Records |
Author |
Daniel Ponsa; Antonio Lopez |
Title |
Variance reduction techniques in particle-based visual contour Tracking |
Type |
Journal Article |
Year |
2009 |
Publication |
Pattern Recognition |
Abbreviated Journal |
PR |
Volume |
42 |
Issue |
11 |
Pages |
2372–2391 |
Keywords |
Contour tracking; Active shape models; Kalman filter; Particle filter; Importance sampling; Unscented particle filter; Rao-Blackwellization; Partitioned sampling |
Abstract |
This paper presents a comparative study of three different strategies to improve the performance of particle filters, in the context of visual contour tracking: the unscented particle filter, the Rao-Blackwellized particle filter, and the partitioned sampling technique. The tracking problem analyzed is the joint estimation of the global and local transformation of the outline of a given target, represented following the active shape model approach. The main contributions of the paper are the novel adaptations of the considered techniques on this generic problem, and the quantitative assessment of their performance in extensive experimental work done. |
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ADAS |
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no |
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ADAS @ adas @ PoL2009a |
Serial |
1168 |
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Author |
Miquel Ferrer; Ernest Valveny; F. Serratosa |
Title |
Median graph: A new exact algorithm using a distance based on the maximum common subgraph |
Type |
Journal Article |
Year |
2009 |
Publication |
Pattern Recognition Letters |
Abbreviated Journal |
PRL |
Volume |
30 |
Issue |
5 |
Pages |
579–588 |
Keywords |
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Abstract |
Median graphs have been presented as a useful tool for capturing the essential information of a set of graphs. Nevertheless, computation of optimal solutions is a very hard problem. In this work we present a new and more efficient optimal algorithm for the median graph computation. With the use of a particular cost function that permits the definition of the graph edit distance in terms of the maximum common subgraph, and a prediction function in the backtracking algorithm, we reduce the size of the search space, avoiding the evaluation of a great amount of states and still obtaining the exact median. We present a set of experiments comparing our new algorithm against the previous existing exact algorithm using synthetic data. In addition, we present the first application of the exact median graph computation to real data and we compare the results against an approximate algorithm based on genetic search. These experimental results show that our algorithm outperforms the previous existing exact algorithm and in addition show the potential applicability of the exact solutions to real problems. |
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Elsevier Science Inc. |
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0167-8655 |
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DAG |
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no |
Call Number |
DAG @ dag @ FVS2009a |
Serial |
1114 |
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Author |
Fadi Dornaika; Angel Sappa |
Title |
Instantaneous 3D motion from image derivatives using the Least Trimmed Square Regression |
Type |
Journal Article |
Year |
2009 |
Publication |
Pattern Recognition Letters |
Abbreviated Journal |
PRL |
Volume |
30 |
Issue |
5 |
Pages |
535–543 |
Keywords |
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Abstract |
This paper presents a new technique to the instantaneous 3D motion estimation. The main contributions are as follows. First, we show that the 3D camera or scene velocity can be retrieved from image derivatives only assuming that the scene contains a dominant plane. Second, we propose a new robust algorithm that simultaneously provides the Least Trimmed Square solution and the percentage of inliers-the non-contaminated data. Experiments on both synthetic and real image sequences demonstrated the effectiveness of the developed method. Those experiments show that the new robust approach can outperform classical robust schemes. |
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Elsevier Science Inc. |
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0167-8655 |
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ADAS |
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no |
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ADAS @ adas @ DoS2009a |
Serial |
1115 |
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Author |
Sergio Escalera; Oriol Pujol; Petia Radeva |
Title |
Separability of Ternary Codes for Sparse Designs of Error-Correcting Output Codes |
Type |
Journal Article |
Year |
2009 |
Publication |
Pattern Recognition Letters |
Abbreviated Journal |
PRL |
Volume |
30 |
Issue |
3 |
Pages |
285–297 |
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Abstract |
Error Correcting Output Codes (ECOC) represent a successful framework to deal with multi-class categorization problems based on combining binary classifiers. In this paper, we present a new formulation of the ternary ECOC distance and the error-correcting capabilities in the ternary ECOC framework. Based on the new measure, we stress on how to design coding matrices preventing codification ambiguity and propose a new Sparse Random coding matrix with ternary distance maximization. The results on the UCI Repository and in a real speed traffic categorization problem show that when the coding design satisfies the new ternary measures, significant performance improvement is obtained independently of the decoding strategy applied. |
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Notes |
MILAB;HuPBA |
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no |
Call Number |
BCNPCL @ bcnpcl @ EPR2009a |
Serial |
1153 |
