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| Records | |||||
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| Author | Idoia Ruiz; Lorenzo Porzi; Samuel Rota Bulo; Peter Kontschieder; Joan Serrat | ||||
| Title | Weakly Supervised Multi-Object Tracking and Segmentation | Type | Conference Article | ||
| Year | 2021 | Publication | IEEE Winter Conference on Applications of Computer Vision Workshops | Abbreviated Journal | |
| Volume | Issue | Pages  |
125-133 | ||
| Keywords | |||||
| Abstract | We introduce the problem of weakly supervised MultiObject Tracking and Segmentation, i.e. joint weakly supervised instance segmentation and multi-object tracking, in which we do not provide any kind of mask annotation.
To address it, we design a novel synergistic training strategy by taking advantage of multi-task learning, i.e. classification and tracking tasks guide the training of the unsupervised instance segmentation. For that purpose, we extract weak foreground localization information, provided by Grad-CAM heatmaps, to generate a partial ground truth to learn from. Additionally, RGB image level information is employed to refine the mask prediction at the edges of the objects. We evaluate our method on KITTI MOTS, the most representative benchmark for this task, reducing the performance gap on the MOTSP metric between the fully supervised and weakly supervised approach to just 12% and 12.7 % for cars and pedestrians, respectively. |
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| Address | Virtual; January 2021 | ||||
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| Publisher | Place of Publication | Editor | |||
| Language | Summary Language | Original Title | |||
| Series Editor | Series Title | Abbreviated Series Title | |||
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| ISSN | ISBN | Medium | |||
| Area | Expedition | Conference | WACVW | ||
| Notes | ADAS; 600.118; 600.124 | Approved | no | ||
| Call Number | Admin @ si @ RPR2021 | Serial | 3548 | ||
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| Author | Jaume Garcia; Debora Gil; Aura Hernandez-Sabate | ||||
| Title | Endowing Canonical Geometries to Cardiac Structures | Type | Book Chapter | ||
| Year | 2010 | Publication | Statistical Atlases And Computational Models Of The Heart | Abbreviated Journal | |
| Volume | 6364 | Issue | Pages |
124-133 | |
| Keywords | |||||
| Abstract | International conference on Cardiac electrophysiological simulation challenge
In this paper, we show that canonical (shape-based) geometries can be endowed to cardiac structures using tubular coordinates defined over their medial axis. We give an analytic formulation of these geometries by means of B-Splines. Since B-Splines present vector space structure PCA can be applied to their control points and statistical models relating boundaries and the interior of the anatomical structures can be derived. We demonstrate the applicability in two cardiac structures, the 3D Left Ventricular volume, and the 2D Left-Right ventricle set in 2D Short Axis view. |
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| Corporate Author | Thesis | ||||
| Publisher | Springer Berlin / Heidelberg | Place of Publication | Editor | Camara, O.; Pop, M.; Rhode, K.; Sermesant, M.; Smith, N.; Young, A. | |
| Language | Summary Language | Original Title | |||
| Series Editor | Series Title | Lecture Notes in Computer Science | Abbreviated Series Title | LNCS | |
| Series Volume | Series Issue | Edition | |||
| ISSN | ISBN | Medium | |||
| Area | Expedition | Conference | |||
| Notes | IAM | Approved | no | ||
| Call Number | IAM @ iam @ GGH2010b | Serial | 1515 | ||
| Permanent link to this record | |||||
| Author | Fadi Dornaika; Alireza Bosaghzadeh; Bogdan Raducanu | ||||
| Title | Efficient Graph Construction for Label Propagation based Multi-observation Face Recognition | Type | Conference Article | ||
| Year | 2013 | Publication | Human Behavior Understanding 4th International Workshop | Abbreviated Journal | |
| Volume | 8212 | Issue | Pages |
124-135 | |
| Keywords | |||||
| Abstract | Workshop on Human Behavior Understanding
Human-machine interaction is a hot topic nowadays in the communities of multimedia and computer vision. In this context, face recognition algorithms (used as primary cue for a person’s identity assessment) work well under controlled conditions but degrade significantly when tested in real-world environments. Recently, graph-based label propagation for multi-observation face recognition was proposed. However, the associated graphs were constructed in an ad-hoc manner (e.g., using the KNN graph) that cannot adapt optimally to the data. In this paper, we propose a novel approach for efficient and adaptive graph construction that can be used for multi-observation face recognition as well as for other recognition problems. Experimental results performed on Honda video face database, show a distinct advantage of the proposed method over the standard graph construction methods. |
