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Author Marta Diez-Ferrer; Debora Gil; Cristian Tebe; Carles Sanchez
Title Positive Airway Pressure to Enhance Computed Tomography Imaging for Airway Segmentation for Virtual Bronchoscopic Navigation Type Journal Article
Year 2018 Publication Respiration Abbreviated Journal RES
Volume 96 Issue 6 Pages 525-534
Keywords (down) Multidetector computed tomography; Bronchoscopy; Continuous positive airway pressure; Image enhancement; Virtual bronchoscopic navigation
Abstract Abstract
RATIONALE:
Virtual bronchoscopic navigation (VBN) guidance to peripheral pulmonary lesions is often limited by insufficient segmentation of the peripheral airways.

OBJECTIVES:
To test the effect of applying positive airway pressure (PAP) during CT acquisition to improve segmentation, particularly at end-expiration.

METHODS:
CT acquisitions in inspiration and expiration with 4 PAP protocols were recorded prospectively and compared to baseline inspiratory acquisitions in 20 patients. The 4 protocols explored differences between devices (flow vs. turbine), exposures (within seconds vs. 15-min) and pressure levels (10 vs. 14 cmH2O). Segmentation quality was evaluated with the number of airways and number of endpoints reached. A generalized mixed-effects model explored the estimated effect of each protocol.

MEASUREMENTS AND MAIN RESULTS:
Patient characteristics and lung function did not significantly differ between protocols. Compared to baseline inspiratory acquisitions, expiratory acquisitions after 15 min of 14 cmH2O PAP segmented 1.63-fold more airways (95% CI 1.07-2.48; p = 0.018) and reached 1.34-fold more endpoints (95% CI 1.08-1.66; p = 0.004). Inspiratory acquisitions performed immediately under 10 cmH2O PAP reached 1.20-fold (95% CI 1.09-1.33; p < 0.001) more endpoints; after 15 min the increase was 1.14-fold (95% CI 1.05-1.24; p < 0.001).

CONCLUSIONS:
CT acquisitions with PAP segment more airways and reach more endpoints than baseline inspiratory acquisitions. The improvement is particularly evident at end-expiration after 15 min of 14 cmH2O PAP. Further studies must confirm that the improvement increases diagnostic yield when using VBN to evaluate peripheral pulmonary lesions.
Address
Corporate Author Thesis
Publisher Place of Publication Editor
Language Summary Language Original Title
Series Editor Series Title Abbreviated Series Title
Series Volume Series Issue Edition
ISSN ISBN Medium
Area Expedition Conference
Notes IAM; 600.145 Approved no
Call Number Admin @ si @ DGT2018 Serial 3135
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Author Alejandro Gonzalez Alzate; David Vazquez; Antonio Lopez; Jaume Amores
Title On-Board Object Detection: Multicue, Multimodal, and Multiview Random Forest of Local Experts Type Journal Article
Year 2017 Publication IEEE Transactions on cybernetics Abbreviated Journal Cyber
Volume 47 Issue 11 Pages 3980 - 3990
Keywords (down) Multicue; multimodal; multiview; object detection
Abstract Despite recent significant advances, object detection continues to be an extremely challenging problem in real scenarios. In order to develop a detector that successfully operates under these conditions, it becomes critical to leverage upon multiple cues, multiple imaging modalities, and a strong multiview (MV) classifier that accounts for different object views and poses. In this paper, we provide an extensive evaluation that gives insight into how each of these aspects (multicue, multimodality, and strong MV classifier) affect accuracy both individually and when integrated together. In the multimodality component, we explore the fusion of RGB and depth maps obtained by high-definition light detection and ranging, a type of modality that is starting to receive increasing attention. As our analysis reveals, although all the aforementioned aspects significantly help in improving the accuracy, the fusion of visible spectrum and depth information allows to boost the accuracy by a much larger margin. The resulting detector not only ranks among the top best performers in the challenging KITTI benchmark, but it is built upon very simple blocks that are easy to implement and computationally efficient.