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Author |
Sergio Escalera; Alicia Fornes; O. Pujol; Petia Radeva; Gemma Sanchez; Josep Llados |
Title |
Blurred Shape Model for Binary and Grey-level Symbol Recognition |
Type |
Journal Article |
Year |
2009 |
Publication |
Pattern Recognition Letters |
Abbreviated Journal |
PRL |
Volume |
30 |
Issue |
15 |
Pages |
1424–1433 |
Keywords |
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Abstract |
Many symbol recognition problems require the use of robust descriptors in order to obtain rich information of the data. However, the research of a good descriptor is still an open issue due to the high variability of symbols appearance. Rotation, partial occlusions, elastic deformations, intra-class and inter-class variations, or high variability among symbols due to different writing styles, are just a few problems. In this paper, we introduce a symbol shape description to deal with the changes in appearance that these types of symbols suffer. The shape of the symbol is aligned based on principal components to make the recognition invariant to rotation and reflection. Then, we present the Blurred Shape Model descriptor (BSM), where new features encode the probability of appearance of each pixel that outlines the symbols shape. Moreover, we include the new descriptor in a system to deal with multi-class symbol categorization problems. Adaboost is used to train the binary classifiers, learning the BSM features that better split symbol classes. Then, the binary problems are embedded in an Error-Correcting Output Codes framework (ECOC) to deal with the multi-class case. The methodology is evaluated on different synthetic and real data sets. State-of-the-art descriptors and classifiers are compared, showing the robustness and better performance of the present scheme to classify symbols with high variability of appearance. |
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Notes |
HuPBA; DAG; MILAB |
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no |
Call Number |
BCNPCL @ bcnpcl @ EFP2009a |
Serial |
1180 |
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Author |
S. Chanda; Umapada Pal; Oriol Ramos Terrades |
Title |
Word-Wise Thai and Roman Script Identification |
Type |
Journal |
Year |
2009 |
Publication |
ACM Transactions on Asian Language Information Processing |
Abbreviated Journal |
TALIP |
Volume |
8 |
Issue |
3 |
Pages |
1-21 |
Keywords |
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Abstract |
In some Thai documents, a single text line of a printed document page may contain words of both Thai and Roman scripts. For the Optical Character Recognition (OCR) of such a document page it is better to identify, at first, Thai and Roman script portions and then to use individual OCR systems of the respective scripts on these identified portions. In this article, an SVM-based method is proposed for identification of word-wise printed Roman and Thai scripts from a single line of a document page. Here, at first, the document is segmented into lines and then lines are segmented into character groups (words). In the proposed scheme, we identify the script of a character group combining different character features obtained from structural shape, profile behavior, component overlapping information, topological properties, and water reservoir concept, etc. Based on the experiment on 10,000 data (words) we obtained 99.62% script identification accuracy from the proposed scheme. |
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1530-0226 |
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DAG |
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no |
Call Number |
Admin @ si @ CPR2009f |
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1869 |
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Author |
Joost Van de Weijer; Cordelia Schmid; Jakob Verbeek; Diane Larlus |
Title |
Learning Color Names for Real-World Applications |
Type |
Journal Article |
Year |
2009 |
Publication |
IEEE Transaction in Image Processing |
Abbreviated Journal |
TIP |
Volume |
18 |
Issue |
7 |
Pages |
1512–1524 |
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Abstract |
Color names are required in real-world applications such as image retrieval and image annotation. Traditionally, they are learned from a collection of labelled color chips. These color chips are labelled with color names within a well-defined experimental setup by human test subjects. However naming colors in real-world images differs significantly from this experimental setting. In this paper, we investigate how color names learned from color chips compare to color names learned from real-world images. To avoid hand labelling real-world images with color names we use Google Image to collect a data set. Due to limitations of Google Image this data set contains a substantial quantity of wrongly labelled data. We propose several variants of the PLSA model to learn color names from this noisy data. Experimental results show that color names learned from real-world images significantly outperform color names learned from labelled color chips for both image retrieval and image annotation. |
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ISSN |
1057-7149 |
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no |
Call Number |
CAT @ cat @ WSV2009 |
Serial |
1195 |
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Author |
Xavier Baro; Sergio Escalera; Jordi Vitria; Oriol Pujol; Petia Radeva |
Title |
Traffic Sign Recognition Using Evolutionary Adaboost Detection and Forest-ECOC Classification |
Type |
Journal Article |
Year |
2009 |
Publication |
IEEE Transactions on Intelligent Transportation Systems |
Abbreviated Journal |
TITS |
Volume |
10 |
Issue |
1 |
Pages |
113–126 |
