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Author Lasse Martensson; Ekta Vats; Anders Hast; Alicia Fornes
Title In Search of the Scribe: Letter Spotting as a Tool for Identifying Scribes in Large Handwritten Text Corpora Type Journal
Year 2019 Publication Journal for Information Technology Studies as a Human Science Abbreviated Journal (up) HUMAN IT
Volume 14 Issue 2 Pages 95-120
Keywords Scribal attribution/ writer identification; digital palaeography; word spotting; mediaeval charters; mediaeval manuscripts
Abstract In this article, a form of the so-called word spotting-method is used on a large set of handwritten documents in order to identify those that contain script of similar execution. The point of departure for the investigation is the mediaeval Swedish manuscript Cod. Holm. D 3. The main scribe of this manuscript has yet not been identified in other documents. The current attempt aims at localising other documents that display a large degree of similarity in the characteristics of the script, these being possible candidates for being executed by the same hand. For this purpose, the method of word spotting has been employed, focusing on individual letters, and therefore the process is referred to as letter spotting in the article. In this process, a set of ‘g’:s, ‘h’:s and ‘k’:s have been selected as templates, and then a search has been made for close matches among the mediaeval Swedish charters. The search resulted in a number of charters that displayed great similarities with the manuscript D 3. The used letter spotting method thus proofed to be a very efficient sorting tool localising similar script samples.
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Notes DAG; 600.097; 600.140; 600.121 Approved no
Call Number Admin @ si @ MVH2019 Serial 3234
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Author Marc Sunset Perez; Marc Comino Trinidad; Dimosthenis Karatzas; Antonio Chica Calaf; Pere Pau Vazquez Alcocer
Title Development of general‐purpose projection‐based augmented reality systems Type Journal
Year 2016 Publication IADIs international journal on computer science and information systems Abbreviated Journal (up) IADIs
Volume 11 Issue 2 Pages 1-18
Keywords
Abstract Despite the large amount of methods and applications of augmented reality, there is little homogenizatio n on the software platforms that support them. An exception may be the low level control software that is provided by some high profile vendors such as Qualcomm and Metaio. However, these provide fine grain modules for e.g. element tracking. We are more co ncerned on the application framework, that includes the control of the devices working together for the development of the AR experience. In this paper we describe the development of a software framework for AR setups. We concentrate on the modular design of the framework, but also on some hard problems such as the calibration stage, crucial for projection – based AR. The developed framework is suitable and has been tested in AR applications using camera – projector pairs, for both fixed and nomadic setups
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Notes DAG; 600.084 Approved no
Call Number Admin @ si @ SCK2016 Serial 2890
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Author Carme Julia; Angel Sappa; Felipe Lumbreras; Joan Serrat; Antonio Lopez
Title Motion Segmentation from Feature Trajectories with Missing Data Type Conference Article
Year 2007 Publication 3rd. Iberian Conference on Pattern Recognition and Image Analysis Abbreviated Journal (up) IbPRIA 2007
Volume LNCS 4477 Issue Pages 483–490
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Address Girona (Spain)
Corporate Author Thesis
Publisher Place of Publication Editor J. Marti et al. (Eds.)
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Notes ADAS Approved no
Call Number ADAS @ adas @ JSL2007a Serial 814
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Author Carme Julia; Angel Sappa; Felipe Lumbreras; Joan Serrat; Antonio Lopez
Title Factorization with Missing and Noisy Data Type Conference Article
Year 2006 Publication 6th International Conference on Computational Science Abbreviated Journal (up) ICCS´06
Volume LNCS 3991 Issue Pages 555–562
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Address Reading (United Kingdom)
Corporate Author Thesis
Publisher Place of Publication Editor
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Notes ADAS Approved no
Call Number ADAS @ adas @ JSL2006b Serial 653
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Author Josep Llados; Felipe Lumbreras; V. Chapaprieta; J. Queralt
Title ICAR: Identity Card Automatic Reader. Type Miscellaneous
Year 2001 Publication Sixth International Conference on Document Analysis and Recognition Abbreviated Journal (up) ICDAR 2001
Volume Issue Pages 470–474
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Address USA
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Notes ADAS;DAG Approved no
Call Number ADAS @ adas @ LLC2001 Serial 112
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Author Angel Sappa; David Geronimo; Fadi Dornaika; Antonio Lopez
Title Real Time Vehicle Pose Using On-Board Stereo Vision System Type Conference Article
Year 2006 Publication International Conference on Image Analysis and Recognition Abbreviated Journal (up) ICIAR
Volume Issue LNCS 4142 Pages 205–216
Keywords
Abstract This paper presents a robust technique for a real time estimation of both camera’s position and orientation—referred as pose. A commercial stereo vision system is used. Unlike previous approaches, it can be used either for urban or highway scenarios. The proposed technique consists of two stages. Initially, a compact 2D representation of the original 3D data points is computed. Then, a RANSAC based least squares approach is used for fitting a plane to the road. At the same time,
relative camera’s position and orientation are computed. The proposed technique is intended to be used on a driving assistance scheme for applications such as obstacle or pedestrian detection. Experimental results on urban environments with different road geometries are presented.
