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Author |
Farhan Riaz; Fernando Vilariño; Mario Dinis-Ribeiro; Miguel Coimbraln |
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Title |
Identifying Potentially Cancerous Tissues in Chromoendoscopy Images |
Type |
Conference Article |
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Year |
2011 |
Publication |
5th Iberian Conference on Pattern Recognition and Image Analysis |
Abbreviated Journal |
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Volume |
6669 |
Issue |
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Pages |
709-716 |
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Keywords  |
Endoscopy, Computer Assisted Diagnosis, Gradient. |
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Abstract |
The dynamics of image acquisition conditions for gastroenterology imaging scenarios pose novel challenges for automatic computer assisted decision systems. Such systems should have the ability to mimic the tissue characterization of the physicians. In this paper, our objective is to compare some feature extraction methods to classify a Chromoendoscopy image into two different classes: Normal and Potentially cancerous. Results show that LoG filters generally give best classification accuracy among the other feature extraction methods considered. |
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Address |
Las Palmas de Gran Canaria. Spain |
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Publisher |
Springer |
Place of Publication |
Berlin |
Editor |
J. Vitria, J.M. Sanches, and M. Hernandez |
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LNCS |
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ISSN |
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ISBN |
978-3-642-21256-7 |
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Area |
800 |
Expedition |
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Conference |
IbPRIA |
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Notes |
MV;SIAI |
Approved |
no |
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Call Number |
Admin @ si @ RVD2011; IAM @ iam @ RVD2011 |
Serial |
1726 |
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Author |
Fernando Vilariño; Panagiota Spyridonos; Jordi Vitria; Fernando Azpiroz; Petia Radeva |
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Title |
Cascade analysis for intestinal contraction detection |
Type |
Conference Article |
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Year |
2006 |
Publication |
20th International Congress and exhibition Computer Assisted Radiology and Surgery |
Abbreviated Journal |
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Issue |
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Pages |
9-10 |
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Keywords |
intestine video analysis, anisotropic features, support vector machine, cascade of classifiers |
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Abstract |
In this work, we address the study of intestinal contractions in a novel approach based on a machine learning framework to process data from Wireless Capsule Video Endoscopy. Wireless endoscopy represents a unique way to visualize the intestine motility by creating long videos to visualize intestine dynamics. In this paper we argue that to analyze huge amount of wireless endoscopy data and define robust methods for contraction detection we should base our approach on sophisticated machine learning techniques. In particular, we propose a cascade of classifiers in order to remove different physiological phenomenon and obtain the motility pattern of small intestines. Our results show obtaining high specificity and sensitivity rates that highlight the high efficiency of the selected approach and support the feasibility of the proposed methodology in the automatic detection and analysis of intestine contractions. |
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Address |
Osaka (Japan) |
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Area |
800 |
Expedition |
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Conference |
CARS |
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Notes |
MV;OR;MILAB;SIAI |
Approved |
no |
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Call Number |
BCNPCL @ bcnpcl @ VSV2006a; IAM @ iam @ VSV2006h |
Serial |
726 |
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Author |
Carles Sanchez; Jorge Bernal; Debora Gil; F. Javier Sanchez |
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Title |
On-line lumen centre detection in gastrointestinal and respiratory endoscopy |
Type |
Conference Article |
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Year |
2013 |
Publication |
Second International Workshop Clinical Image-Based Procedures |
Abbreviated Journal |
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Volume |
8361 |
Issue |
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Pages |
31-38 |
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Keywords |
Lumen centre detection; Bronchoscopy; Colonoscopy |
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Abstract |
We present in this paper a novel lumen centre detection for gastrointestinal and respiratory endoscopic images. The proposed method is based on the appearance and geometry of the lumen, which we defined as the darkest image region which centre is a hub of image gradients. Experimental results validated on the first public annotated gastro-respiratory database prove the reliability of the method for a wide range of images (with precision over 95 %). |
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Address |
Nagoya; Japan; September 2013 |
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Publisher |
Springer International Publishing |
Place of Publication |
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Editor |
Erdt, Marius and Linguraru, Marius George and Oyarzun Laura, Cristina and Shekhar, Raj and Wesarg, Stefan and González Ballester, Miguel Angel and Drechsler, Klaus |
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LNCS |
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Edition |
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ISSN |
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ISBN |
978-3-319-05665-4 |