Address
Corporate Author Thesis
Publisher Place of Publication Editor
Language Summary Language Original Title
Series Editor Series Title Abbreviated Series Title
Series Volume Series Issue Edition
ISSN 2168-2267 ISBN Medium
Area Expedition Conference
Notes ADAS; 600.085; 600.082; 600.076; 600.118 Approved no
Call Number Admin @ si @ Serial 2810
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Author Mohammad Ali Bagheri; Qigang Gao; Sergio Escalera
Title Combining Local and Global Learners in the Pairwise Multiclass Classification Type Journal Article
Year 2015 Publication Pattern Analysis and Applications Abbreviated Journal PAA
Volume 18 Issue 4 Pages 845-860
Keywords (down) Multiclass classification; Pairwise approach; One-versus-one
Abstract Pairwise classification is a well-known class binarization technique that converts a multiclass problem into a number of two-class problems, one problem for each pair of classes. However, in the pairwise technique, nuisance votes of many irrelevant classifiers may result in a wrong class prediction. To overcome this problem, a simple, but efficient method is proposed and evaluated in this paper. The proposed method is based on excluding some classes and focusing on the most probable classes in the neighborhood space, named Local Crossing Off (LCO). This procedure is performed by employing a modified version of standard K-nearest neighbor and large margin nearest neighbor algorithms. The LCO method takes advantage of nearest neighbor classification algorithm because of its local learning behavior as well as the global behavior of powerful binary classifiers to discriminate between two classes. Combining these two properties in the proposed LCO technique will avoid the weaknesses of each method and will increase the efficiency of the whole classification system. On several benchmark datasets of varying size and difficulty, we found that the LCO approach leads to significant improvements using different base learners. The experimental results show that the proposed technique not only achieves better classification accuracy in comparison to other standard approaches, but also is computationally more efficient for tackling classification problems which have a relatively large number of target classes.
Address
Corporate Author Thesis
Publisher Springer London Place of Publication Editor
Language Summary Language Original Title
Series Editor Series Title Abbreviated Series Title
Series Volume Series Issue Edition
ISSN 1433-7541 ISBN Medium
Area Expedition Conference
Notes HuPBA;MILAB Approved no
Call Number Admin @ si @ BGE2014 Serial 2441
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Author Razieh Rastgoo; Kourosh Kiani; Sergio Escalera
Title Hand sign language recognition using multi-view hand skeleton Type Journal Article
Year 2020 Publication Expert Systems With Applications Abbreviated Journal ESWA
Volume 150 Issue Pages 113336
Keywords (down) Multi-view hand skeleton; Hand sign language recognition; 3DCNN; Hand pose estimation; RGB video; Hand action recognition
Abstract Hand sign language recognition from video is a challenging research area in computer vision, which performance is affected by hand occlusion, fast hand movement, illumination changes, or background complexity, just to mention a few. In recent years, deep learning approaches have achieved state-of-the-art results in the field, though previous challenges are not completely solved. In this work, we propose a novel deep learning-based pipeline architecture for efficient automatic hand sign language recognition using Single Shot Detector (SSD), 2D Convolutional Neural Network (2DCNN), 3D Convolutional Neural Network (3DCNN), and Long Short-Term Memory (LSTM) from RGB input videos. We use a CNN-based model which estimates the 3D hand keypoints from 2D input frames. After that, we connect these estimated keypoints to build the hand skeleton by using midpoint algorithm. In order to obtain a more discriminative representation of hands, we project 3D hand skeleton into three views surface images. We further employ the heatmap image of detected keypoints as input for refinement in a stacked fashion. We apply 3DCNNs on the stacked features of hand, including pixel level, multi-view hand skeleton, and heatmap features, to extract discriminant local spatio-temporal features from these stacked inputs. The outputs of the 3DCNNs are fused and fed to a LSTM to model long-term dynamics of hand sign gestures. Analyzing 2DCNN vs. 3DCNN using different number of stacked inputs into the network, we demonstrate that 3DCNN better capture spatio-temporal dynamics of hands. To the best of our knowledge, this is the first time that this multi-modal and multi-view set of hand skeleton features are applied for hand sign language recognition. Furthermore, we present a new large-scale hand sign language dataset, namely RKS-PERSIANSIGN, including 10′000 RGB videos of 100 Persian sign words. Evaluation results of the proposed model on three datasets, NYU, First-Person, and RKS-PERSIANSIGN, indicate that our model outperforms state-of-the-art models in hand sign language recognition, hand pose estimation, and hand action recognition.
