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Author Jordi Gonzalez; Dani Rowe; Javier Varona; Xavier Roca edit  doi
openurl 
  Title Understanding Dynamic Scenes based on Human Sequence Evaluation Type Journal Article
  Year 2009 Publication Image and Vision Computing Abbreviated Journal IMAVIS  
  Volume 27 Issue 10 Pages 1433–1444  
  Keywords Image Sequence Evaluation; High-level processing of monitored scenes; Segmentation and tracking in complex scenes; Event recognition in dynamic scenes; Human motion understanding; Human behaviour interpretation; Natural-language text generation; Realistic demonstrators  
  Abstract In this paper, a Cognitive Vision System (CVS) is presented, which explains the human behaviour of monitored scenes using natural-language texts. This cognitive analysis of human movements recorded in image sequences is here referred to as Human Sequence Evaluation (HSE) which defines a set of transformation modules involved in the automatic generation of semantic descriptions from pixel values. In essence, the trajectories of human agents are obtained to generate textual interpretations of their motion, and also to infer the conceptual relationships of each agent w.r.t. its environment. For this purpose, a human behaviour model based on Situation Graph Trees (SGTs) is considered, which permits both bottom-up (hypothesis generation) and top-down (hypothesis refinement) analysis of dynamic scenes. The resulting system prototype interprets different kinds of behaviour and reports textual descriptions in multiple languages.  
  Address  
  Corporate Author Thesis  
  Publisher Place of Publication Editor  
  Language Summary Language Original Title  
  Series Editor Series Title Abbreviated Series Title (up)  
  Series Volume Series Issue Edition  
  ISSN ISBN Medium  
  Area Expedition Conference  
  Notes ISE Approved no  
  Call Number ISE @ ise @ GRV2009 Serial 1211  
Permanent link to this record
 
 
Author Fadi Dornaika; Bogdan Raducanu edit  doi
openurl 
  Title Three-Dimensional Face Pose Detection and Tracking Using Monocular Videos: Tool and Application Type Journal Article
  Year 2009 Publication IEEE Transactions on Systems, Man and Cybernetics part B Abbreviated Journal TSMCB  
  Volume 39 Issue 4 Pages 935–944  
  Keywords  
  Abstract 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.  
  Address  
  Corporate Author Thesis  
  Publisher Place of Publication Editor  
  Language Summary Language Original Title  
  Series Editor Series Title Abbreviated Series Title (up)  
  Series Volume Series Issue Edition  
  ISSN ISBN Medium  
  Area Expedition Conference  
  Notes OR;MV Approved no  
  Call Number BCNPCL @ bcnpcl @ DoR2009a Serial 1218  
Permanent link to this record
 
 
Author Oriol Ramos Terrades; Ernest Valveny; Salvatore Tabbone edit  doi
openurl 
  Title Optimal Classifier Fusion in a Non-Bayesian Probabilistic Framework Type Journal Article
  Year 2009 Publication IEEE Transactions on Pattern Analysis and Machine Intelligence Abbreviated Journal TPAMI  
  Volume 31 Issue 9 Pages 1630–1644  
  Keywords  
  Abstract 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.  
  Address  
  Corporate Author Thesis  
  Publisher Place of Publication Editor  
  Language Summary Language Original Title  
  Series Editor Series Title Abbreviated Series Title (up)  
  Series Volume Series Issue Edition  
  ISSN 0162-8828 ISBN Medium  
  Area Expedition Conference  
  Notes DAG Approved no  
  Call Number DAG @ dag @ RVT2009 Serial 1220  
Permanent link to this record
 
