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Author  |
Pau Riba; Alicia Fornes; Josep Llados | ||||
| Title | Towards the Alignment of Handwritten Music Scores | Type | Conference Article | ||
| Year | 2015 | Publication | 11th IAPR International Workshop on Graphics Recognition | Abbreviated Journal | |
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| Abstract | It is very common to find different versions of the same music work in archives of Opera Theaters. These differences correspond to modifications 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 differences. Given the difficulties 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 staff 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 | Nancy; France; August 2015 | ||||
| Corporate Author | Thesis | ||||
| Publisher | Springer International Publishing | Place of Publication | Editor | Bart Lamiroy; Rafael 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 | GREC | ||
| Notes | DAG | Approved | no | ||
| Call Number | Admin @ si @ | Serial | 2874 | ||
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| Author |
Pau Riba; Alicia Fornes; Josep Llados | ||||
| Title | 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. |
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| 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 | ||
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| Author |
Pau Riba; Adria Molina; Lluis Gomez; Oriol Ramos Terrades; Josep Llados | ||||
| Title | Learning to Rank Words: Optimizing Ranking Metrics for Word Spotting | Type | Conference Article | ||
| Year | 2021 | Publication | 16th International Conference on Document Analysis and Recognition | Abbreviated Journal | |
| Volume | 12822 | Issue | Pages | 381–395 | |
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| Abstract | In this paper, we explore and evaluate the use of ranking-based objective functions for learning simultaneously a word string and a word image encoder. We consider retrieval frameworks in which the user expects a retrieval list ranked according to a defined relevance score. In the context of a word spotting problem, the relevance score has been set according to the string edit distance from the query string. We experimentally demonstrate the competitive performance of the proposed model on query-by-string word spotting for both, handwritten and real scene word images. We also provide the results for query-by-example word spotting, although it is not the main focus of this work. | ||||
| Address | Lausanne; Suissa; September 2021 | ||||
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| Area | Expedition | Conference | ICDAR | ||
| Notes | DAG; 600.121; 600.140; 110.312 | Approved | no | ||
| Call Number | Admin @ si @ RMG2021 | Serial | 3572 | ||
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| Author |
Pau Riba | ||||
| Title | Distilling Structure from Imagery: Graph-based Models for the Interpretation of Document Images | Type | Book Whole | ||
| Year | 2020 | Publication | PhD Thesis, Universitat Autonoma de Barcelona-CVC | Abbreviated Journal | |
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| Abstract | From its early stages, the community of Pattern Recognition and Computer Vision has considered the importance of leveraging the structural information when understanding images. Usually, graphs have been proposed as a suitable model to represent this kind of information due to their flexibility and representational power able to codify both, the components, objects, or entities and their pairwise relationship. Even though graphs have been successfully applied to a huge variety of tasks, as a result of their symbolic and relational nature, graphs have always suffered from some limitations compared to statistical approaches. Indeed, some trivial mathematical operations do not have an equivalence in the graph domain. For instance, in the core of many pattern recognition applications, there is a need to compare two objects. This operation, which is trivial when considering feature vectors defined in \(\mathbb{R}^n\), is not properly defined for graphs.