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| Address | Barcelona | ||||
| Corporate Author | Thesis | ||||
| Publisher | Springer International Publishing | Place of Publication | Editor | ||
| Language | Summary Language | Original Title | |||
| Series Editor | Series Title | Abbreviated Series Title | |||
| Series Volume | Series Issue | Edition | |||
| ISSN | 0302-9743 | ISBN | 978-3-319-02713-5 | Medium | |
| Area | Expedition | Conference | HBU | ||
| Notes | OR;MV | Approved | no | ||
| Call Number | Admin @ si @ DBR2013 | Serial | 2315 | ||
| Permanent link to this record | |||||
| Author | Patricia Marquez;Debora Gil;Aura Hernandez-Sabate | ||||
| Title | A Complete Confidence Framework for Optical Flow | Type | Conference Article | ||
| Year | 2012 | Publication | 12th European Conference on Computer Vision – Workshops and Demonstrations | Abbreviated Journal | |
| Volume | 7584 | Issue | 2 | Pages |
124-133 |
| Keywords | Optical flow, confidence measures, sparsification plots, error prediction plots | ||||
| Abstract | Medial representations are powerful tools for describing and parameterizing the volumetric shape of anatomical structures. Existing methods show excellent results when applied to 2D objects, but their quality drops across dimensions. This paper contributes to the computation of medial manifolds in two aspects. First, we provide a standard scheme for the computation of medial manifolds that avoid degenerated medial axis segments; second, we introduce an energy based method which performs independently of the dimension. We evaluate quantitatively the performance of our method with respect to existing approaches, by applying them to synthetic shapes of known medial geometry. Finally, we show results on shape representation of multiple abdominal organs, exploring the use of medial manifolds for the representation of multi-organ relations. | ||||
| Address | |||||
| Corporate Author | Thesis | ||||
| Publisher | Springer-Verlag | Place of Publication | Florence, Italy, October 7-13, 2012 | Editor | Andrea Fusiello, Vittorio Murino ,Rita Cucchiara |
| Language | Summary Language | Original Title | |||
| Series Editor | Series Title | Abbreviated Series Title | LNCS | ||
| Series Volume | Series Issue | Edition | |||
| ISSN | ISBN | 978-3-642-33867-0 | Medium | ||
| Area | Expedition | Conference | ECCVW | ||
| Notes | IAM;ADAS; | Approved | no | ||
| Call Number | IAM @ iam @ MGH2012b | Serial | 1991 | ||
| Permanent link to this record | |||||
| Author | Katerine Diaz; Konstantia Georgouli; Anastasios Koidis; Jesus Martinez del Rincon | ||||
| Title | Incremental model learning for spectroscopy-based food analysis | Type | Journal Article | ||
| Year | 2017 | Publication | Chemometrics and Intelligent Laboratory Systems | Abbreviated Journal | CILS |
| Volume | 167 | Issue | Pages |
123-131 | |
| Keywords | Incremental model learning; IGDCV technique; Subspace based learning; IdentificationVegetable oils; FT-IR spectroscopy | ||||
| Abstract | In this paper we propose the use of incremental learning for creating and improving multivariate analysis models in the field of chemometrics of spectral data. As main advantages, our proposed incremental subspace-based learning allows creating models faster, progressively improving previously created models and sharing them between laboratories and institutions without requiring transferring or disclosing individual spectra samples. In particular, our approach allows to improve the generalization and adaptability of previously generated models with a few new spectral samples to be applicable to real-world situations. The potential of our approach is demonstrated using vegetable oil type identification based on spectroscopic data as case study. Results show how incremental models maintain the accuracy of batch learning methodologies while reducing their computational cost and handicaps. | ||||
| 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 | ADAS; 600.118 | Approved | no | ||
| Call Number | Admin @ si @ DGK2017 | Serial | 3002 | ||
| Permanent link to this record | |||||
| Author | Chris Bahnsen; David Vazquez; Antonio Lopez; Thomas B. Moeslund | ||||
| Title | Learning to Remove Rain in Traffic Surveillance by Using Synthetic Data | Type | Conference Article | ||
| Year | 2019 | Publication | 14th International Conference on Computer Vision Theory and Applications | Abbreviated Journal | |