 
Author Carme Julia; Angel Sappa; Felipe Lumbreras; Joan Serrat; Antonio Lopez edit   pdf
doi  openurl
  Title Predicting Missing Ratings in Recommender Systems: Adapted Factorization Approach Type Journal Article
  Year 2009 Publication International Journal of Electronic Commerce Abbreviated Journal  
  Volume 14 Issue 1 Pages 89-108  
  Keywords  
  Abstract The paper presents a factorization-based approach to make predictions in recommender systems. These systems are widely used in electronic commerce to help customers find products according to their preferences. Taking into account the customer's ratings of some products available in the system, the recommender system tries to predict the ratings the customer would give to other products in the system. The proposed factorization-based approach uses all the information provided to compute the predicted ratings, in the same way as approaches based on Singular Value Decomposition (SVD). The main advantage of this technique versus SVD-based approaches is that it can deal with missing data. It also has a smaller computational cost. Experimental results with public data sets are provided to show that the proposed adapted factorization approach gives better predicted ratings than a widely used SVD-based approach.  
  Address  
  Corporate Author Thesis  
  Publisher Place of Publication Editor  
  Language Summary Language Original Title  
  Series Editor Series Title Abbreviated Series Title (up)  
  Series Volume Series Issue Edition  
  ISSN 1086-4415 ISBN Medium  
  Area Expedition Conference  
  Notes ADAS Approved no  
  Call Number ADAS @ adas @ JSL2009b Serial 1237  
Permanent link to this record
 
 
Author Arnau Ramisa; Adriana Tapus; David Aldavert; Ricardo Toledo; Ramon Lopez de Mantaras edit  doi
openurl 
  Title Robust Vision-Based Localization using Combinations of Local Feature Regions Detectors Type Journal Article
  Year 2009 Publication Autonomous Robots Abbreviated Journal AR  
  Volume 27 Issue 4 Pages 373-385  
  Keywords  
  Abstract This paper presents a vision-based approach for mobile robot localization. The model of the environment is topological. The new approach characterizes a place using a signature. This signature consists of a constellation of descriptors computed over different types of local affine covariant regions extracted from an omnidirectional image acquired rotating a standard camera with a pan-tilt unit. This type of representation permits a reliable and distinctive environment modelling. Our objectives were to validate the proposed method in indoor environments and, also, to find out if the combination of complementary local feature region detectors improves the localization versus using a single region detector. Our experimental results show that if false matches are effectively rejected, the combination of different covariant affine region detectors increases notably the performance of the approach by combining the different strengths of the individual detectors. In order to reduce the localization time, two strategies are evaluated: re-ranking the map nodes using a global similarity measure and using standard perspective view field of 45°.
In order to systematically test topological localization methods, another contribution proposed in this work is a novel method to see the degradation in localization performance as the robot moves away from the point where the original signature was acquired. This allows to know the robustness of the proposed signature. In order for this to be effective, it must be done in several, variated, environments that test all the possible situations in which the robot may have to perform localization.		  
  Address  
  Corporate Author Thesis  
  Publisher Place of Publication Editor  
  Language Summary Language Original Title  
  Series Editor Series Title Abbreviated Series Title (up)  
  Series Volume Series Issue Edition  
  ISSN 0929-5593 ISBN Medium  
  Area Expedition Conference  
  Notes ADAS Approved no  
  Call Number Admin @ si @ RTA2009 Serial 1245  
Permanent link to this record
 
 
Author Carlo Gatta; Oriol Pujol; Oriol Rodriguez-Leor; J. M. Ferre; Petia Radeva edit  doi
openurl 
  Title Fast Rigid Registration of Vascular Structures in IVUS Sequences Type Journal Article
  Year 2009 Publication IEEE Transactions on Information Technology in Biomedicine Abbreviated Journal  
  Volume 13 Issue 6 Pages 106-1011  
  Keywords  
  Abstract Intravascular ultrasound (IVUS) technology permits visualization of high-resolution images of internal vascular structures. IVUS is a unique image-guiding tool to display longitudinal view of the vessels, and estimate the length and size of vascular structures with the goal of accurate diagnosis. Unfortunately, due to pulsatile contraction and expansion of the heart, the captured images are affected by different motion artifacts that make visual inspection difficult. In this paper, we propose an efficient algorithm that aligns vascular structures and strongly reduces the saw-shaped oscillation, simplifying the inspection of longitudinal cuts; it reduces the motion artifacts caused by the displacement of the catheter in the short-axis plane and the catheter rotation due to vessel tortuosity. The algorithm prototype aligns 3.16 frames/s and clearly outperforms state-of-the-art methods with similar computational cost. The speed of the algorithm is crucial since it allows to inspect the corrected sequence during patient intervention. Moreover, we improved an indirect methodology for IVUS rigid registration algorithm evaluation.  
  Address  
  Corporate Author Thesis  
  Publisher Place of Publication Editor  
  Language Summary Language Original Title  
  Series Editor Series Title Abbreviated Series Title (up)  
  Series Volume Series Issue Edition  
  ISSN 1089-7771 ISBN Medium  
  Area Expedition Conference  
  Notes MILAB;HuPBA Approved no  
  Call Number BCNPCL @ bcnpcl @ GPL2009 Serial 1250  
Permanent link to this record
 