Address
Corporate Author Thesis
Publisher Place of Publication Editor
Language Summary Language Original Title
Series Editor Series Title Abbreviated Series Title
Series Volume Series Issue Edition
ISSN ISBN Medium
Area Expedition Conference
Notes HuPBA; no proj Approved no
Call Number Admin @ si @ RKE2020a Serial 3411
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Author Eduardo Aguilar; Marc Bolaños; Petia Radeva
Title Regularized uncertainty-based multi-task learning model for food analysis Type Journal Article
Year 2019 Publication Journal of Visual Communication and Image Representation Abbreviated Journal JVCIR
Volume 60 Issue Pages 360-370
Keywords (down) Multi-task models; Uncertainty modeling; Convolutional neural networks; Food image analysis; Food recognition; Food group recognition; Ingredients recognition; Cuisine recognition
Abstract Food plays an important role in several aspects of our daily life. Several computer vision approaches have been proposed for tackling food analysis problems, but very little effort has been done in developing methodologies that could take profit of the existent correlation between tasks. In this paper, we propose a new multi-task model that is able to simultaneously predict different food-related tasks, e.g. dish, cuisine and food categories. Here, we extend the homoscedastic uncertainty modeling to allow single-label and multi-label classification and propose a regularization term, which jointly weighs the tasks as well as their correlations. Furthermore, we propose a new Multi-Attribute Food dataset and a new metric, Multi-Task Accuracy. We prove that using both our uncertainty-based loss and the class regularization term, we are able to improve the coherence of outputs between different tasks. Moreover, we outperform the use of task-specific models on classical measures like accuracy or .
Address
Corporate Author Thesis
Publisher Place of Publication Editor
Language Summary Language Original Title
Series Editor Series Title Abbreviated Series Title
Series Volume Series Issue Edition
ISSN ISBN Medium
Area Expedition Conference
Notes MILAB; no proj Approved no
Call Number Admin @ si @ ABR2019 Serial 3298
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Author Antonio Hernandez; Nadezhda Zlateva; Alexander Marinov; Miguel Reyes; Petia Radeva; Dimo Dimov; Sergio Escalera
Title Human Limb Segmentation in Depth Maps based on Spatio-Temporal Graph Cuts Optimization Type Journal Article
Year 2012 Publication Journal of Ambient Intelligence and Smart Environments Abbreviated Journal JAISE
Volume 4 Issue 6 Pages 535-546
Keywords (down) Multi-modal vision processing; Random Forest; Graph-cuts; multi-label segmentation; human body segmentation
Abstract We present a framework for object segmentation using depth maps based on Random Forest and Graph-cuts theory, and apply it to the segmentation of human limbs. First, from a set of random depth features, Random Forest is used to infer a set of label probabilities for each data sample. This vector of probabilities is used as unary term in α−β swap Graph-cuts algorithm. Moreover, depth values of spatio-temporal neighboring data points are used as boundary potentials. Results on a new multi-label human depth data set show high performance in terms of segmentation overlapping of the novel methodology compared to classical approaches.
Address
Corporate Author Thesis
Publisher Place of Publication Editor
Language Summary Language Original Title
Series Editor Series Title Abbreviated Series Title
Series Volume Series Issue Edition
ISSN 1876-1364 ISBN Medium
Area Expedition Conference
Notes MILAB;HuPBA Approved no
Call Number Admin @ si @ HZM2012a Serial 2006
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Author Albert Clapes; Miguel Reyes; Sergio Escalera
Title Multi-modal User Identification and Object Recognition Surveillance System Type Journal Article
Year 2013 Publication Pattern Recognition Letters Abbreviated Journal PRL
Volume 34 Issue 7 Pages 799-808
Keywords (down) Multi-modal RGB-Depth data analysis; User identification; Object recognition; Intelligent surveillance; Visual features; Statistical learning
Abstract We propose an automatic surveillance system for user identification and object recognition based on multi-modal RGB-Depth data analysis. We model a RGBD environment learning a pixel-based background Gaussian distribution. Then, user and object candidate regions are detected and recognized using robust statistical approaches. The system robustly recognizes users and updates the system in an online way, identifying and detecting new actors in the scene. Moreover, segmented objects are described, matched, recognized, and updated online using view-point 3D descriptions, being robust to partial occlusions and local 3D viewpoint rotations. Finally, the system saves the historic of user–object assignments, being specially useful for surveillance scenarios. The system has been evaluated on a novel data set containing different indoor/outdoor scenarios, objects, and users, showing accurate recognition and better performance than standard state-of-the-art approaches.
