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Author Jorge Charco; Angel Sappa; Boris X. Vintimilla; Henry Velesaca
Title Human Body Pose Estimation in Multi-view Environments Type Book Chapter
Year 2022 Publication ICT Applications for Smart Cities. Intelligent Systems Reference Library Abbreviated Journal
Volume 224 Issue Pages (up) 79-99
Keywords
Abstract This chapter tackles the challenging problem of human pose estimation in multi-view environments to handle scenes with self-occlusions. The proposed approach starts by first estimating the camera pose—extrinsic parameters—in multi-view scenarios; due to few real image datasets, different virtual scenes are generated by using a special simulator, for training and testing the proposed convolutional neural network based approaches. Then, these extrinsic parameters are used to establish the relation between different cameras into the multi-view scheme, which captures the pose of the person from different points of view at the same time. The proposed multi-view scheme allows to robustly estimate human body joints’ position even in situations where they are occluded. This would help to avoid possible false alarms in behavioral analysis systems of smart cities, as well as applications for physical therapy, safe moving assistance for the elderly among other. The chapter concludes by presenting experimental results in real scenes by using state-of-the-art and the proposed multi-view approaches.
Address September 2022
Corporate Author Thesis
Publisher Springer Place of Publication Editor
Language Summary Language Original Title
Series Editor Series Title Abbreviated Series Title ISRL
Series Volume Series Issue Edition
ISSN ISBN 978-3-031-06306-0 Medium
Area Expedition Conference
Notes MSIAU; MACO Approved no
Call Number Admin @ si @ CSV2022b Serial 3810
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Author Asma Bensalah; Pau Riba; Alicia Fornes; Josep Llados
Title Shoot less and Sketch more: An Efficient Sketch Classification via Joining Graph Neural Networks and Few-shot Learning Type Conference Article
Year 2019 Publication 13th IAPR International Workshop on Graphics Recognition Abbreviated Journal
Volume Issue Pages (up) 80-85
Keywords Sketch classification; Convolutional Neural Network; Graph Neural Network; Few-shot learning
Abstract With the emergence of the touchpad devices and drawing tablets, a new era of sketching started afresh. However, the recognition of sketches is still a tough task due to the variability of the drawing styles. Moreover, in some application scenarios there is few labelled data available for training,
which imposes a limitation for deep learning architectures. In addition, in many cases there is a need to generate models able to adapt to new classes. In order to cope with these limitations, we propose a method based on few-shot learning and graph neural networks for classifying sketches aiming for an efficient neural model. We test our approach with several databases of
sketches, showing promising results.
Address Sydney; Australia; September 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 GREC
Notes DAG; 600.140; 601.302; 600.121 Approved no
Call Number Admin @ si @ BRF2019 Serial 3354
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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 (up) 81-105
Keywords 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 Hamdi Dibeklioglu; M.O. Hortas; I. Kosunen; P. Zuzánek; Albert Ali Salah; Theo Gevers
Title Design and implementation of an affect-responsive interactive photo frame Type Journal
Year 2011 Publication Journal on Multimodal User Interfaces Abbreviated Journal JMUI
Volume 4 Issue 2 Pages (up) 81-95
Keywords
Abstract This paper describes an affect-responsive interactive photo-frame application that offers its user a different experience with every use. It relies on visual analysis of activity levels and facial expressions of its users to select responses from a database of short video segments. This ever-growing database is automatically prepared by an offline analysis of user-uploaded videos. The resulting system matches its user’s affect along dimensions of valence and arousal, and gradually adapts its response to each specific user. In an extended mode, two such systems are coupled and feed each other with visual content. The strengths and weaknesses of the system are assessed through a usability study, where a Wizard-of-Oz response logic is contrasted with the fully automatic system that uses affective and activity-based features, either alone, or in tandem.
