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Author R. Bertrand; P. Gomez-Krämer; Oriol Ramos Terrades; P. Franco; Jean-Marc Ogier
Title A System Based On Intrinsic Features for Fraudulent Document Detection Type Conference Article
Year 2013 Publication 12th International Conference on Document Analysis and Recognition Abbreviated Journal
Volume Issue Pages (down) 106-110
Keywords paper document; document analysis; fraudulent document; forgery; fake
Abstract Paper documents still represent a large amount of information supports used nowadays and may contain critical data. Even though official documents are secured with techniques such as printed patterns or artwork, paper documents suffer froma lack of security.
However, the high availability of cheap scanning and printing hardware allows non-experts to easily create fake documents. As the use of a watermarking system added during the document production step is hardly possible, solutions have to be proposed to distinguish a genuine document from a forged one.
In this paper, we present an automatic forgery detection method based on document’s intrinsic features at character level. This method is based on the one hand on outlier character detection in a discriminant feature space and on the other hand on the detection of strictly similar characters. Therefore, a feature set iscomputed for all characters. Then, based on a distance between characters of the same class.
Address Washington; USA; August 2013
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 1520-5363 ISBN Medium
Area Expedition Conference ICDAR
Notes DAG; 600.061 Approved no
Call Number Admin @ si @ BGR2013a Serial 2332
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Author Monica Piñol; Angel Sappa; Ricardo Toledo
Title Adaptive Feature Descriptor Selection based on a Multi-Table Reinforcement Learning Strategy Type Journal Article
Year 2015 Publication Neurocomputing Abbreviated Journal NEUCOM
Volume 150 Issue A Pages (down) 106–115
Keywords Reinforcement learning; Q-learning; Bag of features; Descriptors
Abstract This paper presents and evaluates a framework to improve the performance of visual object classification methods, which are based on the usage of image feature descriptors as inputs. The goal of the proposed framework is to learn the best descriptor for each image in a given database. This goal is reached by means of a reinforcement learning process using the minimum information. The visual classification system used to demonstrate the proposed framework is based on a bag of features scheme, and the reinforcement learning technique is implemented through the Q-learning approach. The behavior of the reinforcement learning with different state definitions is evaluated. Additionally, a method that combines all these states is formulated in order to select the optimal state. Finally, the chosen actions are obtained from the best set of image descriptors in the literature: PHOW, SIFT, C-SIFT, SURF and Spin. Experimental results using two public databases (ETH and COIL) are provided showing both the validity of the proposed approach and comparisons with state of the art. In all the cases the best results are obtained with the proposed 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 ISBN Medium
Area Expedition Conference
Notes ADAS; 600.055; 600.076 Approved no
Call Number Admin @ si @ PST2015 Serial 2473
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Author Xim Cerda-Company; C. Alejandro Parraga; Xavier Otazu
Title Which tone-mapping is the best? A comparative study of tone-mapping perceived quality Type Abstract
Year 2014 Publication Perception Abbreviated Journal
Volume 43 Issue Pages (down) 106
Keywords
Abstract Perception 43 ECVP Abstract Supplement
High-dynamic-range (HDR) imaging refers to the methods designed to increase the brightness dynamic range present in standard digital imaging techniques. This increase is achieved by taking the same picture under di erent exposure values and mapping the intensity levels into a single image by way of a tone-mapping operator (TMO). Currently, there is no agreement on how to evaluate the quality
of di erent TMOs. In this work we psychophysically evaluate 15 di erent TMOs obtaining rankings based on the perceived properties of the resulting tone-mapped images. We performed two di erent experiments on a CRT calibrated display using 10 subjects: (1) a study of the internal relationships between grey-levels and (2) a pairwise comparison of the resulting 15 tone-mapped images. In (1) observers internally matched the grey-levels to a reference inside the tone-mapped images and in the real scene. In (2) observers performed a pairwise comparison of the tone-mapped images alongside the real scene. We obtained two rankings of the TMOs according their performance. In (1) the best algorithm
was ICAM by J.Kuang et al (2007) and in (2) the best algorithm was a TMO by Krawczyk et al (2005). Our results also show no correlation between these two rankings.
