|   | 
Details
   web
Records
Author Pau Riba; Alicia Fornes; Josep Llados
Title (up) Towards the Alignment of Handwritten Music Scores Type Book Chapter
Year 2017 Publication International Workshop on Graphics Recognition. GREC 2015.Graphic Recognition. Current Trends and Challenges Abbreviated Journal
Volume 9657 Issue Pages 103-116
Keywords Optical Music Recognition; Handwritten Music Scores; Dynamic Time Warping alignment
Abstract It is very common to nd di erent versions of the same music work in archives of Opera Theaters. These di erences correspond to modi cations and annotations from the musicians. From the musicologist point of view, these variations are very interesting and deserve study.
This paper explores the alignment of music scores as a tool for automatically detecting the passages that contain such di erences. Given the diculties in the recognition of handwritten music scores, our goal is to align the music scores and at the same time, avoid the recognition of music elements as much as possible. After removing the sta lines, braces and ties, the bar lines are detected. Then, the bar units are described as a whole using the Blurred Shape Model. The bar units alignment is performed by using Dynamic Time Warping. The analysis of the alignment path is used to detect the variations in the music scores. The method has been evaluated on a subset of the CVC-MUSCIMA dataset, showing encouraging results.
Address
Corporate Author Thesis
Publisher Place of Publication Editor Bart Lamiroy; R Dueire Lins
Language Summary Language Original Title
Series Editor Series Title Abbreviated Series Title LNCS
Series Volume Series Issue Edition
ISSN ISBN 978-3-319-52158-9 Medium
Area Expedition Conference
Notes DAG; 600.097; 602.006; 600.121 Approved no
Call Number Admin @ si @ RFL2017 Serial 2955
Permanent link to this record
 

 
Author Arnau Baro; Pau Riba; Alicia Fornes
Title (up) Towards the recognition of compound music notes in handwritten music scores Type Conference Article
Year 2016 Publication 15th international conference on Frontiers in Handwriting Recognition Abbreviated Journal
Volume Issue Pages
Keywords
Abstract The recognition of handwritten music scores still remains an open problem. The existing approaches can only deal with very simple handwritten scores mainly because of the variability in the handwriting style and the variability in the composition of groups of music notes (i.e. compound music notes). In this work we focus on this second problem and propose a method based on perceptual grouping for the recognition of compound music notes. Our method has been tested using several handwritten music scores of the CVC-MUSCIMA database and compared with a commercial Optical Music Recognition (OMR) software. Given that our method is learning-free, the obtained results are promising.
Address Shenzhen; China; October 2016
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 2167-6445 ISBN Medium
Area Expedition Conference ICFHR
Notes DAG; 600.097 Approved no
Call Number Admin @ si @ BRF2016 Serial 2903
Permanent link to this record
 

 
Author Estefania Talavera; Nicolai Petkov; Petia Radeva
Title (up) Towards Unsupervised Familiar Scene Recognition in Egocentric Videos Type Miscellaneous
Year 2019 Publication Arxiv Abbreviated Journal
Volume Issue Pages
Keywords
Abstract CoRR abs/1905.04093
Nowadays, there is an upsurge of interest in using lifelogging devices. Such devices generate huge amounts of image data; consequently, the need for automatic methods for analyzing and summarizing these data is drastically increasing. We present a new method for familiar scene recognition in egocentric videos, based on background pattern detection through automatically configurable COSFIRE filters. We present some experiments over egocentric data acquired with the Narrative Clip.
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 menciona Approved no
Call Number Admin @ si @ TPR2019b Serial 3379
Permanent link to this record
 

