms |
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Conference Article |
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Year |
2009 |
Publication |
16th IEEE International Conference on Image Processing |
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4389 - 4392 |
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Abstract |
Ground-truth data is essential for the objective evaluation of object detection methods in computer vision. Many works claim their method is robust but they support it with experiments which are not quantitatively assessed with regard some ground-truth. This is one of the main obstacles to properly evaluate and compare such methods. One of the main reasons is that creating an extensive and representative ground-truth is very time consuming, specially in the case of video sequences, where thousands of frames have to be labelled. Could such a ground-truth be generated, at least in part, automatically? Though it may seem a contradictory question, we show that this is possible for the case of video sequences recorded from a moving camera. The key idea is transferring existing frame segmentations from a reference sequence into another video sequence recorded at a different time on the same track, possibly under a different ambient lighting. We have carried out experiments on several video sequence pairs and quantitatively assessed the precision of the transformed ground-truth, which prove that our approach is not only feasible but also quite accurate. |
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Cairo, Egypt |
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1522-4880 |
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978-1-4244-5653-6 |
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ICIP |
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ADAS |
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no |
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Call Number |
ADAS @ adas @ ADS2009 |
Serial |
1201 |
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Author |
Vitaliy Konovalov; Albert Clapes; Sergio Escalera |
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Title |
Automatic Hand Detection in RGB-Depth Data Sequences |
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Conference Article |
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Year |
2013 |
Publication |
16th Catalan Conference on Artificial Intelligence |
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91-100 |
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Detecting hands in multi-modal RGB-Depth visual data has become a challenging Computer Vision problem with several applications of interest. This task involves dealing with changes in illumination, viewpoint variations, the articulated nature of the human body, the high flexibility of the wrist articulation, and the deformability of the hand itself. In this work, we propose an accurate and efficient automatic hand detection scheme to be applied in Human-Computer Interaction (HCI) applications in which the user is seated at the desk and, thus, only the upper body is visible. Our main hypothesis is that hand landmarks remain at a nearly constant geodesic distance from an automatically located anatomical reference point.
In a given frame, the human body is segmented first in the depth image. Then, a
graph representation of the body is built in which the geodesic paths are computed from the reference point. The dense optical flow vectors on the corresponding RGB image are used to reduce ambiguities of the geodesic paths’ connectivity, allowing to eliminate false edges interconnecting different body parts. Finally, we are able to detect the position of both hands based on invariant geodesic distances and optical flow within the body region, without involving costly learning procedures. |
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Vic; October 2013 |
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LNCS |
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CCIA |
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HuPBA;MILAB |
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no |
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Call Number |
Admin @ si @ KCE2013 |
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2323 |
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Author |
Sergio Escalera; Josep Moya; Laura Igual; Veronica Violant; Maria Teresa Anguera |
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Title |
Automatic Human Behavior Analysis in ADHD |
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Conference Article |
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2012 |
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Eunethydis 2nd International ADHD Conference |
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Poster |
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EUNETHYDIS |
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MILAB;HuPBA |
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Admin @ si @ EMI2012a |
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2058 |
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Author |
Victoria Ruiz; Angel Sanchez; Jose F. Velez; Bogdan Raducanu |
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Title |
Automatic Image-Based Waste Classification |
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Conference Article |
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Year |
2019 |
Publication |
International Work-Conference on the Interplay Between Natural and Artificial Computation. From Bioinspired Systems and Biomedical Applications to Machine Learning |
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11487 |
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422–431 |
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Computer Vision; Deep learning; Convolutional neural networks; Waste classification |
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Abstract |
The management of solid waste in large urban environments has become a complex problem due to increasing amount of waste generated every day by citizens and companies. Current Computer Vision and Deep Learning techniques can help in the automatic detection and classification of waste types for further recycling tasks. In this work, we use the TrashNet dataset to train and compare different deep learning architectures for automatic classification of garbage types. In particular, several Convolutional Neural Networks (CNN) architectures were compared: VGG, Inception and ResNet. The best classification results were obtained using a combined Inception-ResNet model that achieved 88.6% of accuracy. These are the best results obtained with the considered dataset. |
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Almeria; June 2019 |
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IWINAC |
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LAMP; 600.120 |