 
Author Fosca De Iorio; Carolina Malagelada; Fernando Azpiroz; M. Maluenda; C. Violanti; Laura Igual; Jordi Vitria; Juan R. Malagelada edit  doi
openurl 
  Title Intestinal motor activity, endoluminal motion and transit Type Journal Article
  Year 2009 Publication Neurogastroenterology & Motility Abbreviated Journal NEUMOT  
  Volume 21 Issue 12 Pages 1264–e119  
  Keywords  
  Abstract A programme for evaluation of intestinal motility has been recently developed based on endoluminal image analysis using computer vision methodology and machine learning techniques. Our aim was to determine the effect of intestinal muscle inhibition on wall motion, dynamics of luminal content and transit in the small bowel. Fourteen healthy subjects ingested the endoscopic capsule (Pillcam, Given Imaging) in fasting conditions. Seven of them received glucagon (4.8 microg kg(-1) bolus followed by a 9.6 microg kg(-1) h(-1) infusion during 1 h) and in the other seven, fasting activity was recorded, as controls. This dose of glucagon has previously shown to inhibit both tonic and phasic intestinal motor activity. Endoluminal image and displacement was analyzed by means of a computer vision programme specifically developed for the evaluation of muscular activity (contractile and non-contractile patterns), intestinal contents, endoluminal motion and transit. Thirty-minute periods before, during and after glucagon infusion were analyzed and compared with equivalent periods in controls. No differences were found in the parameters measured during the baseline (pretest) periods when comparing glucagon and control experiments. During glucagon infusion, there was a significant reduction in contractile activity (0.2 +/- 0.1 vs 4.2 +/- 0.9 luminal closures per min, P < 0.05; 0.4 +/- 0.1 vs 3.4 +/- 1.2% of images with radial wrinkles, P < 0.05) and a significant reduction of endoluminal motion (82 +/- 9 vs 21 +/- 10% of static images, P < 0.05). Endoluminal image analysis, by means of computer vision and machine learning techniques, can reliably detect reduced intestinal muscle activity and motion.  
  Address  
  Corporate Author Thesis  
  Publisher Place of Publication Editor  
  Language Summary Language Original Title  
  Series Editor Series Title Abbreviated Series Title (up)  
  Series Volume Series Issue Edition  
  ISSN ISBN Medium  
  Area Expedition Conference  
  Notes OR;MILAB;MV Approved no  
  Call Number BCNPCL @ bcnpcl @ DMA2009 Serial 1251  
Permanent link to this record
 
 
Author Oriol Pujol; David Masip edit  doi
openurl 
  Title Geometry-Based Ensembles: Toward a Structural Characterization of the Classification Boundary Type Journal Article
  Year 2009 Publication IEEE Transactions on Pattern Analysis and Machine Intelligence Abbreviated Journal TPAMI  
  Volume 31 Issue 6 Pages 1140–1146  
  Keywords  
  Abstract 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  
  Address  
  Corporate Author Thesis  
  Publisher Place of Publication Editor  
  Language Summary Language Original Title  
  Series Editor Series Title Abbreviated Series Title (up)  
  Series Volume Series Issue Edition  
  ISSN ISBN Medium  
  Area Expedition Conference  
  Notes OR;HuPBA;MV Approved no  
  Call Number BCNPCL @ bcnpcl @ PuM2009 Serial 1252  
Permanent link to this record
 