| Volume | Issue | Pages |
123-130 | ||
| Keywords | Rain Removal; Traffic Surveillance; Image Denoising | ||||
| Abstract | Rainfall is a problem in automated traffic surveillance. Rain streaks occlude the road users and degrade the overall visibility which in turn decrease object detection performance. One way of alleviating this is by artificially removing the rain from the images. This requires knowledge of corresponding rainy and rain-free images. Such images are often produced by overlaying synthetic rain on top of rain-free images. However, this method fails to incorporate the fact that rain fall in the entire three-dimensional volume of the scene. To overcome this, we introduce training data from the SYNTHIA virtual world that models rain streaks in the entirety of a scene. We train a conditional Generative Adversarial Network for rain removal and apply it on traffic surveillance images from SYNTHIA and the AAU RainSnow datasets. To measure the applicability of the rain-removed images in a traffic surveillance context, we run the YOLOv2 object detection algorithm on the original and rain-removed frames. The results on SYNTHIA show an 8% increase in detection accuracy compared to the original rain image. Interestingly, we find that high PSNR or SSIM scores do not imply good object detection performance. | ||||
| Address | Praga; Czech Republic; February 2019 | ||||
| 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 | VISIGRAPP | ||
| Notes | ADAS; 600.118 | Approved | no | ||
| Call Number | Admin @ si @ BVL2019 | Serial | 3256 | ||
| Permanent link to this record | |||||
| Author | Jaume Garcia; Debora Gil; Francesc Carreras ; Sandra Pujades; R.Leta; Xavier Alomar; Guillem Pons-LLados | ||||
| Title | Un Model 3D del Ventricle Esquerre Integrant Anatomia i Funcionalitat | Type | Conference Article | ||
| Year | 2008 | Publication | XX Congrés de la Societat Catalana de Cardiologia, Actes del Congres | Abbreviated Journal | |
| Volume | Issue | Pages |
122 | ||
| Keywords | |||||
| Abstract | Els canvis en la dinàmica del Ventricle Esquerre (VE) reflecteixen la majoria de malalties cardiovasculars . Els avenços en imatge mèdica han impulsat la recerca en models i simulacions de la dinàmica 3D del VE . La majoria dels models existents sols consideren l’anatomia externa del VE i no permeten una avaluació de l’acoblament electromecànic . Donat que la mecànica d’un muscle depèn de la orientació de les seves fibres, un model realista hauria d’incloure la disposició espacial de la banda ventricular helicoidal (BVH) .
Proposem desenvolupar un model del VE adaptat a cada pacient que integri, per primer cop, l’anatomia de la banda ventricular, l’anatomia externa del VE i la seva funcionalitat, per a una millor determinació del patró d’activació electromecànica |
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| Address | |||||
| Corporate Author | Thesis | ||||
| Publisher | Place of Publication | Barcelona | Editor | ||
| Language | catalan | Summary Language | catalan | Original Title | |
| Series Editor | Series Title | Abbreviated Series Title | |||
| Series Volume | Series Issue | Edition | |||
| ISSN | ISBN | Medium | |||
| Area | Expedition | Conference | |||
| Notes | IAM | Approved | no | ||
| Call Number | IAM @ iam @ GGC2008c | Serial | 1504 | ||
| Permanent link to this record | |||||
| Author | Pau Riba; Anjan Dutta; Lutz Goldmann; Alicia Fornes; Oriol Ramos Terrades; Josep Llados | ||||
| Title | Table Detection in Invoice Documents by Graph Neural Networks | Type | Conference Article | ||
| Year | 2019 | Publication | 15th International Conference on Document Analysis and Recognition | Abbreviated Journal | |
| Volume | Issue | Pages |
122-127 | ||
| Keywords | |||||
| Abstract | Tabular structures in documents offer a complementary dimension to the raw textual data, representing logical or quantitative relationships among pieces of information. In digital mail room applications, where a large amount of
administrative documents must be processed with reasonable accuracy, the detection and interpretation of tables is crucial. Table recognition has gained interest in document image analysis, in particular in unconstrained formats (absence of rule lines, unknown information of rows and columns). In this work, we propose a graph-based approach for detecting tables in document images. Instead of using the raw content (recognized text), we make use of the location, context and content type, thus it is purely a structure perception approach, not dependent on the language and the quality of the text reading. Our framework makes use of Graph Neural Networks (GNNs) in order to describe the local repetitive structural information of tables in invoice documents. Our proposed model has been experimentally validated in two invoice datasets and achieved encouraging results. Additionally, due to the scarcity of benchmark datasets for this task, we have contributed to the community a novel dataset derived from the RVL-CDIP invoice data. It will be publicly released to facilitate future research. |