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 1783-7677 ISBN Medium
Area Expedition Conference
Notes ALTRES;ISE Approved no
Call Number Admin @ si @ DHK2011 Serial 1842
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Author Arnau Baro; Pau Riba; Jorge Calvo-Zaragoza; Alicia Fornes
Title Optical Music Recognition by Long Short-Term Memory Networks Type Book Chapter
Year 2018 Publication Graphics Recognition. Current Trends and Evolutions Abbreviated Journal
Volume 11009 Issue Pages (up) 81-95
Keywords Optical Music Recognition; Recurrent Neural Network; Long ShortTerm Memory
Abstract Optical Music Recognition refers to the task of transcribing the image of a music score into a machine-readable format. Many music scores are written in a single staff, and therefore, they could be treated as a sequence. Therefore, this work explores the use of Long Short-Term Memory (LSTM) Recurrent Neural Networks for reading the music score sequentially, where the LSTM helps in keeping the context. For training, we have used a synthetic dataset of more than 40000 images, labeled at primitive level. The experimental results are promising, showing the benefits of our approach.
Address
Corporate Author Thesis
Publisher Springer Place of Publication Editor A. Fornes, B. Lamiroy
Language Summary Language Original Title
Series Editor Series Title Abbreviated Series Title LNCS
Series Volume Series Issue Edition
ISSN ISBN 978-3-030-02283-9 Medium
Area Expedition Conference GREC
Notes DAG; 600.097; 601.302; 601.330; 600.121 Approved no
Call Number Admin @ si @ BRC2018 Serial 3227
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Author Ernest Valveny; Salvatore Tabbone; Oriol Ramos Terrades; Emilie Jean-Marie Odile
Title Performance Characterization of Shape Descriptors for Symbol Representation Type Conference Article
Year 2007 Publication Seventh IAPR International Workshop on Graphics Recognition Abbreviated Journal
Volume Issue Pages (up) 82–83
Keywords
Abstract
Address Curitiba (Brazil)
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 GREC
Notes DAG Approved no
Call Number DAG @ dag @ VTR2007 Serial 889
Permanent link to this record
 
 
Author Jose Antonio Rodriguez; Florent Perronnin
Title Score Normalization for Hmm-based Word Spotting Using Universal Background Model Type Conference Article
Year 2008 Publication International Conference on Frontiers in Handwriting Recognition Abbreviated Journal
Volume Issue Pages (up) 82–87
Keywords
Abstract
Address Montreal (Canada)
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 ICFHR
Notes Approved no
Call Number Admin @ si @ RoP2008c Serial 1067
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Author N. Serrano; L. Tarazon; D. Perez; Oriol Ramos Terrades; S. Juan
Title The GIDOC Prototype Type Conference Article
Year 2010 Publication 10th International Workshop on Pattern Recognition in Information Systems Abbreviated Journal
Volume Issue Pages (up) 82-89
Keywords
Abstract Transcription of handwritten text in (old) documents is an important, time-consuming task for digital libraries. It might be carried out by first processing all document images off-line, and then manually supervising system transcriptions to edit incorrect parts. However, current techniques for automatic page layout analysis, text line detection and handwriting recognition are still far from perfect, and thus post-editing system output is not clearly better than simply ignoring it.

A more effective approach to transcribe old text documents is to follow an interactive- predictive paradigm in which both, the system is guided by the user, and the user is assisted by the system to complete the transcription task as efficiently as possible. Following this approach, a system prototype called GIDOC (Gimp-based Interactive transcription of old text DOCuments) has been developed to provide user-friendly, integrated support for interactive-predictive layout analysis, line detection and handwriting transcription.

GIDOC is designed to work with (large) collections of homogeneous documents, that is, of similar structure and writing styles. They are annotated sequentially, by (par- tially) supervising hypotheses drawn from statistical models that are constantly updated with an increasing number of available annotated documents. And this is done at different annotation levels. For instance, at the level of page layout analysis, GIDOC uses a novel text block detection method in which conventional, memoryless techniques are improved with a “history” model of text block positions. Similarly, at the level of text line image transcription, GIDOC includes a handwriting recognizer which is steadily improved with a growing number of (partially) supervised transcriptions.