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 ECVP
Notes CIC; NEUROBIT; 600.074 Approved no
Call Number Admin @ si @ CPO2014 Serial 2527
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Author Mohammad Ali Bagheri; Qigang Gao; Sergio Escalera; Huamin Ren; Thomas B. Moeslund; Elham Etemad
Title Locality Regularized Group Sparse Coding for Action Recognition Type Journal Article
Year 2017 Publication Computer Vision and Image Understanding Abbreviated Journal CVIU
Volume 158 Issue Pages (down) 106-114
Keywords Bag of words; Feature encoding; Locality constrained coding; Group sparse coding; Alternating direction method of multipliers; Action recognition
Abstract Bag of visual words (BoVW) models are widely utilized in image/ video representation and recognition. The cornerstone of these models is the encoding stage, in which local features are decomposed over a codebook in order to obtain a representation of features. In this paper, we propose a new encoding algorithm by jointly encoding the set of local descriptors of each sample and considering the locality structure of descriptors. The proposed method takes advantages of locality coding such as its stability and robustness to noise in descriptors, as well as the strengths of the group coding strategy by taking into account the potential relation among descriptors of a sample. To efficiently implement our proposed method, we consider the Alternating Direction Method of Multipliers (ADMM) framework, which results in quadratic complexity in the problem size. The method is employed for a challenging classification problem: action recognition by depth cameras. Experimental results demonstrate the outperformance of our methodology compared to the state-of-the-art on the considered datasets.
Address
Corporate Author Thesis
Publisher Place of Publication Editor
Language Summary Language Original Title
Series Editor Series Title Abbreviated Series Title
Series Volume Series Issue Edition
ISSN ISBN Medium
Area Expedition Conference
Notes HuPBA; no proj Approved no
Call Number Admin @ si @ BGE2017 Serial 3014
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Author David Berga; C. Wloka; JK. Tsotsos
Title Modeling task influences for saccade sequence and visual relevance prediction Type Journal Article
Year 2019 Publication Journal of Vision Abbreviated Journal JV
Volume 19 Issue 10 Pages (down) 106c-106c
Keywords
Abstract Previous work from Wloka et al. (2017) presented the Selective Tuning Attentive Reference model Fixation Controller (STAR-FC), an active vision model for saccade prediction. Although the model is able to efficiently predict saccades during free-viewing, it is well known that stimulus and task instructions can strongly affect eye movement patterns (Yarbus, 1967). These factors are considered in previous Selective Tuning architectures (Tsotsos and Kruijne, 2014)(Tsotsos, Kotseruba and Wloka, 2016)(Rosenfeld, Biparva & Tsotsos 2017), proposing a way to combine bottom-up and top-down contributions to fixation and saccade programming. In particular, task priming has been shown to be crucial to the deployment of eye movements, involving interactions between brain areas related to goal-directed behavior, working and long-term memory in combination with stimulus-driven eye movement neuronal correlates. Initial theories and models of these influences include (Rao, Zelinsky, Hayhoe and Ballard, 2002)(Navalpakkam and Itti, 2005)(Huang and Pashler, 2007) and show distinct ways to process the task requirements in combination with bottom-up attention. In this study we extend the STAR-FC with novel computational definitions of Long-Term Memory, Visual Task Executive and a Task Relevance Map. With these modules we are able to use textual instructions in order to guide the model to attend to specific categories of objects and/or places in the scene. We have designed our memory model by processing a hierarchy of visual features learned from salient object detection datasets. The relationship between the executive task instructions and the memory representations has been specified using a tree of semantic similarities between the learned features and the object category labels. Results reveal that by using this model, the resulting relevance maps and predicted saccades have a higher probability to fall inside the salient regions depending on the distinct task instructions.
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 NEUROBIT; 600.128; 600.120 Approved no
Call Number Admin @ si @ BWT2019 Serial 3308
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Author Sergi Garcia Bordils; Dimosthenis Karatzas; Marçal Rusiñol
Title Accelerating Transformer-Based Scene Text Detection and Recognition via Token Pruning Type Conference Article
Year 2023 Publication 17th International Conference on Document Analysis and Recognition Abbreviated Journal
Volume 14192 Issue Pages (down) 106-121
Keywords Scene Text Detection; Scene Text Recognition; Transformer Acceleration
Abstract Scene text detection and recognition is a crucial task in computer vision with numerous real-world applications. Transformer-based approaches are behind all current state-of-the-art models and have achieved excellent performance. However, the computational requirements of the transformer architecture makes training these methods slow and resource heavy. In this paper, we introduce a new token pruning strategy that significantly decreases training and inference times without sacrificing performance, striking a balance between accuracy and speed. We have applied this pruning technique to our own end-to-end transformer-based scene text understanding architecture. Our method uses a separate detection branch to guide the pruning of uninformative image features, which significantly reduces the number of tokens at the input of the transformer. Experimental results show how our network is able to obtain competitive results on multiple public benchmarks while running at significantly higher speeds.