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Notes ADAS Approved no
Call Number ADAS @ adas @ SGD2006b Serial 671
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Author Carme Julia; Angel Sappa; Felipe Lumbreras; Joan Serrat; Antonio Lopez
Title An Iterative Multiresolution Scheme for SFM Type Conference Article
Year 2006 Publication International Conference on Image Analysis and Recognition Abbreviated Journal (up) ICIAR 2006
Volume LNCS 4141 Issue 1 Pages 804–815
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Notes ADAS Approved no
Call Number ADAS @ adas @ JSL2006c Serial 704
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Author Maria Vanrell; Felipe Lumbreras; A. Pujol; Ramon Baldrich; Josep Llados; Juan J. Villanueva
Title Colour Normalisation Based on Background Information. Type Miscellaneous
Year 2001 Publication Proceeding ICIP 2001, IEEE International Conference on Image Processing Abbreviated Journal (up) ICIP 2001
Volume Issue 1 Pages 874–877
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Abstract
Address Grecia.
Corporate Author Thesis
Publisher Place of Publication Editor
Language Summary Language Original Title
Series Editor Series Title Abbreviated Series Title
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Notes ADAS;DAG;CIC Approved no
Call Number ADAS @ adas @ VLP2001 Serial 167
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Author G.Blasco; Simone Balocco; J.Puig; J.Sanchez-Gonzalez; W.Ricart; J.Daunis-I-Estadella; X.Molina; S.Pedraza; J.M.Fernandez-Real
Title Carotid pulse wave velocity by magnetic resonance imaging is increased in middle-aged subjects with the metabolic syndrome Type Journal Article
Year 2015 Publication International Journal of Cardiovascular Imaging Abbreviated Journal (up) ICJI
Volume 31 Issue 3 Pages 603-612
Keywords Metabolic syndrome; Arterial stiffness; Pulse wave velocity; Carotid artery; Magnetic resonance
Abstract Arterial pulse wave velocity (PWV), an independent predictor of cardiovascular disease, physiologically increases with age; however, growing evidence suggests metabolic syndrome (MetS) accelerates this increase. Magnetic resonance imaging (MRI) enables reliable noninvasive assessment of arterial stiffness by measuring arterial PWV in specific vascular segments. We investigated the association between the presence of MetS and its components with carotid PWV (cPWV) in asymptomatic subjects without diabetes. We assessed cPWV by MRI in 61 individuals (mean age, 55.3 ± 14.1 years; median age, 55 years): 30 with MetS and 31 controls with similar age, sex, body mass index, and LDL-cholesterol levels. The study population was dichotomized by the median age. To remove the physiological association between PWV and age, unpaired t tests and multiple regression analyses were performed using the residuals of the regression between PWV and age. cPWV was higher in middle-aged subjects with MetS than in those without (p = 0.001), but no differences were found in elder subjects (p = 0.313). cPWV was associated with diastolic blood pressure (r = 0.276, p = 0.033) and waist circumference (r = 0.268, p = 0.038). The presence of MetS was associated with increased cPWV regardless of age, sex, blood pressure, and waist (p = 0.007). The MetS components contributing independently to an increased cPWV were hypertension (p = 0.018) and hypertriglyceridemia (p = 0.002). The presence of MetS is associated with an increased cPWV in middle-aged subjects. In particular, hypertension and hypertriglyceridemia may contribute to early progression of carotid stiffness.