Address Funchal, Portugal
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 978-989-8425-14-0 Medium
Area Expedition Conference PRIS
Notes DAG Approved no
Call Number Admin @ si @ STP2010 Serial 1868
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Author Marçal Rusiñol; Josep Llados
Title A Performance Evaluation Protocol for Symbol Spotting Systems in Terms of Recognition and Location Indices Type Journal Article
Year 2009 Publication International Journal on Document Analysis and Recognition Abbreviated Journal IJDAR
Volume 12 Issue 2 Pages (up) 83-96
Keywords Performance evaluation; Symbol Spotting; Graphics Recognition
Abstract Symbol spotting systems are intended to retrieve regions of interest from a document image database where the queried symbol is likely to be found. They shall have the ability to recognize and locate graphical symbols in a single step. In this paper, we present a set of measures to evaluate the performance of a symbol spotting system in terms of recognition abilities, location accuracy and scalability. We show that the proposed measures allow to determine the weaknesses and strengths of different methods. In particular we have tested a symbol spotting method based on a set of four different off-the-shelf shape descriptors.
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 1433-2833 ISBN Medium
Area Expedition Conference
Notes DAG Approved no
Call Number DAG @ dag @ RuL2009a Serial 1166
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Author Xavier Boix; Josep M. Gonfaus; Joost Van de Weijer; Andrew Bagdanov; Joan Serrat; Jordi Gonzalez
Title Harmony Potentials: Fusing Global and Local Scale for Semantic Image Segmentation Type Journal Article
Year 2012 Publication International Journal of Computer Vision Abbreviated Journal IJCV
Volume 96 Issue 1 Pages (up) 83-102
Keywords
Abstract The Hierarchical Conditional Random Field(HCRF) model have been successfully applied to a number of image labeling problems, including image segmentation. However, existing HCRF models of image segmentation do not allow multiple classes to be assigned to a single region, which limits their ability to incorporate contextual information across multiple scales.
At higher scales in the image, this representation yields an oversimpli ed model since multiple classes can be reasonably expected to appear within large regions. This simpli ed model particularly limits the impact of information at higher scales. Since class-label information at these scales is usually more reliable than at lower, noisier scales, neglecting this information is undesirable. To
address these issues, we propose a new consistency potential for image labeling problems, which we call the harmony potential. It can encode any possible combi-
nation of labels, penalizing only unlikely combinations of classes. We also propose an e ective sampling strategy over this expanded label set that renders tractable the underlying optimization problem. Our approach obtains state-of-the-art results on two challenging, standard benchmark datasets for semantic image segmentation: PASCAL VOC 2010, and MSRC-21.
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 0920-5691 ISBN Medium
Area Expedition Conference
Notes ISE;CIC;ADAS Approved no
Call Number Admin @ si @ BGW2012 Serial 1718
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Author Alicia Fornes; Volkmar Frinken; Andreas Fischer; Jon Almazan; G. Jackson; Horst Bunke
Title A Keyword Spotting Approach Using Blurred Shape Model-Based Descriptors Type Conference Article
Year 2011 Publication Proceedings of the 2011 Workshop on Historical Document Imaging and Processing Abbreviated Journal
Volume Issue Pages (up) 83-90
Keywords
Abstract The automatic processing of handwritten historical documents is considered a hard problem in pattern recognition. In addition to the challenges given by modern handwritten data, a lack of training data as well as effects caused by the degradation of documents can be observed. In this scenario, keyword spotting arises to be a viable solution to make documents amenable for searching and browsing. For this task we propose the adaptation of shape descriptors used in symbol recognition. By treating each word image as a shape, it can be represented using the Blurred Shape Model and the De-formable Blurred Shape Model. Experiments on the George Washington database demonstrate that this approach is able to outperform the commonly used Dynamic Time Warping approach.