In this thesis, we have investigated the importance of the structural information from two perspectives, the traditional graph-based methods and the new advances on Geometric Deep Learning. On the one hand, we explore the problem of defining a graph representation and how to deal with it on a large scale and noisy scenario. On the other hand, Graph Neural Networks are proposed to first redefine a Graph Edit Distance methodologies as a metric learning problem, and second, to apply them in a real use case scenario for the detection of repetitive patterns which define tables in invoice documents. As experimental framework, we have validated the different methodological contributions in the domain of Document Image Analysis and Recognition. |
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| Corporate Author | Thesis | Ph.D. thesis | |||
| Publisher | Ediciones Graficas Rey | Place of Publication | Editor | Josep Llados;Alicia Fornes | |
| Language | Summary Language | Original Title | |||
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| Series Volume | Series Issue | Edition | |||
| ISSN | ISBN | 978-84-121011-6-4 | Medium | ||
| Area | Expedition | Conference | |||
| Notes | DAG; 600.121 | Approved | no | ||
| Call Number | Admin @ si @ Rib20 | Serial | 3478 | ||
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| Author |
Pau Cano; Debora Gil; Eva Musulen | ||||
| Title | Towards automatic detection of helicobacter pylori in histological samples of gastric tissue | Type | Conference Article | ||
| Year | 2023 | Publication | IEEE International Symposium on Biomedical Imaging | Abbreviated Journal | |
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| Address | Cartagena de Indias; Colombia; April 2023 | ||||
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| Area | Expedition | Conference | ISBI | ||
| Notes | IAM | Approved | no | ||
| Call Number | Admin @ si @ CGM2023 | Serial | 3953 | ||
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| Author |
Pau Cano; Alvaro Caravaca; Debora Gil; Eva Musulen | ||||
| Title | Diagnosis of Helicobacter pylori using AutoEncoders for the Detection of Anomalous Staining Patterns in Immunohistochemistry Images | Type | Miscellaneous | ||
| Year | 2023 | Publication | Arxiv | Abbreviated Journal | |
| Volume | Issue | Pages | 107241 | ||
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| Abstract | This work addresses the detection of Helicobacter pylori a bacterium classified since 1994 as class 1 carcinogen to humans. By its highest specificity and sensitivity, the preferred diagnosis technique is the analysis of histological images with immunohistochemical staining, a process in which certain stained antibodies bind to antigens of the biological element of interest. This analysis is a time demanding task, which is currently done by an expert pathologist that visually inspects the digitized samples.
We propose to use autoencoders to learn latent patterns of healthy tissue and detect H. pylori as an anomaly in image staining. Unlike existing classification approaches, an autoencoder is able to learn patterns in an unsupervised manner (without the need of image annotations) with high performance. In particular, our model has an overall 91% of accuracy with 86\% sensitivity, 96% specificity and 0.97 AUC in the detection of H. pylori. |
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| Notes | IAM | Approved | no | ||
| Call Number | Admin @ si @ CCG2023 | Serial | 3855 | ||
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| Author |
Pau Baiget; Xavier Roca; Jordi Gonzalez | ||||
| Title | Autonomous Virtual Agents for Performance Evaluation of Tracking Algorithms | Type | Book Chapter | ||
| Year | 2008 | Publication | Articulated Motion and Deformable Objects, 5th International Conference AMDO 2008, | Abbreviated Journal | |
| Volume | 5098 | Issue | Pages | 299-308 | |
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| Address | Port d'Andratx (Mallorca) | ||||
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| Language | Summary Language | Original Title | |||
| Series Editor | Series Title | Abbreviated Series Title | LNCS | ||
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| Notes | ISE | Approved | no | ||
| Call Number | ISE @ ise @ BRG2008 | Serial | 974 | ||
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| Author |
Pau Baiget; Joan Soto; Xavier Roca; Jordi Gonzalez | ||||
| Title | Automatic Generation of Computer-Animated Sequences based on Human Behaviour Modelling | Type | Conference Article | ||
| Year | 2007 | Publication | 10th International Conference on Computer Graphics and Artificial Intelligence | Abbreviated Journal | |
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| Address | Athens (Greece) | ||||
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| Area | Expedition | Conference | 3IA | ||
| Notes | ISE | Approved | no | ||
| Call Number | ISE @ ise @ BSR2007 | Serial | 808 | ||
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| Author |
Pau Baiget; Eric Sommerlade; I. Reid; Jordi Gonzalez | ||||
| Title | Finding Prototypes to Estimate Trajectory Development in Outdoor Scenarios | Type | Conference Article | ||
| Year | 2008 | Publication | First International Workshop on Tracking Humans for the Evaluation of their Motion in Image Sequences BMVC 2008, | Abbreviated Journal | |
| Volume | Issue | Pages | 27–34 | ||
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| Address | Leed | ||||
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| ISSN | ISBN | 978-84-935251-9-4 | Medium | ||
| Area | Expedition | Conference | THEMIS’ | ||
| Notes | ISE | Approved | no | ||
| Call Number | ISE @ ise @ BSR2008 | Serial | 1008 | ||
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| Author |
Pau Baiget; Carles Fernandez; Xavier Roca; Jordi Gonzalez | ||||