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The high variability of sign appearance in uncontrolled environments has made the detection and classification of road signs a challenging problem in computer vision. In this paper, we introduce a novel approach for the detection and classification of traffic signs. Detection is based on a boosted detectors cascade, trained with a novel evolutionary version of Adaboost, which allows the use of large feature spaces. Classification is defined as a multiclass categorization problem. A battery of classifiers is trained to split classes in an Error-Correcting Output Code (ECOC) framework. We propose an ECOC design through a forest of optimal tree structures that are embedded in the ECOC matrix. The novel system offers high performance and better accuracy than the state-of-the-art strategies and is potentially better in terms of noise, affine deformation, partial occlusions, and reduced illumination. |
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1524-9050 |
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Notes |
OR;MILAB;HuPBA;MV |
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no |
Call Number |
BCNPCL @ bcnpcl @ BEV2008 |
Serial |
1116 |
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Author |
Aura Hernandez-Sabate; Debora Gil;Eduard Fernandez-Nofrerias;Petia Radeva; Enric Marti |
Title |
Approaching Artery Rigid Dynamics in IVUS |
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Journal Article |
Year |
2009 |
Publication |
IEEE Transactions on Medical Imaging |
Abbreviated Journal |
TMI |
Volume |
28 |
Issue |
11 |
Pages |
1670-1680 |
Keywords |
Fourier analysis; intravascular ultrasound (IVUS) dynamics; longitudinal motion; quality measures; tissue deformation. |
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Tissue biomechanical properties (like strain and stress) are playing an increasing role in diagnosis and long-term treatment of intravascular coronary diseases. Their assessment strongly relies on estimation of vessel wall deformation. Since intravascular ultrasound (IVUS) sequences allow visualizing vessel morphology and reflect its dynamics, this technique represents a useful tool for evaluation of tissue mechanical properties. Image misalignment introduced by vessel-catheter motion is a major artifact for a proper tracking of tissue deformation. In this work, we focus on compensating and assessing IVUS rigid in-plane motion due to heart beating. Motion parameters are computed by considering both the vessel geometry and its appearance in the image. Continuum mechanics laws serve to introduce a novel score measuring motion reduction in in vivo sequences. Synthetic experiments validate the proposed score as measure of motion parameters accuracy; whereas results in in vivo pullbacks show the reliability of the presented methodologies in clinical cases. |
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0278-0062 |
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IAM; MILAB |
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no |
Call Number |
IAM @ iam @ HGF2009 |
Serial |
1545 |
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Author |
Oriol Ramos Terrades; Ernest Valveny; Salvatore Tabbone |
Title |
Optimal Classifier Fusion in a Non-Bayesian Probabilistic Framework |
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Journal Article |
Year |
2009 |
Publication |
IEEE Transactions on Pattern Analysis and Machine Intelligence |
Abbreviated Journal |
TPAMI |
Volume |
31 |
Issue |
9 |
Pages |
1630–1644 |
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The combination of the output of classifiers has been one of the strategies used to improve classification rates in general purpose classification systems. Some of the most common approaches can be explained using the Bayes' formula. In this paper, we tackle the problem of the combination of classifiers using a non-Bayesian probabilistic framework. This approach permits us to derive two linear combination rules that minimize misclassification rates under some constraints on the distribution of classifiers. In order to show the validity of this approach we have compared it with other popular combination rules from a theoretical viewpoint using a synthetic data set, and experimentally using two standard databases: the MNIST handwritten digit database and the GREC symbol database. Results on the synthetic data set show the validity of the theoretical approach. Indeed, results on real data show that the proposed methods outperform other common combination schemes. |
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0162-8828 |
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DAG |
Approved |
no |
Call Number |
DAG @ dag @ RVT2009 |
Serial |
1220 |
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Author |
Oriol Pujol; David Masip |
Title |
Geometry-Based Ensembles: Toward a Structural Characterization of the Classification Boundary |
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Journal Article |
Year |
2009 |
Publication |
IEEE Transactions on Pattern Analysis and Machine Intelligence |
Abbreviated Journal |
TPAMI |
Volume |
31 |
Issue |
6 |
Pages |
1140–1146 |
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This article introduces a novel binary discriminative learning technique based on the approximation of the non-linear decision boundary by a piece-wise linear smooth additive model. The decision border is geometrically defined by means of the characterizing boundary points – points that belong to the optimal boundary under a certain notion of robustness. Based on these points, a set of locally robust linear classifiers is defined and assembled by means of a Tikhonov regularized optimization procedure in an additive model to create a final lambda-smooth decision rule. As a result, a very simple and robust classifier with a strong geometrical meaning and non-linear behavior is obtained. The simplicity of the method allows its extension to cope with some of nowadays machine learning challenges, such as online learning, large scale learning or parallelization, with linear computational complexity. We validate our approach on the UCI database. Finally, we apply our technique in online and large scale scenarios, and in six real life computer vision and pattern recognition problems: gender recognition, intravascular ultrasound tissue classification, speed traffic sign detection, Chagas' disease severity detection, clef classification and action recognition using a 3D accelerometer data. The results are promising and this paper opens a line of research that deserves further attention |