 
Author Bonifaz Stuhr
Title (up) Towards Unsupervised Representation Learning: Learning, Evaluating and Transferring Visual Representations Type Book Whole
Year 2023 Publication PhD Thesis, Universitat Autonoma de Barcelona-CVC Abbreviated Journal
Volume Issue Pages
Keywords
Abstract Unsupervised representation learning aims at finding methods that learn representations from data without annotation-based signals. Abstaining from annotations not only leads to economic benefits but may – and to some extent already does – result in advantages regarding the representation’s structure, robustness, and generalizability to different tasks. In the long run, unsupervised methods are expected to surpass their supervised counterparts due to the reduction of human intervention and the inherently more general setup that does not bias the optimization towards an objective originating from specific annotation-based signals. While major advantages of unsupervised representation learning have been recently observed in natural language processing, supervised methods still dominate in vision domains for most tasks. In this dissertation, we contribute to the field of unsupervised (visual) representation learning from three perspectives: (i) Learning representations: We design unsupervised, backpropagation-free Convolutional Self-Organizing Neural Networks (CSNNs) that utilize self-organization- and Hebbian-based learning rules to learn convolutional kernels and masks to achieve deeper backpropagation-free models. Thereby, we observe that backpropagation-based and -free methods can suffer from an objective function mismatch between the unsupervised pretext task and the target task. This mismatch can lead to performance decreases for the target task. (ii) Evaluating representations: We build upon the widely used (non-)linear evaluation protocol to define pretext- and target-objective-independent metrics for measuring the objective function mismatch. With these metrics, we evaluate various pretext and target tasks and disclose dependencies of the objective function mismatch concerning different parts of the training and model setup. (iii) Transferring representations: We contribute CARLANE, the first 3-way sim-to-real domain adaptation benchmark for 2D lane detection. We adopt several well-known unsupervised domain adaptation methods as baselines and propose a method based on prototypical cross-domain self-supervised learning. Finally, we focus on pixel-based unsupervised domain adaptation and contribute a content-consistent unpaired image-to-image translation method that utilizes masks, global and local discriminators, and similarity sampling to mitigate content inconsistencies, as well as feature-attentive denormalization to fuse content-based statistics into the generator stream. In addition, we propose the cKVD metric to incorporate class-specific content inconsistencies into perceptual metrics for measuring translation quality.
Address
Corporate Author Thesis Ph.D. thesis
Publisher IMPRIA Place of Publication Editor Jordi Gonzalez;Jurgen Brauer
Language Summary Language Original Title
Series Editor Series Title Abbreviated Series Title
Series Volume Series Issue Edition
ISSN ISBN 978-84-126409-6-0 Medium
Area Expedition Conference
Notes ISE Approved no
Call Number Admin @ si @ Stu2023 Serial 3966
Permanent link to this record
 

 
Author Pau Rodriguez; Jordi Gonzalez; Josep M. Gonfaus; Xavier Roca
Title (up) Towards Visual Personality Questionnaires based on Deep Learning and Social Media Type Conference Article
Year 2019 Publication 21st International Conference on Social Influence and Social Psychology Abbreviated Journal
Volume Issue Pages
Keywords
Abstract
Address April 2019; Tokio; Japan
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 ICSISP
Notes ISE; 600.119 Approved no
Call Number Admin @ si @ RGG2020 Serial 3554
Permanent link to this record
 

 
Author Carles Sanchez
Title (up) Tracheal ring detection in bronchoscopy Type Report
Year 2011 Publication CVC Technical Report Abbreviated Journal
Volume 168 Issue Pages
Keywords Bronchoscopy, tracheal ring, segmentation
Abstract Endoscopy is the process in which a camera is introduced inside a human.
Given that endoscopy provides realistic images (in contrast to other modalities) and allows non-invase minimal intervention procedures (which can aid in diagnosis and surgical interventions), its use has spreaded during last decades.
In this project we will focus on bronchoscopic procedures, during which the camera is introduced through the trachea in order to have a diagnostic of the patient. The diagnostic interventions are focused on: degree of stenosis (reduction in tracheal area), prosthesis or early diagnosis of tumors. In the first case, assessment of the luminal area and the calculation of the diameters of the tracheal rings are required. A main limitation is that all the process is done by hand,
which means that the doctor takes all the measurements and decisions just by looking at the screen. As far as we know there is no computational framework for helping the doctors in the diagnosis.
This project will consist of analysing bronchoscopic videos in order to extract useful information for the diagnostic of the degree of stenosis. In particular we will focus on segmentation of the tracheal rings. As a result of this project several strategies (for detecting tracheal rings) had been implemented in order to compare their performance.
Address
Corporate Author Thesis Master's thesis
Publisher Place of Publication Editor Debora Gil, F.Javier Sanchez
Language english Summary Language english Original Title
Series Editor Series Title Abbreviated Series Title
Series Volume Series Issue Edition
ISSN ISBN Medium
Area Expedition Conference
Notes IAM;MV Approved no
Call Number IAM @ iam @ San2011 Serial 1841
Permanent link to this record
 