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Publisher Springer Netherlands Place of Publication Editor
Language Summary Language Original Title
Series Editor Series Title Abbreviated Series Title
Series Volume Series Issue Edition
ISSN 1569-5794 ISBN Medium
Area Expedition Conference
Notes MILAB Approved no
Call Number Admin @ si @ BBP2015 Serial 2670
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Author David Geronimo; Angel Sappa; Antonio Lopez; Daniel Ponsa
Title Adaptive Image Sampling and Windows Classification for On-board Pedestrian Detection Type Conference Article
Year 2007 Publication Proceedings of the 5th International Conference on Computer Vision Systems Abbreviated Journal (up) ICVS
Volume Issue Pages
Keywords Pedestrian Detection
Abstract On–board pedestrian detection is in the frontier of the state–of–the–art since it implies processing outdoor scenarios from a mobile platform and searching for aspect–changing objects in cluttered urban environments. Most promising approaches include the development of classifiers based on feature selection and machine learning. However, they use a large number of features which compromises real–time. Thus, methods for running the classifiers in only a few image windows must be provided. In this paper we contribute in both aspects, proposing a camera
pose estimation method for adaptive sparse image sampling, as well as a classifier for pedestrian detection based on Haar wavelets and edge orientation histograms as features and AdaBoost as learning machine. Both proposals are compared with relevant approaches in the literature, showing comparable results but reducing processing time by four for the sampling tasks and by ten for the classification one.
Address Bielefeld (Germany)
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Notes ADAS Approved no
Call Number ADAS @ adas @ gsl2007a Serial 786
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Author Qingshan Chen; Zhenzhen Quan; Yujun Li; Chao Zhai; Mikhail Mozerov
Title An Unsupervised Domain Adaption Approach for Cross-Modality RGB-Infrared Person Re-Identification Type Journal Article
Year 2023 Publication IEEE Sensors Journal Abbreviated Journal (up) IEEE-SENS
Volume 23 Issue 24 Pages
Keywords Q. Chen, Z. Quan, Y. Li, C. Zhai and M. G. Mozerov
Abstract Dual-camera systems commonly employed in surveillance serve as the foundation for RGB-infrared (IR) cross-modality person re-identification (ReID). However, significant modality differences give rise to inferior performance compared to single-modality scenarios. Furthermore, most existing studies in this area rely on supervised training with meticulously labeled datasets. Labeling RGB-IR image pairs is more complex than labeling conventional image data, and deploying pretrained models on unlabeled datasets can lead to catastrophic performance degradation. In contrast to previous solutions that focus solely on cross-modality or domain adaptation issues, this article presents an end-to-end unsupervised domain adaptation (UDA) framework for the cross-modality person ReID, which can simultaneously address both of these challenges. This model employs source domain classes, target domain clusters, and unclustered instance samples for the training, maximizing the comprehensive use of the dataset. Moreover, it addresses the problem of mismatched clustering labels between the two modalities in the target domain by incorporating a label matching module that reassigns reliable clusters with labels, ensuring correspondence between different modality labels. We construct the loss function by incorporating distinctiveness loss and multiplicity loss, both of which are determined by the similarity of neighboring features in the predicted feature space and the difference between distant features. This approach enables efficient feature clustering and cluster class assignment to occur concurrently. Eight UDA cross-modality person ReID experiments are conducted on three real datasets and six synthetic datasets. The experimental results unequivocally demonstrate that the proposed model outperforms the existing state-of-the-art algorithms to a significant degree. Notably, in RegDB → RegDB_light, the Rank-1 accuracy exhibits a remarkable improvement of 8.24%.
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Notes LAMP Approved no
Call Number Admin @ si @ CQL2023 Serial 3884
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Author Frederic Sampedro; Sergio Escalera
Title Spatial codification of label predictions in Multi-scale Stacked Sequential Learning: A case study on multi-class medical volume segmentation Type Journal Article
Year 2015 Publication IET Computer Vision Abbreviated Journal (up) IETCV
Volume 9 Issue 3 Pages 439 - 446
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Abstract In this study, the authors propose the spatial codification of label predictions within the multi-scale stacked sequential learning (MSSL) framework, a successful learning scheme to deal with non-independent identically distributed data entries. After providing a motivation for this objective, they describe its theoretical framework based on the introduction of the blurred shape model as a smart descriptor to codify the spatial distribution of the predicted labels and define the new extended feature set for the second stacked classifier. They then particularise this scheme to be applied in volume segmentation applications. Finally, they test the implementation of the proposed framework in two medical volume segmentation datasets, obtaining significant performance improvements (with a 95% of confidence) in comparison to standard Adaboost classifier and classical MSSL approaches.