 
Author Sergio Escalera; Oriol Pujol; J. Mauri; Petia Radeva edit  doi
openurl 
  Title Intravascular Ultrasound Tissue Characterization with Sub-class Error-Correcting Output Codes Type Journal Article
  Year 2009 Publication Journal of Signal Processing Systems Abbreviated Journal  
  Volume 55 Issue 1-3 Pages 35–47  
  Keywords  
  Abstract Intravascular ultrasound (IVUS) represents a powerful imaging technique to explore coronary vessels and to study their morphology and histologic properties. In this paper, we characterize different tissues based on radial frequency, texture-based, and combined features. To deal with the classification of multiple tissues, we require the use of robust multi-class learning techniques. In this sense, error-correcting output codes (ECOC) show to robustly combine binary classifiers to solve multi-class problems. In this context, we propose a strategy to model multi-class classification tasks using sub-classes information in the ECOC framework. The new strategy splits the classes into different sub-sets according to the applied base classifier. Complex IVUS data sets containing overlapping data are learnt by splitting the original set of classes into sub-classes, and embedding the binary problems in a problem-dependent ECOC design. The method automatically characterizes different tissues, showing performance improvements over the state-of-the-art ECOC techniques for different base classifiers. Furthermore, the combination of RF and texture-based features also shows improvements over the state-of-the-art approaches.  
  Address  
  Corporate Author Thesis  
  Publisher Place of Publication Editor  
  Language Summary Language Original Title  
  Series Editor Series Title Abbreviated Series Title (up)  
  Series Volume Series Issue Edition  
  ISSN 1939-8018 ISBN Medium  
  Area Expedition Conference  
  Notes MILAB;HuPBA Approved no  
  Call Number BCNPCL @ bcnpcl @ EPM2009 Serial 1258  
Permanent link to this record
 
 
Author Arjan Gijsenij; Theo Gevers; Joost Van de Weijer edit  doi
openurl 
  Title Generalized Gamut Mapping using Image Derivative Structures for Color Constancy Type Journal Article
  Year 2010 Publication International Journal of Computer Vision Abbreviated Journal IJCV  
  Volume 86 Issue 2-3 Pages 127-139  
  Keywords  
  Abstract The gamut mapping algorithm is one of the most promising methods to achieve computational color constancy. However, so far, gamut mapping algorithms are restricted to the use of pixel values to estimate the illuminant. Therefore, in this paper, gamut mapping is extended to incorporate the statistical nature of images. It is analytically shown that the proposed gamut mapping framework is able to include any linear filter output. The main focus is on the local n-jet describing the derivative structure of an image. It is shown that derivatives have the advantage over pixel values to be invariant to disturbing effects (i.e. deviations of the diagonal model) such as saturated colors and diffuse light. Further, as the n-jet based gamut mapping has the ability to use more information than pixel values alone, the combination of these algorithms are more stable than the regular gamut mapping algorithm. Different methods of combining are proposed. Based on theoretical and experimental results conducted on large scale data sets of hyperspectral, laboratory and realworld scenes, it can be derived that (1) in case of deviations of the diagonal model, the derivative-based approach outperforms the pixel-based gamut mapping, (2) state-of-the-art algorithms are outperformed by the n-jet based gamut mapping, (3) the combination of the different n-jet based gamut  
  Address  
  Corporate Author Thesis  
  Publisher Kluwer Academic Publishers Hingham, MA, USA Place of Publication Editor  
  Language Summary Language Original Title  
  Series Editor Series Title Abbreviated Series Title (up)  
  Series Volume Series Issue Edition  
  ISSN 0920-5691 ISBN Medium  
  Area Expedition Conference  
  Notes ISE Approved no  
  Call Number CAT @ cat @ GGW2010 Serial 1274  
Permanent link to this record
 