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no |
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RSV2019 |
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3273 |
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Author |
Marçal Rusiñol; R.Roset; Josep Llados; C.Montaner |
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Title |
Automatic Index Generation of Digitized Map Series by Coordinate Extraction and Interpretation |
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Conference Article |
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Year |
2011 |
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In Proceedings of the Sixth International Workshop on Digital Technologies in Cartographic Heritage |
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CartoHerit |
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DAG |
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Admin @ si @ RRL2011b |
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1978 |
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Author |
Marçal Rusiñol; R.Roset; Josep Llados; C.Montaner |
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Title |
Automatic Index Generation of Digitized Map Series by Coordinate Extraction and Interpretation |
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2011 |
Publication |
e-Perimetron |
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ePER |
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6 |
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4 |
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219-229 |
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By means of computer vision algorithms scanned images of maps are processed in order to extract relevant geographic information from printed coordinate pairs. The meaningful information is then transformed into georeferencing information for each single map sheet, and the complete set is compiled to produce a graphical index sheet for the map series along with relevant metadata. The whole process is fully automated and trained to attain maximum effectivity and throughput. |
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DAG |
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Admin @ si @ RRL2011a |
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1765 |
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Author |
Laura Igual; Joan Carles Soliva; Roger Gimeno; Sergio Escalera; Oscar Vilarroya; Petia Radeva |
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Title |
Automatic Internal Segmentation of Caudate Nucleus for Diagnosis of Attention Deficit Hyperactivity Disorder |
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Conference Article |
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2012 |
Publication |
9th International Conference on Image Analysis and Recognition |
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7325 |
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II |
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222-229 |
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Poster
Studies on volumetric brain Magnetic Resonance Imaging (MRI) showed neuroanatomical abnormalities in pediatric Attention-Deficit/Hyperactivity Disorder (ADHD). In particular, the diminished right caudate volume is one of the most replicated findings among ADHD samples in morphometric MRI studies. In this paper, we propose a fully-automatic method for internal caudate nucleus segmentation based on machine learning. Moreover, the ratio between right caudate body volume and the bilateral caudate body volume is applied in a ADHD diagnostic test. We separately validate the automatic internal segmentation of caudate in head and body structures and the diagnostic test using real data from ADHD and control subjects. As a result, we show accurate internal caudate segmentation and similar performance among the proposed automatic diagnostic test and the manual annotation. |
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Aveiro, Portugal |
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0302-9743 |
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978-3-642-31297-7 |
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ICIAR |
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OR; HuPBA; MILAB |
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no |
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Admin @ si @ ISG2012 |
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2059 |
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Author |
Jose Antonio Rodriguez; Gemma Sanchez; Josep Llados |
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Title |
Automatic Interpretation of Proofreading Sketches |
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Miscellaneous |
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Year |
2006 |
Publication |
3rd Eurographics Workshop on Sketch Based Interfaces and Modeling (SBIM´06), 35–42 |
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Vienna (Austria) |
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DAG |
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DAG @ dag @ RSL2006a |
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716 |
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Author |
Ellen J.L. Brunenberg; Oriol Pujol; Bart M. Ter Haar Romeny; Petia Radeva |
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Title |
Automatic IVUS Segmentation of Atherosclerotic Plaque with Stop & Go Snake |
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Miscellaneous |
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2006 |
Publication |
9th International Conference on Medical Image Computing and Computer–Assisted Intervention (MICCAI´06), 9–16 |
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Address |
Copenhagen (Denmark) |
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Corporate Author |
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Summary Language |
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Original Title |
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Series Editor |
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Series Title |
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Abbreviated Series Title |
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Series Volume |
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Edition |
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ISSN |
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ISBN |
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Expedition |
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Conference |
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Notes |
MILAB;HuPBA |
Approved |
no |
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Call Number |
BCNPCL @ bcnpcl @ BPT2006 |
Serial |
767 |
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