Address
Corporate Author Thesis
Publisher Elsevier Place of Publication Editor
Language Summary Language Original Title
Series Editor Series Title Abbreviated Series Title
Series Volume Series Issue Edition
ISSN ISBN Medium
Area Expedition Conference
Notes HUPBA; 600.046; 605.203;MILAB Approved no
Call Number Admin @ si @ CRE2013 Serial 2248
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Author Miguel Reyes; Albert Clapes; Jose Ramirez; Juan R Revilla; Sergio Escalera
Title Automatic Digital Biometry Analysis based on Depth Maps Type Journal Article
Year 2013 Publication Computers in Industry Abbreviated Journal COMPUTIND
Volume 64 Issue 9 Pages 1316-1325
Keywords (down) Multi-modal data fusion; Depth maps; Posture analysis; Anthropometric data; Musculo-skeletal disorders; Gesture analysis
Abstract World Health Organization estimates that 80% of the world population is affected by back-related disorders during his life. Current practices to analyze musculo-skeletal disorders (MSDs) are expensive, subjective, and invasive. In this work, we propose a tool for static body posture analysis and dynamic range of movement estimation of the skeleton joints based on 3D anthropometric information from multi-modal data. Given a set of keypoints, RGB and depth data are aligned, depth surface is reconstructed, keypoints are matched, and accurate measurements about posture and spinal curvature are computed. Given a set of joints, range of movement measurements is also obtained. Moreover, gesture recognition based on joint movements is performed to look for the correctness in the development of physical exercises. The system shows high precision and reliable measurements, being useful for posture reeducation purposes to prevent MSDs, as well as tracking the posture evolution of patients in rehabilitation treatments.
Address
Corporate Author Thesis
Publisher Elsevier Place of Publication Editor
Language Summary Language Original Title
Series Editor Series Title Abbreviated Series Title
Series Volume Series Issue Edition
ISSN ISBN Medium
Area Expedition Conference
Notes HuPBA;MILAB Approved no
Call Number Admin @ si @ RCR2013 Serial 2252
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Author Jaume Amores
Title Multiple Instance Classification: review, taxonomy and comparative study Type Journal Article
Year 2013 Publication Artificial Intelligence Abbreviated Journal AI
Volume 201 Issue Pages 81-105
Keywords (down) Multi-instance learning; Codebook; Bag-of-Words
Abstract Multiple Instance Learning (MIL) has become an important topic in the pattern recognition community, and many solutions to this problemhave been proposed until now. Despite this fact, there is a lack of comparative studies that shed light into the characteristics and behavior of the different methods. In this work we provide such an analysis focused on the classification task (i.e.,leaving out other learning tasks such as regression). In order to perform our study, we implemented
fourteen methods grouped into three different families. We analyze the performance of the approaches across a variety of well-known databases, and we also study their behavior in synthetic scenarios in order to highlight their characteristics. As a result of this analysis, we conclude that methods that extract global bag-level information show a clearly superior performance in general. In this sense, the analysis permits us to understand why some types of methods are more successful than others, and it permits us to establish guidelines in the design of new MIL
methods.
Address
Corporate Author Thesis
Publisher Elsevier Science Publishers Ltd. Essex, UK Place of Publication Editor
Language Summary Language Original Title
Series Editor Series Title Abbreviated Series Title
Series Volume Series Issue Edition
ISSN 0004-3702 ISBN Medium
Area Expedition Conference
Notes ADAS; 601.042; 600.057 Approved no
Call Number Admin @ si @ Amo2013 Serial 2273
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Author Jaume Amores
Title MILDE: multiple instance learning by discriminative embedding Type Journal Article
Year 2015 Publication Knowledge and Information Systems Abbreviated Journal KAIS
Volume 42 Issue 2 Pages 381-407
Keywords (down) Multi-instance learning; Codebook; Bag of words
Abstract While the objective of the standard supervised learning problem is to classify feature vectors, in the multiple instance learning problem, the objective is to classify bags, where each bag contains multiple feature vectors. This represents a generalization of the standard problem, and this generalization becomes necessary in many real applications such as drug activity prediction, content-based image retrieval, and others. While the existing paradigms are based on learning the discriminant information either at the instance level or at the bag level, we propose to incorporate both levels of information. This is done by defining a discriminative embedding of the original space based on the responses of cluster-adapted instance classifiers. Results clearly show the advantage of the proposed method over the state of the art, where we tested the performance through a variety of well-known databases that come from real problems, and we also included an analysis of the performance using synthetically generated data.