| Title | Automatic Learning of Conceptual Knowledge for the Interpretation of Human Behavior in Video Sequences | Type | Book Chapter | ||
| Year | 2007 | Publication | 3rd Iberian Conference on Pattern Recognition and Image Analysis (IbPRIA 2007), J. Marti et al. (Eds.) LNCS 4477:507–514 | Abbreviated Journal | |
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| Address | Girona (Spain) | ||||
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| Notes | ISE | Approved | no | ||
| Call Number | ISE @ ise @ BFR2007 | Serial | 807 | ||
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| Author |
Pau Baiget; Carles Fernandez; Xavier Roca; Jordi Gonzalez | ||||
| Title | Generation of Augmented Video Sequences Combining Behavioral Animation and Multi Object Tracking | Type | Journal Article | ||
| Year | 2009 | Publication | Computer Animation and Virtual Worlds | Abbreviated Journal | |
| Volume | 20 | Issue | 4 | Pages | 473–489 |
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| Abstract | In this paper we present a novel approach to generate augmented video sequences in real-time, involving interactions between virtual and real agents in real scenarios. On the one hand, real agent motion is estimated by means of a multi-object tracking algorithm, which determines real objects' position over the scenario for each time step. On the other hand, virtual agents are provided with behavior models considering their interaction with the environment and with other agents. The resulting framework allows to generate video sequences involving behavior-based virtual agents that react to real agent behavior and has applications in education, simulation, and in the game and movie industries. We show the performance of the proposed approach in an indoor and outdoor scenario simulating human and vehicle agents. Copyright © 2009 John Wiley & Sons, Ltd.
We present a novel approach to generate augmented video sequences in real-time, involving interactions between virtual and real agents in real scenarios. On the one hand, real agent motion is estimated by means of a multi-object tracking algorithm, which determines real objects' position over the scenario for each time step. On the other hand, virtual agents are provided with behavior models considering their interaction with the environment and with other agents. © 2009 Wiley Periodicals, Inc. |
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| Notes | ISE | Approved | no | ||
| Call Number | ISE @ ise @ BFR2009 | Serial | 1170 | ||
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| Author |
Pau Baiget; Carles Fernandez; Xavier Roca; Jordi Gonzalez | ||||
| Title | Trajectory-Based Abnormality Categorization for Learning Route Patterns in Surveillance | Type | Book Chapter | ||
| Year | 2012 | Publication | Detection and Identification of Rare Audiovisual Cues, Studies in Computational Intelligence | Abbreviated Journal | |
| Volume | 384 | Issue | 3 | Pages | 87-95 |
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| Abstract | The recognition of abnormal behaviors in video sequences has raised as a hot topic in video understanding research. Particularly, an important challenge resides on automatically detecting abnormality. However, there is no convention about the types of anomalies that training data should derive. In surveillance, these are typically detected when new observations differ substantially from observed, previously learned behavior models, which represent normality. This paper focuses on properly defining anomalies within trajectory analysis: we propose a hierarchical representation conformed by Soft, Intermediate, and Hard Anomaly, which are identified from the extent and nature of deviation from learned models. Towards this end, a novel Gaussian Mixture Model representation of learned route patterns creates a probabilistic map of the image plane, which is applied to detect and classify anomalies in real-time. Our method overcomes limitations of similar existing approaches, and performs correctly even when the tracking is affected by different sources of noise. The reliability of our approach is demonstrated experimentally. | ||||
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| Publisher | Springer Berlin Heidelberg | Place of Publication | Editor | ||
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| ISSN | 1860-949X | ISBN | 978-3-642-24033-1 | Medium | |
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| Notes | ISE | Approved | no | ||
| Call Number | Admin @ si @ BFR2012 | Serial | 2062 | ||
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| Author |
Pau Baiget | ||||
| Title | Interpretation of Human Behavior in Image Sequences | Type | Report | ||
| Year | 2007 | Publication | CVC Technical Report #102 | Abbreviated Journal | |
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| Address | CVC (UAB) | ||||
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| Notes | Approved | no | |||
| Call Number | Admin @ si @ Bai2007 | Serial | 816 | ||
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| Author |
Pau Baiget | ||||
| Title | Modeling Human Behavior for Image Sequence Understanding and Generation | Type | Book Whole | ||
| Year | 2009 | Publication | PhD Thesis, Universitat Autonoma de Barcelona-CVC | Abbreviated Journal | |
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| Abstract | The comprehension of animal behavior, especially human behavior, is one of the most ancient and studied problems since the beginning of civilization. The big list of factors that interact to determine a person action require the collaboration of different disciplines, such as psichology, biology, or sociology. In the last years the analysis of human behavior has received great attention also from the computer vision community, given the latest advances in the acquisition of human motion data from image sequences.