Address San Jose; CA; USA; August 2023
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 @ GKR2023a Serial 3907
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Author Alvaro Cepero; Albert Clapes; Sergio Escalera
Title Quantitative analysis of non-verbal communication for competence analysis Type Conference Article
Year 2013 Publication 16th Catalan Conference on Artificial Intelligence Abbreviated Journal
Volume 256 Issue Pages (down) 105-114
Keywords
Abstract
Address Vic; October 2013
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 CCIA
Notes HUPBA;MILAB Approved no
Call Number Admin @ si @ CCE2013 Serial 2324
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Author Mireia Sole; Joan Blanco; Debora Gil; G. Fonseka; Richard Frodsham; Oliver Valero; Francesca Vidal; Zaida Sarrate
Title Análisis 3d de la territorialidad cromosómica en células espermatogénicas: explorando la infertilidad desde un nuevo prisma Type Journal
Year 2017 Publication Revista Asociación para el Estudio de la Biología de la Reproducción Abbreviated Journal ASEBIR
Volume 22 Issue 2 Pages (down) 105
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 IAM; 600.096; 600.145 Approved no
Call Number Admin @ si @ SBG2017d Serial 3042
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Author Rada Deeb; Joost Van de Weijer; Damien Muselet; Mathieu Hebert; Alain Tremeau
Title Deep spectral reflectance and illuminant estimation from self-interreflections Type Journal Article
Year 2019 Publication Journal of the Optical Society of America A Abbreviated Journal JOSA A
Volume 31 Issue 1 Pages (down) 105-114
Keywords
Abstract In this work, we propose a convolutional neural network based approach to estimate the spectral reflectance of a surface and spectral power distribution of light from a single RGB image of a V-shaped surface. Interreflections happening in a concave surface lead to gradients of RGB values over its area. These gradients carry a lot of information concerning the physical properties of the surface and the illuminant. Our network is trained with only simulated data constructed using a physics-based interreflection model. Coupling interreflection effects with deep learning helps to retrieve the spectral reflectance under an unknown light and to estimate spectral power distribution of this light as well. In addition, it is more robust to the presence of image noise than classical approaches. Our results show that the proposed approach outperforms state-of-the-art learning-based approaches on simulated data. In addition, it gives better results on real data compared to other interreflection-based approaches.
Address
Corporate Author Thesis
Publisher Place of Publication Editor
Language Summary Language Original Title
Series Editor Series Title Abbreviated Series Title
Series Volume Series Issue Edition
ISSN ISBN Medium
Area Expedition Conference
Notes LAMP; 600.120 Approved no
Call Number Admin @ si @ DWM2019 Serial 3362
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Author Marçal Rusiñol; Josep Llados
Title A Region-Based Hashing Approach for Symbol Spotting in Technical Documents Type Book Chapter
Year 2008 Publication Graphics Recognition: Recent Advances and New Opportunities Abbreviated Journal
Volume 5046 Issue Pages (down) 104–113
Keywords
Abstract
Address
Corporate Author Thesis
Publisher Place of Publication Editor W. Lius, J. Llados, J.M. Ogier
Language Summary Language Original Title
Series Editor Series Title Abbreviated Series Title LNCS
Series Volume Series Issue Edition
ISSN ISBN Medium
Area Expedition Conference
Notes DAG Approved no
Call Number DAG @ dag @ RuL2008a Serial 959
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Author Josep Llados; Ernest Valveny; Gemma Sanchez; Enric Marti
Title Symbol recognition: current advances and perspectives Type Book Chapter
Year 2002 Publication Graphics Recognition Algorithms And Applications Abbreviated Journal LNCS
Volume 2390 Issue Pages (down) 104-128
Keywords
Abstract The recognition of symbols in graphic documents is an intensive research activity in the community of pattern recognition and document analysis. A key issue in the interpretation of maps, engineering drawings, diagrams, etc. is the recognition of domain dependent symbols according to a symbol database. In this work we first review the most outstanding symbol recognition methods from two different points of view: application domains and pattern recognition methods. In the second part of the paper, open and unaddressed problems involved in symbol recognition are described, analyzing their current state of art and discussing future research challenges. Thus, issues such as symbol representation, matching, segmentation, learning, scalability of recognition methods and performance evaluation are addressed in this work. Finally, we discuss the perspectives of symbol recognition concerning to new paradigms such as user interfaces in handheld computers or document database and WWW indexing by graphical content.