Address
Corporate Author Thesis
Publisher Springer London Place of Publication Editor
Language Summary Language Original Title
Series Editor Series Title Abbreviated Series Title
Series Volume Series Issue Edition
ISSN 0219-1377 ISBN Medium
Area Expedition Conference
Notes ADAS; 601.042; 600.057; 600.076 Approved no
Call Number Admin @ si @ Amo2015 Serial 2383
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Author L. Calvet; A. Ferrer; M. Gomes; A. Juan; David Masip
Title Combining Statistical Learning with Metaheuristics for the Multi-Depot Vehicle Routing Problem with Market Segmentation Type Journal Article
Year 2016 Publication Computers & Industrial Engineering Abbreviated Journal CIE
Volume 94 Issue Pages 93-104
Keywords (down) Multi-Depot Vehicle Routing Problem; market segmentation applications; hybrid algorithms; statistical learning
Abstract In real-life logistics and distribution activities it is usual to face situations in which the distribution of goods has to be made from multiple warehouses or depots to the nal customers. This problem is known as the Multi-Depot Vehicle Routing Problem (MDVRP), and it typically includes two sequential and correlated stages: (a) the assignment map of customers to depots, and (b) the corresponding design of the distribution routes. Most of the existing work in the literature has focused on minimizing distance-based distribution costs while satisfying a number of capacity constraints. However, no attention has been given so far to potential variations in demands due to the tness of the customerdepot mapping in the case of heterogeneous depots. In this paper, we consider this realistic version of the problem in which the depots are heterogeneous in terms of their commercial o er and customers show di erent willingness to consume depending on how well the assigned depot ts their preferences. Thus, we assume that di erent customer-depot assignment maps will lead to di erent customer-expenditure levels. As a consequence, market-segmentation strategiesneed to be considered in order to increase sales and total income while accounting for the distribution costs. To solve this extension of the MDVRP, we propose a hybrid approach that combines statistical learning techniques with a metaheuristic framework. First, a set of predictive models is generated from historical data. These statistical models allow estimating the demand of any customer depending on the assigned depot. Then, the estimated expenditure of each customer is included as part of an enriched objective function as a way to better guide the stochastic local search inside the metaheuristic framework. A set of computational experiments contribute to illustrate our approach and how the extended MDVRP considered here di ers in terms of the proposed solutions from the traditional one.
Address
Corporate Author Thesis
Publisher PERGAMON-ELSEVIER SCIENCE LTD Place of Publication Editor
Language Summary Language Original Title
Series Editor Series Title Abbreviated Series Title CIE
Series Volume Series Issue Edition
ISSN 0360-8352 ISBN Medium
Area Expedition Conference
Notes OR;MV; Approved no
Call Number Admin @ si @ CFG2016 Serial 2749
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Author Miguel Angel Bautista; Sergio Escalera; Xavier Baro; Petia Radeva; Jordi Vitria; Oriol Pujol
Title Minimal Design of Error-Correcting Output Codes Type Journal Article
Year 2011 Publication Pattern Recognition Letters Abbreviated Journal PRL
Volume 33 Issue 6 Pages 693-702
Keywords (down) Multi-class classification; Error-correcting output codes; Ensemble of classifiers
Abstract IF JCR CCIA 1.303 2009 54/103
The classification of large number of object categories is a challenging trend in the pattern recognition field. In literature, this is often addressed using an ensemble of classifiers. In this scope, the Error-correcting output codes framework has demonstrated to be a powerful tool for combining classifiers. However, most state-of-the-art ECOC approaches use a linear or exponential number of classifiers, making the discrimination of a large number of classes unfeasible. In this paper, we explore and propose a minimal design of ECOC in terms of the number of classifiers. Evolutionary computation is used for tuning the parameters of the classifiers and looking for the best minimal ECOC code configuration. The results over several public UCI datasets and different multi-class computer vision problems show that the proposed methodology obtains comparable (even better) results than state-of-the-art ECOC methodologies with far less number of dichotomizers.