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OR;HuPBA;MV |
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no |
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BCNPCL @ bcnpcl @ PuM2009 |
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1252 |
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Author |
David Masip; Agata Lapedriza; Jordi Vitria |
Title |
Boosted Online Learning for Face Recognition |
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Journal Article |
Year |
2009 |
Publication |
IEEE Transactions on Systems, Man and Cybernetics part B |
Abbreviated Journal |
TSMCB |
Volume |
39 |
Issue |
2 |
Pages |
530–538 |
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Face recognition applications commonly suffer from three main drawbacks: a reduced training set, information lying in high-dimensional subspaces, and the need to incorporate new people to recognize. In the recent literature, the extension of a face classifier in order to include new people in the model has been solved using online feature extraction techniques. The most successful approaches of those are the extensions of the principal component analysis or the linear discriminant analysis. In the current paper, a new online boosting algorithm is introduced: a face recognition method that extends a boosting-based classifier by adding new classes while avoiding the need of retraining the classifier each time a new person joins the system. The classifier is learned using the multitask learning principle where multiple verification tasks are trained together sharing the same feature space. The new classes are added taking advantage of the structure learned previously, being the addition of new classes not computationally demanding. The present proposal has been (experimentally) validated with two different facial data sets by comparing our approach with the current state-of-the-art techniques. The results show that the proposed online boosting algorithm fares better in terms of final accuracy. In addition, the global performance does not decrease drastically even when the number of classes of the base problem is multiplied by eight. |
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1083–4419 |
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OR;MV |
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BCNPCL @ bcnpcl @ MLV2009 |
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1155 |
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Author |
Fadi Dornaika; Bogdan Raducanu |
Title |
Three-Dimensional Face Pose Detection and Tracking Using Monocular Videos: Tool and Application |
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Journal Article |
Year |
2009 |
Publication |
IEEE Transactions on Systems, Man and Cybernetics part B |
Abbreviated Journal |
TSMCB |
Volume |
39 |
Issue |
4 |
Pages |
935–944 |
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Recently, we have proposed a real-time tracker that simultaneously tracks the 3-D head pose and facial actions in monocular video sequences that can be provided by low quality cameras. This paper has two main contributions. First, we propose an automatic 3-D face pose initialization scheme for the real-time tracker by adopting a 2-D face detector and an eigenface system. Second, we use the proposed methods-the initialization and tracking-for enhancing the human-machine interaction functionality of an AIBO robot. More precisely, we show how the orientation of the robot's camera (or any active vision system) can be controlled through the estimation of the user's head pose. Applications based on head-pose imitation such as telepresence, virtual reality, and video games can directly exploit the proposed techniques. Experiments on real videos confirm the robustness and usefulness of the proposed methods. |
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OR;MV |
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BCNPCL @ bcnpcl @ DoR2009a |
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1218 |
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Author |
Petia Radeva; Jordi Vitria; Fernando Vilariño; Panagiota Spyridonos; Fernando Azpiroz; Juan Malagelada; Fosca de Iorio; Anna Accarino |
Title |
Cascade analysis for intestinal contraction detection |
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Patent |
Year |
2009 |
Publication |
US 2009/0284589 A1 |
Abbreviated Journal |
USPO |
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1-25 |
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A method and system cascade analysisi for intestinal contraction detection is provided by extracting from image frames captured in-vivo. The method and system also relate to the detection of turbid liquids in intestinal tracts, to automatic detection of video image frames taken in the gastrointestinal tract including a field of view obstructed by turbid media, and more particulary, to extraction of image data obstructed by turbid media. |
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US Patent Office |
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Publisher |
US Patent Office |
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MILAB; OR; MV;SIAI |
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no |
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IAM @ iam @ RVV2009 |
Serial |
1700 |
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