Despite the increasing availability of that data, there still exists a gap towards obtaining a conceptual representation of the obtained observations. Human behavior analysis is based on a qualitative interpretation of the results, and therefore the assignment of concepts to quantitative data is linked to a certain ambiguity. This Thesis tackles the problem of obtaining a proper representation of human behavior in the contexts of computer vision and animation. On the one hand, a good behavior model should permit the recognition and explanation the observed activity in image sequences. On the other hand, such a model must allow the generation of new synthetic instances, which model the behavior of virtual agents. First, we propose methods to automatically learn the models from observations. Given a set of quantitative results output by a vision system, a normal behavior model is learnt. This results provides a tool to determine the normality or abnormality of future observations. However, machine learning methods are unable to provide a richer description of the observations. We confront this problem by means of a new method that incorporates prior knowledge about the enviornment and about the expected behaviors. This framework, formed by the reasoning engine FMTL and the modeling tool SGT allows the generation of conceptual descriptions of activity in new image sequences. Finally, we demonstrate the suitability of the proposed framework to simulate behavior of virtual agents, which are introduced into real image sequences and interact with observed real agents, thereby easing the generation of augmented reality sequences. The set of approaches presented in this Thesis has a growing set of potential applications. The analysis and description of behavior in image sequences has its principal application in the domain of smart video--surveillance, in order to detect suspicious or dangerous behaviors. Other applications include automatic sport commentaries, elderly monitoring, road traffic analysis, and the development of semantic video search engines. Alternatively, behavioral virtual agents allow to simulate accurate real situations, such as fires or crowds. Moreover, the inclusion of virtual agents into real image sequences has been widely deployed in the games and cinema industries. |
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| Address | Bellaterra (Spain) | ||||
| Corporate Author | Thesis | Ph.D. thesis | |||
| Publisher | Ediciones Graficas Rey | Place of Publication | Editor | Jordi Gonzalez;Xavier Roca | |
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| Notes | Approved | no | |||
| Call Number | Admin @ si @ Bai2009 | Serial | 1210 | ||
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| Author |
Patrick Brandao; O. Zisimopoulos; E. Mazomenos; G. Ciutib; Jorge Bernal; M. Visentini-Scarzanell; A. Menciassi; P. Dario; A. Koulaouzidis; A. Arezzo; D.J. Hawkes; D. Stoyanov | ||||
| Title | Towards a computed-aided diagnosis system in colonoscopy: Automatic polyp segmentation using convolution neural networks | Type | Journal | ||
| Year | 2018 | Publication | Journal of Medical Robotics Research | Abbreviated Journal | JMRR |
| Volume | 3 | Issue | 2 | Pages | |
| Keywords | convolutional neural networks; colonoscopy; computer aided diagnosis | ||||
| Abstract | Early diagnosis is essential for the successful treatment of bowel cancers including colorectal cancer (CRC) and capsule endoscopic imaging with robotic actuation can be a valuable diagnostic tool when combined with automated image analysis. We present a deep learning rooted detection and segmentation framework for recognizing lesions in colonoscopy and capsule endoscopy images. We restructure established convolution architectures, such as VGG and ResNets, by converting them into fully-connected convolution networks (FCNs), ne-tune them and study their capabilities for polyp segmentation and detection. We additionally use Shape-from-Shading (SfS) to recover depth and provide a richer representation of the tissue's structure in colonoscopy images. Depth is
incorporated into our network models as an additional input channel to the RGB information and we demonstrate that the resulting network yields improved performance. Our networks are tested on publicly available datasets and the most accurate segmentation model achieved a mean segmentation IU of 47.78% and 56.95% on the ETIS-Larib and CVC-Colon datasets, respectively. For polyp detection, the top performing models we propose surpass the current state of the art with detection recalls superior to 90% for all datasets tested. To our knowledge, we present the rst work to use FCNs for polyp segmentation in addition to proposing a novel combination of SfS and RGB that boosts performance. |
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| Notes | MV; no menciona | Approved | no | ||
| Call Number | BZM2018 | Serial | 2976 | ||
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