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Area |
800 |
Expedition |
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Conference |
CLIP |
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Notes |
MV; IAM; 600.047; 600.044; 600.060 |
Approved |
no |
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Call Number |
Admin @ si @ SBG2013 |
Serial |
2302 |
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Author |
Jorge Bernal; F. Javier Sanchez; Fernando Vilariño |
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Title |
Current Challenges on Polyp Detection in Colonoscopy Videos: From Region Segmentation to Region Classification. a Pattern Recognition-based Approach.ased Approach |
Type |
Conference Article |
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Year |
2011 |
Publication |
2nd International Workshop on Medical Image Analysis and Descriptionfor Diagnosis Systems |
Abbreviated Journal |
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Volume |
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Issue |
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Pages |
62-71 |
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Keywords |
Medical Imaging, Colonoscopy, Pattern Recognition, Segmentation, Polyp Detection, Region Description, Machine Learning, Real-time. |
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Abstract |
In this paper we present our approach on real-time polyp detection in colonoscopy videos. Our method consists of three stages: Image Segmentation, Region Description and Image Classification. Taking into account the constraints of our project, we introduce our segmentation system that is based on the model of appearance of the polyp that we have defined after observing real videos from colonoscopy processes. The output of this stage will ideally be a low number of regions of which one of them should cover the whole polyp region (if there is one in the image). This regions will be described in terms of features and, as a result of a machine learning schema, classified based on the values that they have for the several features that we will use on their description. Although we are still on the early stages of the project, we present some preliminary segmentation results that indicates that we are going in a good direction. |
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Address |
Rome, Italy |
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Publisher |
SciTePress |
Place of Publication |
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Editor |
Djemal, Khalifa |
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Medium |
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Area |
800 |
Expedition |
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Conference |
MIAD |
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Notes |
MV;SIAI |
Approved |
no |
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Call Number |
IAM @ iam @ BSV2011a |
Serial |
1695 |
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Author |
Jorge Bernal; F. Javier Sanchez; Fernando Vilariño |
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Title |
Depth of Valleys Accumulation Algorithm for Object Detection |
Type |
Conference Article |
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Year |
2011 |
Publication |
14th Congrès Català en Intel·ligencia Artificial |
Abbreviated Journal |
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Volume |
1 |
Issue |
1 |
Pages |
71-80 |
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Keywords |
Object Recognition, Object Region Identification, Image Analysis, Image Processing |
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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. |
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Address |
Lleida |
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Corporate Author |
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ISSN |
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ISBN |
978-1-60750-841-0 |
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Area |
800 |
Expedition |
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Conference |
CCIA |
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Notes |
MV;SIAI |
Approved |
no |
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Call Number |
IAM @ iam @ BSV2011b |
Serial |
1699 |
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Author |
Jorge Bernal |
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Title |
Use of Projection and Back-projection Methods in Bidimensional Computed Tomography Image Reconstruction |
Type |
Report |
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Year |
2009 |
Publication |
CVC Tecnical Report |
Abbreviated Journal |
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Volume |
141 |
Issue |
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Pages |
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Keywords |
Projection, Back-projection, CT scan, Euclidean geometry, Radon transform |
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Abstract |
One of the biggest drawbacks related to the use of CT scanners is the cost (in memory and in time) associated. In this project many methods to simulate their functioning, but in a more feasible way (taking an industrial point of view), will be studied.
The main group of techniques that are being used are the one entitled as ’back-projection’. The concept behind is to simulate the X ray emission in CT scans by lines that cross with the image we want to reconstruct.
In the first part of this document euclidean geometry is used to face the tasks of projec- tion and back-projection. After analysing the results achieved it has been proved that this approach does not lead to a fully perfect reconstruction (and also has some other problems related to running time and memory cost). Because of this in the second part of the document ’Filtered Back-projection’ method is introduced in order to improve the results.
Filtered Back-projection methods rely on mathematical transforms (Fourier, Radon) in order to provide more accurate results that can be obtained in much less time. The main cause of this better results is the use of a filtering process before the back-projection in order to avoid high frequency-caused errors.