 
Author Eduard Vazquez; Theo Gevers; M. Lucassen; Joost Van de Weijer; Ramon Baldrich edit  doi
openurl 
  Title Saliency of Color Image Derivatives: A Comparison between Computational Models and Human Perception Type Journal Article
  Year 2010 Publication Journal of the Optical Society of America A Abbreviated Journal JOSA A  
  Volume 27 Issue 3 Pages 613–621  
  Keywords  
  Abstract In this paper, computational methods are proposed to compute color edge saliency based on the information content of color edges. The computational methods are evaluated on bottom-up saliency in a psychophysical experiment, and on a more complex task of salient object detection in real-world images. The psychophysical experiment demonstrates the relevance of using information theory as a saliency processing model and that the proposed methods are significantly better in predicting color saliency (with a human-method correspondence up to 74.75% and an observer agreement of 86.8%) than state-of-the-art models. Furthermore, results from salient object detection confirm that an early fusion of color and contrast provide accurate performance to compute visual saliency with a hit rate up to 95.2%.  
  Address  
  Corporate Author Thesis  
  Publisher Place of Publication Editor  
  Language Summary Language Original Title  
  Series Editor Series Title Abbreviated Series Title (up)  
  Series Volume Series Issue Edition  
  ISSN ISBN Medium  
  Area Expedition Conference  
  Notes ISE;CIC Approved no  
  Call Number CAT @ cat @ VGL2010 Serial 1275  
Permanent link to this record
 
 
Author Sergio Escalera; Oriol Pujol; Petia Radeva edit  doi
openurl 
  Title Traffic sign recognition system with β -correction Type Journal Article
  Year 2010 Publication Machine Vision and Applications Abbreviated Journal MVA  
  Volume 21 Issue 2 Pages 99–111  
  Keywords  
  Abstract Traffic sign classification represents a classical application of multi-object recognition processing in uncontrolled adverse environments. Lack of visibility, illumination changes, and partial occlusions are just a few problems. In this paper, we introduce a novel system for multi-class classification of traffic signs based on error correcting output codes (ECOC). ECOC is based on an ensemble of binary classifiers that are trained on bi-partition of classes. We classify a wide set of traffic signs types using robust error correcting codings. Moreover, we introduce the novel β-correction decoding strategy that outperforms the state-of-the-art decoding techniques, classifying a high number of classes with great success.  
  Address  
  Corporate Author Thesis  
  Publisher Springer-Verlag Place of Publication Editor  
  Language Summary Language Original Title  
  Series Editor Series Title Abbreviated Series Title (up)  
  Series Volume Series Issue Edition  
  ISSN 0932-8092 ISBN Medium  
  Area Expedition Conference  
  Notes MILAB;HUPBA Approved no  
  Call Number BCNPCL @ bcnpcl @ EPR2010a Serial 1276  
Permanent link to this record
 
 
Author Sergio Escalera; Oriol Pujol; Petia Radeva edit  doi
openurl 
  Title On the Decoding Process in Ternary Error-Correcting Output Codes Type Journal Article
  Year 2010 Publication IEEE on Pattern Analysis and Machine Intelligence Abbreviated Journal TPAMI  
  Volume 32 Issue 1 Pages 120–134  
  Keywords  
  Abstract A common way to model multiclass classification problems is to design a set of binary classifiers and to combine them. Error-correcting output codes (ECOC) represent a successful framework to deal with these type of problems. Recent works in the ECOC framework showed significant performance improvements by means of new problem-dependent designs based on the ternary ECOC framework. The ternary framework contains a larger set of binary problems because of the use of a ldquodo not carerdquo symbol that allows us to ignore some classes by a given classifier. However, there are no proper studies that analyze the effect of the new symbol at the decoding step. In this paper, we present a taxonomy that embeds all binary and ternary ECOC decoding strategies into four groups. We show that the zero symbol introduces two kinds of biases that require redefinition of the decoding design. A new type of decoding measure is proposed, and two novel decoding strategies are defined. We evaluate the state-of-the-art coding and decoding strategies over a set of UCI machine learning repository data sets and into a real traffic sign categorization problem. The experimental results show that, following the new decoding strategies, the performance of the ECOC design is significantly improved.  
  Address  
  Corporate Author Thesis  
  Publisher Place of Publication Editor  
  Language Summary Language Original Title  
  Series Editor Series Title Abbreviated Series Title (up)  
  Series Volume Series Issue Edition  
  ISSN 0162-8828 ISBN Medium  
  Area Expedition Conference  
  Notes MILAB;HUPBA Approved no  
  Call Number BCNPCL @ bcnpcl @ EPR2010b Serial 1277  
Permanent link to this record
 