 
Author Carles Sanchez
Title (up) Tracheal Structure Characterization using Geometric and Appearance Models for Efficient Assessment of Stenosis in Videobronchoscopy Type Book Whole
Year 2014 Publication PhD Thesis, Universitat Autonoma de Barcelona-CVC Abbreviated Journal
Volume Issue Pages
Keywords
Abstract Recent advances in endoscopic devices have increased their use for minimal invasive diagnostic and intervention procedures. Among all endoscopic modalities, bronchoscopy is one of the most frequent with around 261 millions of procedures per year. Although the use of bronchoscopy is spread among clinical facilities it presents some drawbacks, being the visual inspection for the assessment of anatomical measurements the most prevalent of them. In
particular, inaccuracies in the estimation of the degree of stenosis (the percentage of obstructed airway) decreases its diagnostic yield and might lead to erroneous treatments. An objective computation of tracheal stenosis in bronchoscopy videos would constitute a breakthrough for this non-invasive technique and a reduction in treatment cost.
This thesis settles the first steps towards on-line reliable extraction of anatomical information from videobronchoscopy for computation of objective measures. In particular, we focus on the computation of the degree of stenosis, which is obtained by comparing the area delimited by a healthy tracheal ring and the stenosed lumen. Reliable extraction of airway structures in interventional videobronchoscopy is a challenging task. This is mainly due to the large variety of acquisition conditions (positions and illumination), devices (different digitalizations) and in videos acquired at the operating room the unpredicted presence of surgical devices (such as probe ends). This thesis contributes to on-line stenosis assessment in several ways. We
propose a parametric strategy for the extraction of lumen and tracheal rings regions based on the characterization of their geometry and appearance that guide a deformable model. The geometric and appearance characterization is based on a physical model describing the way bronchoscopy images are obtained and includes local and global descriptions. In order to ensure a systematic applicability we present a statistical framework to select the optimal
parameters of our method. Experiments perform on the first public annotated database, show that the performance of our method is comparable to the one provided by clinicians and its computation time allows for a on-line implementation in the operating room.
Address
Corporate Author Thesis Ph.D. thesis
Publisher Ediciones Graficas Rey Place of Publication Editor F. Javier Sanchez;Debora Gil;Jorge Bernal
Language Summary Language Original Title
Series Editor Series Title Abbreviated Series Title
Series Volume Series Issue Edition
ISSN ISBN 978-84-940902-9-5 Medium
Area Expedition Conference
Notes IAM; 600.075 Approved no
Call Number Admin @ si @ San2014 Serial 2575
Permanent link to this record
 

 
Author Antonio Lopez; Joan Serrat
Title (up) Tracing crease curves by solving a system of differential equations. Type Miscellaneous
Year 1996 Publication Abbreviated Journal
Volume Issue Pages
Keywords
Abstract
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 Approved no
Call Number ADAS @ adas @ LoS1996 Serial 84
Permanent link to this record
 