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| Address | Sydney; Australia; September 2019 | ||||
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| ISSN | ISBN | Medium | |||
| Area | Expedition | Conference | ICDAR | ||
| Notes | DAG; 600.140; 601.302; 602.167; 600.121; 600.141 | Approved | no | ||
| Call Number | Admin @ si @ RDG2019 | Serial | 3355 | ||
| Permanent link to this record | |||||
| Author | Kai Wang; Joost Van de Weijer; Luis Herranz | ||||
| Title | ACAE-REMIND for online continual learning with compressed feature replay | Type | Journal Article | ||
| Year | 2021 | Publication | Pattern Recognition Letters | Abbreviated Journal | PRL |
| Volume | 150 | Issue | Pages |
122-129 | |
| Keywords | online continual learning; autoencoders; vector quantization | ||||
| Abstract | Online continual learning aims to learn from a non-IID stream of data from a number of different tasks, where the learner is only allowed to consider data once. Methods are typically allowed to use a limited buffer to store some of the images in the stream. Recently, it was found that feature replay, where an intermediate layer representation of the image is stored (or generated) leads to superior results than image replay, while requiring less memory. Quantized exemplars can further reduce the memory usage. However, a drawback of these methods is that they use a fixed (or very intransigent) backbone network. This significantly limits the learning of representations that can discriminate between all tasks. To address this problem, we propose an auxiliary classifier auto-encoder (ACAE) module for feature replay at intermediate layers with high compression rates. The reduced memory footprint per image allows us to save more exemplars for replay. In our experiments, we conduct task-agnostic evaluation under online continual learning setting and get state-of-the-art performance on ImageNet-Subset, CIFAR100 and CIFAR10 dataset. | ||||
| Address | |||||
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| Notes | LAMP; 600.147; 601.379; 600.120; 600.141 | Approved | no | ||
| Call Number | Admin @ si @ WWH2021 | Serial | 3575 | ||
| Permanent link to this record | |||||
| Author | Mohammad Rouhani; Angel Sappa | ||||
| Title | A Novel Approach to Geometric Fitting of Implicit Quadrics | Type | Conference Article | ||
| Year | 2009 | Publication | 8th International Conference on Advanced Concepts for Intelligent Vision Systems | Abbreviated Journal | |
| Volume | 5807 | Issue | Pages |
121–132 | |
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| Abstract | This paper presents a novel approach for estimating the geometric distance from a given point to the corresponding implicit quadric curve/surface. The proposed estimation is based on the height of a tetrahedron, which is used as a coarse but reliable estimation of the real distance. The estimated distance is then used for finding the best set of quadric parameters, by means of the Levenberg-Marquardt algorithm, which is a common framework in other geometric fitting approaches. Comparisons of the proposed approach with previous ones are provided to show both improvements in CPU time as well as in the accuracy of the obtained results. | ||||
| Address | Bordeaux, France | ||||
| Corporate Author | Thesis | ||||
| Publisher | Springer Berlin Heidelberg | Place of Publication | Editor | ||
| Language | Summary Language | Original Title | |||
| Series Editor | Series Title | Abbreviated Series Title | LNCS | ||
| Series Volume | Series Issue | Edition | |||
| ISSN | 0302-9743 | ISBN | 978-3-642-04696-4 | Medium | |
| Area | Expedition | Conference | ACIVS | ||
| Notes | ADAS | Approved | no | ||
| Call Number | ADAS @ adas @ RoS2009 | Serial | 1194 | ||
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| Author | Marc Oliu; Ciprian Corneanu; Laszlo A. Jeni; Jeffrey F. Cohn; Takeo Kanade; Sergio Escalera | ||||
| Title | Continuous Supervised Descent Method for Facial Landmark Localisation | Type | Conference Article | ||
| Year | 2016 | Publication | 13th Asian Conference on Computer Vision | Abbreviated Journal | |
| Volume | 10112 | Issue | Pages |
121-135 | |