 
Author Carme Julia; Angel Sappa; Felipe Lumbreras; Joan Serrat; Antonio Lopez edit   pdf
doi  openurl
  Title An Iterative Multiresolution Scheme for SFM with Missing Data: single and multiple object scenes Type Journal Article
  Year 2010 Publication Image and Vision Computing Abbreviated Journal IMAVIS  
  Volume 28 Issue 1 Pages 164-176  
  Keywords  
  Abstract Most of the techniques proposed for tackling the Structure from Motion problem (SFM) cannot deal with high percentages of missing data in the matrix of trajectories. Furthermore, an additional problem should be faced up when working with multiple object scenes: the rank of the matrix of trajectories should be estimated. This paper presents an iterative multiresolution scheme for SFM with missing data to be used in both the single and multiple object cases. The proposed scheme aims at recovering missing entries in the original input matrix. The objective is to improve the results by applying a factorization technique to the partially or totally filled in matrix instead of to the original input one. Experimental results obtained with synthetic and real data sequences, containing single and multiple objects, are presented to show the viability of the proposed approach.  
  Address  
  Corporate Author Thesis  
  Publisher Place of Publication Editor  
  Language Summary Language Original Title  
  Series Editor Series Title Abbreviated Series Title (up)  
  Series Volume Series Issue Edition  
  ISSN 0262-8856 ISBN Medium  
  Area Expedition Conference  
  Notes ADAS Approved no  
  Call Number ADAS @ adas @ JSL2010 Serial 1278  
Permanent link to this record
 
 
Author Bogdan Raducanu; Jordi Vitria; Ales Leonardis edit  url
doi  openurl
  Title Online pattern recognition and machine learning techniques for computer-vision: Theory and applications Type Journal Article
  Year 2010 Publication Image and Vision Computing Abbreviated Journal IMAVIS  
  Volume 28 Issue 7 Pages 1063–1064  
  Keywords  
  Abstract (Editorial for the Special Issue on Online pattern recognition and machine learning techniques)
In real life, visual learning is supposed to be a continuous process. This paradigm has found its way also in artificial vision systems. There is an increasing trend in pattern recognition represented by online learning approaches, which aims at continuously updating the data representation when new information arrives. Starting with a minimal dataset, the initial knowledge is expanded by incorporating incoming instances, which may have not been previously available or foreseen at the system’s design stage. An interesting characteristic of this strategy is that the train and test phases take place simultaneously. Given the increasing interest in this subject, the aim of this special issue is to be a landmark event in the development of online learning techniques and their applications with the hope that it will capture the interest of a wider audience and will attract even more researchers. We received 19 contributions, of which 9 have been accepted for publication, after having been subjected to usual peer review process.		  
  Address  
  Corporate Author Thesis  
  Publisher Elsevier Place of Publication Editor  
  Language Summary Language Original Title  
  Series Editor Series Title Abbreviated Series Title (up)  
  Series Volume Series Issue Edition  
  ISSN 0262-8856 ISBN Medium  
  Area Expedition Conference  
  Notes OR;MV Approved no  
  Call Number BCNPCL @ bcnpcl @ RVL2010 Serial 1280  
Permanent link to this record
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