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Publisher Place of Publication Editor
Language Summary Language Original Title
Series Editor Series Title Abbreviated Series Title
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ISSN 1751-9632 ISBN Medium
Area Expedition Conference
Notes HuPBA;MILAB Approved no
Call Number Admin @ si @ SaE2015 Serial 2551
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Author Huamin Ren; Nattiya Kanhabua; Andreas Mogelmose; Weifeng Liu; Kaustubh Kulkarni; Sergio Escalera; Xavier Baro; Thomas B. Moeslund
Title Back-dropout Transfer Learning for Action Recognition Type Journal Article
Year 2018 Publication IET Computer Vision Abbreviated Journal (up) IETCV
Volume 12 Issue 4 Pages 484-491
Keywords Learning (artificial intelligence); Pattern Recognition
Abstract Transfer learning aims at adapting a model learned from source dataset to target dataset. It is a beneficial approach especially when annotating on the target dataset is expensive or infeasible. Transfer learning has demonstrated its powerful learning capabilities in various vision tasks. Despite transfer learning being a promising approach, it is still an open question how to adapt the model learned from the source dataset to the target dataset. One big challenge is to prevent the impact of category bias on classification performance. Dataset bias exists when two images from the same category, but from different datasets, are not classified as the same. To address this problem, a transfer learning algorithm has been proposed, called negative back-dropout transfer learning (NB-TL), which utilizes images that have been misclassified and further performs back-dropout strategy on them to penalize errors. Experimental results demonstrate the effectiveness of the proposed algorithm. In particular, the authors evaluate the performance of the proposed NB-TL algorithm on UCF 101 action recognition dataset, achieving 88.9% recognition rate.
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Notes HUPBA; no proj Approved no
Call Number Admin @ si @ RKM2018 Serial 3071
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Author Mohamed Ilyes Lakhal; Hakan Çevikalp; Sergio Escalera; Ferda Ofli
Title Recurrent Neural Networks for Remote Sensing Image Classification Type Journal Article
Year 2018 Publication IET Computer Vision Abbreviated Journal (up) IETCV
Volume 12 Issue 7 Pages 1040 - 1045
Keywords
Abstract Automatically classifying an image has been a central problem in computer vision for decades. A plethora of models has been proposed, from handcrafted feature solutions to more sophisticated approaches such as deep learning. The authors address the problem of remote sensing image classification, which is an important problem to many real world applications. They introduce a novel deep recurrent architecture that incorporates high-level feature descriptors to tackle this challenging problem. Their solution is based on the general encoder–decoder framework. To the best of the authors’ knowledge, this is the first study to use a recurrent network structure on this task. The experimental results show that the proposed framework outperforms the previous works in the three datasets widely used in the literature. They have achieved a state-of-the-art accuracy rate of 97.29% on the UC Merced dataset.
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Notes HUPBA; no proj Approved no
Call Number Admin @ si @ LÇE2018 Serial 3119
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Author Meysam Madadi; Sergio Escalera; Xavier Baro; Jordi Gonzalez
Title End-to-end Global to Local CNN Learning for Hand Pose Recovery in Depth data Type Journal Article
Year 2022 Publication IET Computer Vision Abbreviated Journal (up) IETCV
Volume 16 Issue 1 Pages 50-66
Keywords Computer vision; data acquisition; human computer interaction; learning (artificial intelligence); pose estimation
Abstract Despite recent advances in 3D pose estimation of human hands, especially thanks to the advent of CNNs and depth cameras, this task is still far from being solved. This is mainly due to the highly non-linear dynamics of fingers, which make hand model training a challenging task. In this paper, we exploit a novel hierarchical tree-like structured CNN, in which branches are trained to become specialized in predefined subsets of hand joints, called local poses. We further fuse local pose features, extracted from hierarchical CNN branches, to learn higher order dependencies among joints in the final pose by end-to-end training. Lastly, the loss function used is also defined to incorporate appearance and physical constraints about doable hand motion and deformation. Finally, we introduce a non-rigid data augmentation approach to increase the amount of training depth data. Experimental results suggest that feeding a tree-shaped CNN, specialized in local poses, into a fusion network for modeling joints correlations and dependencies, helps to increase the precision of final estimations, outperforming state-of-the-art results on NYU and SyntheticHand datasets.
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Notes HUPBA; ISE; 600.098; 600.119 Approved no
Call Number Admin @ si @ MEB2022 Serial 3652
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