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Author |
Albert Berenguel; Oriol Ramos Terrades; Josep Llados; Cristina Cañero |
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Title |
Banknote counterfeit detection through background texture printing analysis |
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2016 |
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12th IAPR Workshop on Document Analysis Systems |
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This paper is focused on the detection of counterfeit photocopy banknotes. The main difficulty is to work on a real industrial scenario without any constraint about the acquisition device and with a single image. The main contributions of this paper are twofold: first the adaptation and performance evaluation of existing approaches to classify the genuine and photocopy banknotes using background texture printing analysis, which have not been applied into this context before. Second, a new dataset of Euro banknotes images acquired with several cameras under different luminance conditions to evaluate these methods. Experiments on the proposed algorithms show that mixing SIFT features and sparse coding dictionaries achieves quasi perfect classification using a linear SVM with the created dataset. Approaches using dictionaries to cover all possible texture variations have demonstrated to be robust and outperform the state-of-the-art methods using the proposed benchmark. |
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Rumania; May 2016 |
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DAS |
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DAG; 600.061; 601.269; 600.097 |
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Admin @ si @ BRL2016 |
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2950 |
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Author |
Lluis Gomez; Dimosthenis Karatzas |
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Title |
TextProposals: a Text‐specific Selective Search Algorithm for Word Spotting in the Wild |
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Journal Article |
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2017 |
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Pattern Recognition |
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PR |
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70 |
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60-74 |
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Motivated by the success of powerful while expensive techniques to recognize words in a holistic way (Goel et al., 2013; Almazán et al., 2014; Jaderberg et al., 2016) object proposals techniques emerge as an alternative to the traditional text detectors. In this paper we introduce a novel object proposals method that is specifically designed for text. We rely on a similarity based region grouping algorithm that generates a hierarchy of word hypotheses. Over the nodes of this hierarchy it is possible to apply a holistic word recognition method in an efficient way.
Our experiments demonstrate that the presented method is superior in its ability of producing good quality word proposals when compared with class-independent algorithms. We show impressive recall rates with a few thousand proposals in different standard benchmarks, including focused or incidental text datasets, and multi-language scenarios. Moreover, the combination of our object proposals with existing whole-word recognizers (Almazán et al., 2014; Jaderberg et al., 2016) shows competitive performance in end-to-end word spotting, and, in some benchmarks, outperforms previously published results. Concretely, in the challenging ICDAR2015 Incidental Text dataset, we overcome in more than 10% F-score the best-performing method in the last ICDAR Robust Reading Competition (Karatzas, 2015). Source code of the complete end-to-end system is available at https://github.com/lluisgomez/TextProposals. |
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DAG; 600.084; 601.197; 600.121; 600.129 |
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Admin @ si @ GoK2017 |
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2886 |
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Author |
Lluis Gomez; Anguelos Nicolaou; Dimosthenis Karatzas |
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Improving patch‐based scene text script identification with ensembles of conjoined networks |
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2017 |
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Pattern Recognition |
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PR |
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67 |
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85-96 |
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DAG; 600.084; 600.121; 600.129 |
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Admin @ si @ GNK2017 |
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2887 |
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Lluis Gomez; Y. Patel; Marçal Rusiñol; C.V. Jawahar; Dimosthenis Karatzas |
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Self‐supervised learning of visual features through embedding images into text topic spaces |
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Conference Article |
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2017 |
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30th IEEE Conference on Computer Vision and Pattern Recognition |
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End-to-end training from scratch of current deep architectures for new computer vision problems would require Imagenet-scale datasets, and this is not always possible. In this paper we present a method that is able to take advantage of freely available multi-modal content to train computer vision algorithms without human supervision. We put forward the idea of performing self-supervised learning of visual features by mining a large scale corpus of multi-modal (text and image) documents. We show that discriminative visual features can be learnt efficiently by training a CNN to predict the semantic context in which a particular image is more probable to appear as an illustration. For this we leverage the hidden semantic structures discovered in the text corpus with a well-known topic modeling technique. Our experiments demonstrate state of the art performance in image classification, object detection, and multi-modal retrieval compared to recent self-supervised or natural-supervised approaches. |
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Honolulu; Hawaii; July 2017 |
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CVPR |
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DAG; 600.084; 600.121 |
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Admin @ si @ GPR2017 |
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2889 |
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Marc Sunset Perez; Marc Comino Trinidad; Dimosthenis Karatzas; Antonio Chica Calaf; Pere Pau Vazquez Alcocer |
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Development of general‐purpose projection‐based augmented reality systems |
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2016 |
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IADIs international journal on computer science and information systems |
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IADIs |
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11 |
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2 |
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1-18 |
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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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DAG; 600.084 |
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Admin @ si @ SCK2016 |
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2890 |
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Author |
Marçal Rusiñol; Josep Llados |
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Title |
Flowchart Recognition in Patent Information Retrieval |
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Book Chapter |
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2017 |
Publication |
Current Challenges in Patent Information Retrieval |
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37 |
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351-368 |
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Springer Berlin Heidelberg |
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M. Lupu; K. Mayer; N. Kando; A.J. Trippe |
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DAG; 600.097; 600.121 |
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Admin @ si @ RuL2017 |
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2896 |
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Author |
Alicia Fornes; Josep Llados; Oriol Ramos Terrades; Marçal Rusiñol |
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Title |
La Visió per Computador com a Eina per a la Interpretació Automàtica de Fonts Documentals |
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2016 |
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Lligall, Revista Catalana d'Arxivística |
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39 |
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20-46 |
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DAG; 600.097 |
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Admin @ si @ FLR2016 |
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2897 |
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Arnau Baro; Pau Riba; Alicia Fornes |
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Towards the recognition of compound music notes in handwritten music scores |
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2016 |
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15th international conference on Frontiers in Handwriting Recognition |
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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. |
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Shenzhen; China; October 2016 |
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2167-6445 |
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ICFHR |
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DAG; 600.097 |
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Admin @ si @ BRF2016 |
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2903 |
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Joana Maria Pujadas-Mora; Alicia Fornes; Josep Llados; Anna Cabre |
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Bridging the gap between historical demography and computing: tools for computer-assisted transcription and the analysis of demographic sources |
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2016 |
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The future of historical demography. Upside down and inside out |
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127-131 |
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Acco Publishers |
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K.Matthijs; S.Hin; H.Matsuo; J.Kok |
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978-94-6292-722-3 |
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DAG; 600.097 |
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Admin @ si @ PFL2016 |
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2907 |
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Oriol Vicente; Alicia Fornes; Ramon Valdes |
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The Digital Humanities Network of the UABCie: a smart structure of research and social transference for the digital humanities |
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2016 |
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Digital Humanities Centres: Experiences and Perspectives |
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Warsaw; Poland; December 2016 |
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DHLABS |
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DAG; 600.097 |
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Admin @ si @ VFV2016 |
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2908 |
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