Address
Corporate Author Thesis
Publisher ACM Place of Publication Editor
Language Summary Language Original Title
Series Editor Series Title Abbreviated Series Title
Series Volume Series Issue Edition
ISSN ISBN 978-1-4503-0916-5 Medium
Area Expedition Conference HIP
Notes DAG Approved no
Call Number Admin @ si @ FFF2011a Serial 1823
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Author Shida Beigpour; Christian Riess; Joost Van de Weijer; Elli Angelopoulou
Title Multi-Illuminant Estimation with Conditional Random Fields Type Journal Article
Year 2014 Publication IEEE Transactions on Image Processing Abbreviated Journal TIP
Volume 23 Issue 1 Pages (up) 83-95
Keywords color constancy; CRF; multi-illuminant
Abstract Most existing color constancy algorithms assume uniform illumination. However, in real-world scenes, this is not often the case. Thus, we propose a novel framework for estimating the colors of multiple illuminants and their spatial distribution in the scene. We formulate this problem as an energy minimization task within a conditional random field over a set of local illuminant estimates. In order to quantitatively evaluate the proposed method, we created a novel data set of two-dominant-illuminant images comprised of laboratory, indoor, and outdoor scenes. Unlike prior work, our database includes accurate pixel-wise ground truth illuminant information. The performance of our method is evaluated on multiple data sets. Experimental results show that our framework clearly outperforms single illuminant estimators as well as a recently proposed multi-illuminant estimation approach.
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 1057-7149 ISBN Medium
Area Expedition Conference
Notes CIC; LAMP; 600.074; 600.079 Approved no
Call Number Admin @ si @ BRW2014 Serial 2451
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Author Pau Torras; Mohamed Ali Souibgui; Sanket Biswas; Alicia Fornes
Title Segmentation-Free Alignment of Arbitrary Symbol Transcripts to Images Type Conference Article
Year 2023 Publication Document Analysis and Recognition – ICDAR 2023 Workshops Abbreviated Journal
Volume 14193 Issue Pages (up) 83-93
Keywords Historical Manuscripts; Symbol Alignment
Abstract Developing arbitrary symbol recognition systems is a challenging endeavour. Even using content-agnostic architectures such as few-shot models, performance can be substantially improved by providing a number of well-annotated examples into training. In some contexts, transcripts of the symbols are available without any position information associated to them, which enables using line-level recognition architectures. A way of providing this position information to detection-based architectures is finding systems that can align the input symbols with the transcription. In this paper we discuss some symbol alignment techniques that are suitable for low-data scenarios and provide an insight on their perceived strengths and weaknesses. In particular, we study the usage of Connectionist Temporal Classification models, Attention-Based Sequence to Sequence models and we compare them with the results obtained on a few-shot recognition system.
Address
Corporate Author Thesis
Publisher Place of Publication Editor
Language Summary Language Original Title
Series Editor Series Title Abbreviated Series Title LNCS
Series Volume Series Issue Edition
ISSN ISBN Medium
Area Expedition Conference ICDAR
Notes DAG Approved no
Call Number Admin @ si @ TSS2023 Serial 3850
Permanent link to this record
 
 
Author Mathieu Nicolas Delalandre; Tony Pridmore; Ernest Valveny; Eric Trupin; Herve Locteau
Title Building Synthetic Graphical Documents for Performance Evaluation Type Conference Article
Year 2007 Publication Seventh IAPR International Workshop on Graphics Recognition Abbreviated Journal
Volume Issue Pages (up) 84–87
Keywords
Abstract
Address Curitiba (Brasil)
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 GREC
Notes DAG Approved no
Call Number DAG @ dag @ DPV2007 Serial 840
Permanent link to this record
 
 
Author Shida Beigpour; Joost Van de Weijer
Title Photo-Realistic Color Alteration for Architecture and Design Type Conference Article
Year 2010 Publication Proceedings of The CREATE 2010 Conference Abbreviated Journal
Volume Issue Pages (up) 84–88
Keywords
Abstract As color is a strong stimuli we receive from the exterior world, choosing the right color can prove crucial in creating the desired architecture and desing. We propose a framework to apply a realistic color change on both objects and their illuminant lights for snapshots of architectural designs, in order to visualize and choose the right color before actully applying the change in the real world. The proposed framework is based on the laws of physics in order to accomplish realistic and physically plausible results.
Address Gjovik (Norway)
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 CREATE
Notes CIC Approved no
Call Number CAT @ cat @ BeW2010 Serial 1330
Permanent link to this record