Address
Corporate Author Thesis
Publisher Elsevier Place of Publication Editor
Language Summary Language Original Title
Series Editor Series Title Abbreviated Series Title
Series Volume Series Issue Edition
ISSN 0167-8655 ISBN Medium
Area Expedition Conference
Notes MILAB; OR;HuPBA;MV Approved no
Call Number Admin @ si @ BEB2011a Serial 1800
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Author Ivan Huerta; Ariel Amato; Xavier Roca; Jordi Gonzalez
Title Exploiting Multiple Cues in Motion Segmentation Based on Background Subtraction Type Journal Article
Year 2013 Publication Neurocomputing Abbreviated Journal NEUCOM
Volume 100 Issue Pages 183–196
Keywords (down) Motion segmentation; Shadow suppression; Colour segmentation; Edge segmentation; Ghost detection; Background subtraction
Abstract This paper presents a novel algorithm for mobile-object segmentation from static background scenes, which is both robust and accurate under most of the common problems found in motionsegmentation. In our first contribution, a case analysis of motionsegmentation errors is presented taking into account the inaccuracies associated with different cues, namely colour, edge and intensity. Our second contribution is an hybrid architecture which copes with the main issues observed in the case analysis by fusing the knowledge from the aforementioned three cues and a temporal difference algorithm. On one hand, we enhance the colour and edge models to solve not only global and local illumination changes (i.e. shadows and highlights) but also the camouflage in intensity. In addition, local information is also exploited to solve the camouflage in chroma. On the other hand, the intensity cue is applied when colour and edge cues are not available because their values are beyond the dynamic range. Additionally, temporal difference scheme is included to segment motion where those three cues cannot be reliably computed, for example in those background regions not visible during the training period. Lastly, our approach is extended for handling ghost detection. The proposed method obtains very accurate and robust motionsegmentation results in multiple indoor and outdoor scenarios, while outperforming the most-referred state-of-art approaches.
Address
Corporate Author Thesis
Publisher Elsevier Place of Publication Editor
Language Summary Language Original Title
Series Editor Series Title Abbreviated Series Title
Series Volume Series Issue Edition
ISSN ISBN Medium
Area Expedition Conference
Notes ISE Approved no
Call Number Admin @ si @ HAR2013 Serial 1808
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Author Angel Sappa; Cristhian A. Aguilera-Carrasco; Juan A. Carvajal Ayala; Miguel Oliveira; Dennis Romero; Boris X. Vintimilla; Ricardo Toledo
Title Monocular visual odometry: A cross-spectral image fusion based approach Type Journal Article
Year 2016 Publication Robotics and Autonomous Systems Abbreviated Journal RAS
Volume 85 Issue Pages 26-36
Keywords (down) Monocular visual odometry; LWIR-RGB cross-spectral imaging; Image fusion
Abstract This manuscript evaluates the usage of fused cross-spectral images in a monocular visual odometry approach. Fused images are obtained through a Discrete Wavelet Transform (DWT) scheme, where the best setup is empirically obtained by means of a mutual information based evaluation metric. The objective is to have a flexible scheme where fusion parameters are adapted according to the characteristics of the given images. Visual odometry is computed from the fused monocular images using an off the shelf approach. Experimental results using data sets obtained with two different platforms are presented. Additionally, comparison with a previous approach as well as with monocular-visible/infrared spectra are also provided showing the advantages of the proposed scheme.
Address
Corporate Author Thesis
Publisher Elsevier B.V. Place of Publication Editor
Language Summary Language Original Title
Series Editor Series Title Abbreviated Series Title
Series Volume Series Issue Edition
ISSN ISBN Medium
Area Expedition Conference
Notes ADAS;600.086; 600.076 Approved no
Call Number Admin @ si @SAC2016 Serial 2811
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Author Olivier Penacchio
Title Mixed Hodge Structures and Equivariant Sheaves on the Projective Plane Type Journal Article
Year 2011 Publication Mathematische Nachrichten Abbreviated Journal MN
Volume 284 Issue 4 Pages 526-542
Keywords (down) Mixed Hodge structures, equivariant sheaves, MSC (2010) Primary: 14C30, Secondary: 14F05, 14M25
Abstract We describe an equivalence of categories between the category of mixed Hodge structures and a category of equivariant vector bundles on a toric model of the complex projective plane which verify some semistability condition. We then apply this correspondence to define an invariant which generalizes the notion of R-split mixed Hodge structure and give calculations for the first group of cohomology of possibly non smooth or non-complete curves of genus 0 and 1. Finally, we describe some extension groups of mixed Hodge structures in terms of equivariant extensions of coherent sheaves. © 2011 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim
Address
Corporate Author Thesis
Publisher WILEY-VCH Verlag Place of Publication Editor R. Mennicken
Language Summary Language Original Title
Series Editor Series Title Abbreviated Series Title
Series Volume Series Issue Edition
ISSN 1522-2616 ISBN Medium
Area Expedition Conference
Notes CIC Approved no
Call Number Admin @ si @ Pen2011 Serial 1721
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