 
Author Ricardo Toledo; X. Orriols; X. Binefa; Petia Radeva; Jordi Vitria; Juan J. Villanueva
Title (up) Tracking Elongated Structures using Statistical Snakes. Type Miscellaneous
Year 2000 Publication Computer Vision and Pattern Recognition CVPR´00, 1:157–162. Abbreviated Journal
Volume Issue Pages
Keywords
Abstract
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 OR;MILAB;ADAS;MV Approved no
Call Number BCNPCL @ bcnpcl @ TOB2000 Serial 339
Permanent link to this record
 

 
Author Jordi Gonzalez; Thomas B. Moeslund
Title (up) Tracking Humans for the Evaluation of their Motion in Image Sequences Type Book Whole
Year 2008 Publication First International Workshop, THEMIS Abbreviated Journal
Volume Issue Pages
Keywords
Abstract
Address Leeds (UK)
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 978-84-935251-9-4 Medium
Area Expedition Conference THEMIS
Notes Approved no
Call Number ISE @ ise @ GMW2008 Serial 1002
Permanent link to this record
 

 
Author Sergio Escalera; Oriol Pujol; Petia Radeva
Title (up) Traffic Sign Classification using Error Correcting Techniques Type Conference Article
Year 2007 Publication 2nd International Conference on Computer Vision Theory and Applications Abbreviated Journal
Volume Issue Pages 281–285
Keywords
Abstract
Address Barcelona (Spain)
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 VISAPP
Notes MILAB;HuPBA Approved no
Call Number BCNPCL @ bcnpcl @ EPR2007a Serial 909
Permanent link to this record
 

 
Author David Geronimo; Joan Serrat; Antonio Lopez; Ramon Baldrich
Title (up) Traffic sign recognition for computer vision project-based learning Type Journal Article
Year 2013 Publication IEEE Transactions on Education Abbreviated Journal T-EDUC
Volume 56 Issue 3 Pages 364-371
Keywords traffic signs
Abstract This paper presents a graduate course project on computer vision. The aim of the project is to detect and recognize traffic signs in video sequences recorded by an on-board vehicle camera. This is a demanding problem, given that traffic sign recognition is one of the most challenging problems for driving assistance systems. Equally, it is motivating for the students given that it is a real-life problem. Furthermore, it gives them the opportunity to appreciate the difficulty of real-world vision problems and to assess the extent to which this problem can be solved by modern computer vision and pattern classification techniques taught in the classroom. The learning objectives of the course are introduced, as are the constraints imposed on its design, such as the diversity of students' background and the amount of time they and their instructors dedicate to the course. The paper also describes the course contents, schedule, and how the project-based learning approach is applied. The outcomes of the course are discussed, including both the students' marks and their personal feedback.
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 0018-9359 ISBN Medium
Area Expedition Conference
Notes ADAS; CIC Approved no
Call Number Admin @ si @ GSL2013; ADAS @ adas @ Serial 2160
Permanent link to this record
 

 
Author Sergio Escalera; Oriol Pujol; Petia Radeva
Title (up) 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
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 Oriol Pujol; Petia Radeva; Jordi Vitria
Title (up) Traffic sign recognition using an adaptive boosting multiclass framework Type Miscellaneous
Year 2005 Publication Workshop sobre Reconocimiento de Formas y Analisis de Imagenes (AERFAI), I Congreso Español de Informatica (CEDI´2005) Abbreviated Journal
Volume Issue Pages
Keywords
Abstract
Address Granada (Spain)
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 OR;MILAB;HuPBA;MV Approved no
Call Number BCNPCL @ bcnpcl @ PRV2005 Serial 618
Permanent link to this record
 

 
Author Xavier Baro; Sergio Escalera; Jordi Vitria; Oriol Pujol; Petia Radeva
Title (up) 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
Keywords
Abstract 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.
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 1524-9050 ISBN Medium
Area Expedition Conference
Notes OR;MILAB;HuPBA;MV Approved no
Call Number BCNPCL @ bcnpcl @ BEV2008 Serial 1116
Permanent link to this record