Address London, UK
Corporate Author Thesis
Publisher Springer-Verlag Place of Publication Editor Dorothea Blostein and Young- Bin Kwon
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 3-540-44066-6 Medium
Area Expedition Conference GREC
Notes DAG; IAM; Approved no
Call Number IAM @ iam @ LVS2002 Serial 1572
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Author Maedeh Aghaei; Mariella Dimiccoli; C. Canton-Ferrer; Petia Radeva
Title Towards social pattern characterization from egocentric photo-streams Type Journal Article
Year 2018 Publication Computer Vision and Image Understanding Abbreviated Journal CVIU
Volume 171 Issue Pages (down) 104-117
Keywords Social pattern characterization; Social signal extraction; Lifelogging; Convolutional and recurrent neural networks
Abstract Following the increasingly popular trend of social interaction analysis in egocentric vision, this article presents a comprehensive pipeline for automatic social pattern characterization of a wearable photo-camera user. The proposed framework relies merely on the visual analysis of egocentric photo-streams and consists of three major steps. The first step is to detect social interactions of the user where the impact of several social signals on the task is explored. The detected social events are inspected in the second step for categorization into different social meetings. These two steps act at event-level where each potential social event is modeled as a multi-dimensional time-series, whose dimensions correspond to a set of relevant features for each task; finally, LSTM is employed to classify the time-series. The last step of the framework is to characterize social patterns of the user. Our goal is to quantify the duration, the diversity and the frequency of the user social relations in various social situations. This goal is achieved by the discovery of recurrences of the same people across the whole set of social events related to the user. Experimental evaluation over EgoSocialStyle – the proposed dataset in this work, and EGO-GROUP demonstrates promising results on the task of social pattern characterization from egocentric photo-streams.
Address
Corporate Author Thesis
Publisher Place of Publication Editor
Language Summary Language Original Title
Series Editor Series Title Abbreviated Series Title
Series Volume Series Issue Edition
ISSN ISBN Medium
Area Expedition Conference
Notes MILAB; no proj Approved no
Call Number Admin @ si @ ADC2018 Serial 3022
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Author Alicia Fornes; Josep Llados; Joan Mas; Joana Maria Pujadas-Mora; Anna Cabre
Title A Bimodal Crowdsourcing Platform for Demographic Historical Manuscripts Type Conference Article
Year 2014 Publication Digital Access to Textual Cultural Heritage Conference Abbreviated Journal
Volume Issue Pages (down) 103-108
Keywords
Abstract In this paper we present a crowdsourcing web-based application for extracting information from demographic handwritten document images. The proposed application integrates two points of view: the semantic information for demographic research, and the ground-truthing for document analysis research. Concretely, the application has the contents view, where the information is recorded into forms, and the labeling view, with the word labels for evaluating document analysis techniques. The crowdsourcing architecture allows to accelerate the information extraction (many users can work simultaneously), validate the information, and easily provide feedback to the users. We finally show how the proposed application can be extended to other kind of demographic historical manuscripts.
Address Madrid; May 2014
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 978-1-4503-2588-2 Medium
Area Expedition Conference DATeCH
Notes DAG; 600.061; 602.006; 600.077 Approved no
Call Number Admin @ si @ FLM2014 Serial 2516
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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 (down) 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
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Author Gemma Rotger; Francesc Moreno-Noguer; Felipe Lumbreras; Antonio Agudo
Title Detailed 3D face reconstruction from a single RGB image Type Journal
Year 2019 Publication Journal of WSCG Abbreviated Journal JWSCG
Volume 27 Issue 2 Pages (down) 103-112
Keywords 3D Wrinkle Reconstruction; Face Analysis, Optimization.
Abstract This paper introduces a method to obtain a detailed 3D reconstruction of facial skin from a single RGB image.
To this end, we propose the exclusive use of an input image without requiring any information about the observed material nor training data to model the wrinkle properties. They are detected and characterized directly from the image via a simple and effective parametric model, determining several features such as location, orientation, width, and height. With these ingredients, we propose to minimize a photometric error to retrieve the final detailed 3D map, which is initialized by current techniques based on deep learning. In contrast with other approaches, we only require estimating a depth parameter, making our approach fast and intuitive. Extensive experimental evaluation is presented in a wide variety of synthetic and real images, including different skin properties and facial
expressions. In all cases, our method outperforms the current approaches regarding 3D reconstruction accuracy, providing striking results for both large and fine wrinkles.
Address 2019/11
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; 600.086; 600.130; 600.122 Approved no
Call Number Admin @ si @ Serial 3708
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