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| Abstract | Recent methods for facial landmark location perform well on close-to-frontal faces but have problems in generalising to large head rotations. In order to address this issue we propose a second order linear regression method that is both compact and robust against strong rotations. We provide a closed form solution, making the method fast to train. We test the method’s performance on two challenging datasets. The first has been intensely used by the community. The second has been specially generated from a well known 3D face dataset. It is considerably more challenging, including a high diversity of rotations and more samples than any other existing public dataset. The proposed method is compared against state-of-the-art approaches, including RCPR, CGPRT, LBF, CFSS, and GSDM. Results upon both datasets show that the proposed method offers state-of-the-art performance on near frontal view data, improves state-of-the-art methods on more challenging head rotation problems and keeps a compact model size. | ||||
| Address | Taipei; Taiwan; November 2016 | ||||
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| Series Editor | Series Title | Abbreviated Series Title | LNCS | ||
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| Area | Expedition | Conference | ACCV | ||
| Notes | HuPBA;MILAB; | Approved | no | ||
| Call Number | Admin @ si @ OCJ2016 | Serial | 2838 | ||
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| Author | Sergio Escalera; Oriol Pujol; Petia Radeva | ||||
| 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 |
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| 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. | ||||
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| 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 | Miguel Angel Bautista; Xavier Baro; Oriol Pujol; Petia Radeva; Jordi Vitria; Sergio Escalera | ||||
| Title | Compact Evolutive Design of Error-Correcting Output Codes | Type | Conference Article | ||
| Year | 2010 | Publication | Supervised and Unsupervised Ensemble Methods and their Applications in the European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases | Abbreviated Journal | |
| Volume | Issue | Pages |
119-128 | ||
| Keywords | Ensemble of Dichotomizers; Error-Correcting Output Codes; Evolutionary optimization | ||||
| Abstract | The classi cation of large number of object categories is a challenging trend in the Machine Learning eld. In literature, this is often addressed using an ensemble of classi ers. In this scope, the Error-Correcting Output Codes framework has demonstrated to be a powerful tool for the combination of classi ers. However, most of the state-of-the-art ECOC approaches use a linear or exponential number of classi ers, 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 classi ers. Evolutionary computation is used for tuning the parameters of the classi ers and looking for the best Minimal ECOC code con guration. The results over several public UCI data sets and a challenging multi-class Computer Vision problem show that the proposed methodology obtains comparable and even better results than state-of-the-art ECOC methodologies with far less number of dichotomizers. | ||||
| Address | Barcelona (Spain) | ||||
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| Area | Expedition | Conference | SUEMA | ||
| Notes | OR;MILAB;HUPBA;MV | Approved | no | ||
| Call Number | BCNPCL @ bcnpcl @ BBP2010 | Serial | 1363 | ||
| Permanent link to this record | |||||
| Author | Neus Salvatella; E Fernandez-Nofrerias; Francesco Ciompi; Oriol Rodriguez-Leor; H. Tizon; Xavier Carrillo; J. Mauri; Petia Radeva | ||||
| Title | Radial Artery Volume Changes After Administration Of Two Different Intra-arterial Drug Regimens. Assessment by Intravascular Ultrasound | Type | Journal Article | ||
| Year | 2010 | Publication | Journal of the American College of Cardiology | Abbreviated Journal | JACC |
| Volume | 56 | Issue | 13s1 | Pages |
B119 |
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| Notes | MILAB | Approved | no | ||
| Call Number | BCNPCL @ bcnpcl @ SFC2010b | Serial | 1364 | ||
| Permanent link to this record | |||||
| Author | Miguel Angel Bautista; Sergio Escalera; Xavier Baro; Oriol Pujol; Jordi Vitria; Petia Radeva | ||||
| Title | Compact Evolutive Design of Error-Correcting Output Codes. Supervised and Unsupervised Ensemble Methods and Applications | Type | Conference Article | ||
| Year | 2010 | Publication | European Conference on Machine Learning | Abbreviated Journal | |
| Volume | I | Issue | Pages |
119-128 | |
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| Area | Expedition | Conference | ECML | ||
| Notes | MILAB; OR;HUPBA;MV | Approved | no | ||
| Call Number | Admin @ si @ BEB2010 | Serial | 1775 | ||
| Permanent link to this record | |||||