As a result of this project two different implementations (one for each approach) had been implemented in order to compare their performance. |
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Address |
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Corporate Author |
Computer Vision Center |
Thesis |
Master's thesis |
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Place of Publication |
Barcelona, Spain |
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Area |
800 |
Expedition |
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Notes |
MV; |
Approved |
no |
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Call Number |
IAM @ iam @ Ber2009 |
Serial |
1693 |
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Author |
Joan M. Nuñez; Debora Gil; Fernando Vilariño |
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Title |
Finger joint characterization from X-ray images for rheymatoid arthritis assessment |
Type |
Conference Article |
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Year |
2013 |
Publication |
6th International Conference on Biomedical Electronics and Devices |
Abbreviated Journal |
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Issue |
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Pages |
288-292 |
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Keywords |
Rheumatoid Arthritis; X-Ray; Hand Joint; Sclerosis; Sharp Van der Heijde |
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Abstract |
In this study we propose amodular systemfor automatic rheumatoid arthritis assessment which provides a joint space width measure. A hand joint model is proposed based on the accurate analysis of a X-ray finger joint image sample set. This model shows that the sclerosis and the lower bone are the main necessary features in order to perform a proper finger joint characterization. We propose sclerosis and lower bone detection methods as well as the experimental setup necessary for its performance assessment. Our characterization is used to propose and compute a joint space width score which is shown to be related to the different degrees of arthritis. This assertion is verified by comparing our proposed score with Sharp Van der Heijde score, confirming that the lower our score is the more advanced is the patient affection. |
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Address |
Barcelona; February 2013 |
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Publisher |
SciTePress |
Place of Publication |
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Area |
800 |
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Conference |
BIODEVICES |
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Notes |
IAM;MV; 600.057; 600.054;SIAI |
Approved |
no |
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Call Number |
IAM @ iam @ NGV2013 |
Serial |
2196 |
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Author |
Fernando Vilariño; Ludmila I. Kuncheva; Petia Radeva |
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Title |
ROC curves and video analysis optimization in intestinal capsule endoscopy |
Type |
Journal Article |
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Year |
2006 |
Publication |
Pattern Recognition Letters |
Abbreviated Journal |
PRL |
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Volume |
27 |
Issue |
8 |
Pages |
875–881 |
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Keywords |
ROC curves; Classification; Classifiers ensemble; Detection of intestinal contractions; Imbalanced classes; Wireless capsule endoscopy |
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Abstract |
Wireless capsule endoscopy involves inspection of hours of video material by a highly qualified professional. Time episodes corresponding to intestinal contractions, which are of interest to the physician constitute about 1% of the video. The problem is to label automatically time episodes containing contractions so that only a fraction of the video needs inspection. As the classes of contraction and non-contraction images in the video are largely imbalanced, ROC curves are used to optimize the trade-off between false positive and false negative rates. Classifier ensemble methods and simple classifiers were examined. Our results reinforce the claims from recent literature that classifier ensemble methods specifically designed for imbalanced problems have substantial advantages over simple classifiers and standard classifier ensembles. By using ROC curves with the bagging ensemble method the inspection time can be drastically reduced at the expense of a small fraction of missed contractions. |
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Area |
800 |
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Notes |
MILAB;MV;SIAI |
Approved |
no |
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Call Number |
BCNPCL @ bcnpcl @ VKR2006; IAM @ iam @ VKR2006 |
Serial |
647 |
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Author |
Carles Sanchez; Debora Gil; Antoni Rosell; Albert Andaluz; F. Javier Sanchez |
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Title |
Segmentation of Tracheal Rings in Videobronchoscopy combining Geometry and Appearance |
Type |
Conference Article |
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Year |
2013 |
Publication |
Proceedings of the International Conference on Computer Vision Theory and Applications |
Abbreviated Journal |
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Volume |
1 |
Issue |
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Pages |
153--161 |
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Keywords |
Video-bronchoscopy, tracheal ring segmentation, trachea geometric and appearance model |
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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 |
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Address |
Barcelona; February 2013 |
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Publisher |
SciTePress |
Place of Publication |
Portugal |
Editor |
Sebastiano Battiato and José Braz |
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LNCS |
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Series Volume |
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Edition |
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ISSN |
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ISBN |
978-989-8565-47-1 |
Medium |
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Area |
800 |
Expedition |
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Conference |
VISAPP |
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Notes |
IAM;MV; 600.044; 600.047; 600.060; 605.203 |
Approved |
no |
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Call Number |
IAM @ iam @ SGR2013 |
Serial |
2123 |
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