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Author |
Ernest Valveny; Miquel Ferrer |
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Title |
Application of Graph Embedding to Solve Graph Matchin Problems |
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Conference Article |
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2008 |
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Colloque International Francophone sur l’Ecrit et le Document |
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13–18 |
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Address ![sorted by Address field, ascending order (up)](http://refbase.cvc.uab.es/img/sort_asc.gif) |
Rouen (France) |
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CIFED’08 |
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DAG @ dag @ VaF2008 |
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1063 |
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Author |
H. Chouaib; Salvatore Tabbone; Oriol Ramos Terrades; F. Cloppet; N. Vincent; A.T. Thierry Paquet |
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Title |
Sélection de Caractéristiques à partir d'un algorithme génétique et d'une combinaison de classifieurs Adaboost |
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2008 |
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Colloque International Francophone sur l'Ecrit et le Document |
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181-186 |
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Address ![sorted by Address field, ascending order (up)](http://refbase.cvc.uab.es/img/sort_asc.gif) |
Rouen, France |
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CIFED |
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DAG |
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Admin @ si @ CTR2008 |
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1874 |
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Author |
T.O. Nguyen; Salvatore Tabbone; Oriol Ramos Terrades; A.T. Thierry |
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Title |
Proposition d'un descripteur de formes et du modèle vectoriel pour la recherche de symboles |
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2008 |
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Colloque International Francophone sur l'Ecrit et le Document |
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79-84 |
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Address ![sorted by Address field, ascending order (up)](http://refbase.cvc.uab.es/img/sort_asc.gif) |
Rouen, France |
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CIFED |
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Admin @ si @ NTR2008b |
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1875 |
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Clement Guerin; Christophe Rigaud; Karell Bertet; Jean-Christophe Burie; Arnaud Revel ; Jean-Marc Ogier |
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Title |
Réduction de l’espace de recherche pour les personnages de bandes dessinées |
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Conference Article |
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2014 |
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19th National Congress Reconnaissance de Formes et l'Intelligence Artificielle |
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contextual search; document analysis; comics characters |
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Les bandes dessinées représentent un patrimoine culturel important dans de nombreux pays et leur numérisation massive offre la possibilité d'effectuer des recherches dans le contenu des images. À ce jour, ce sont principalement les structures des pages et leurs contenus textuels qui ont été étudiés, peu de travaux portent sur le contenu graphique. Nous proposons de nous appuyer sur des éléments déjà étudiés tels que la position des cases et des bulles, pour réduire l'espace de recherche et localiser les personnages en fonction de la queue des bulles. L'évaluation de nos différentes contributions à partir de la base eBDtheque montre un taux de détection des queues de bulle de 81.2%, de localisation des personnages allant jusqu'à 85% et un gain d'espace de recherche de plus de 50%. |
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Address ![sorted by Address field, ascending order (up)](http://refbase.cvc.uab.es/img/sort_asc.gif) |
Rouen; Francia; July 2014 |
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RFIA |
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DAG; 600.077 |
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Admin @ si @ GRB2014 |
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2480 |
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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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Conference Article |
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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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Address ![sorted by Address field, ascending order (up)](http://refbase.cvc.uab.es/img/sort_asc.gif) |
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 |
Sergio Escalera; Alicia Fornes; Oriol Pujol; Petia Radeva |
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Title |
Multi-class Binary Symbol Classification with Circular Blurred Shape Models |
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Conference Article |
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2009 |
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15th International Conference on Image Analysis and Processing |
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5716 |
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1005–1014 |
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Multi-class binary symbol classification requires the use of rich descriptors and robust classifiers. Shape representation is a difficult task because of several symbol distortions, such as occlusions, elastic deformations, gaps or noise. In this paper, we present the Circular Blurred Shape Model descriptor. This descriptor encodes the arrangement information of object parts in a correlogram structure. A prior blurring degree defines the level of distortion allowed to the symbol. Moreover, we learn the new feature space using a set of Adaboost classifiers, which are combined in the Error-Correcting Output Codes framework to deal with the multi-class categorization problem. The presented work has been validated over different multi-class data sets, and compared to the state-of-the-art descriptors, showing significant performance improvements. |
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Address ![sorted by Address field, ascending order (up)](http://refbase.cvc.uab.es/img/sort_asc.gif) |
Salerno, Italy |
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Springer Berlin Heidelberg |
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0302-9743 |
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978-3-642-04145-7 |
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ICIAP |
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MILAB;HuPBA;DAG |
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BCNPCL @ bcnpcl @ EFP2009c |
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1186 |
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Author |
Dena Bazazian; Dimosthenis Karatzas; Andrew Bagdanov |
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Title |
Word Spotting in Scene Images based on Character Recognition |
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Conference Article |
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2018 |
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IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops |
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1872-1874 |
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In this paper we address the problem of unconstrained Word Spotting in scene images. We train a Fully Convolutional Network to produce heatmaps of all the character classes. Then, we employ the Text Proposals approach and, via a rectangle classifier, detect the most likely rectangle for each query word based on the character attribute maps. We evaluate the proposed method on ICDAR2015 and show that it is capable of identifying and recognizing query words in natural scene images. |
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Address ![sorted by Address field, ascending order (up)](http://refbase.cvc.uab.es/img/sort_asc.gif) |
Salt Lake City; USA; June 2018 |
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CVPRW |
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DAG; 600.129; 600.121 |
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BKB2018a |
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3179 |
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Ilke Demir; Dena Bazazian; Adriana Romero; Viktoriia Sharmanska; Lyne P. Tchapmi |
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Title |
WiCV 2018: The Fourth Women In Computer Vision Workshop |
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2018 |
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4th Women in Computer Vision Workshop |
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1941-19412 |
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Conferences; Computer vision; Industries; Object recognition; Engineering profession; Collaboration; Machine learning |
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We present WiCV 2018 – Women in Computer Vision Workshop to increase the visibility and inclusion of women researchers in computer vision field, organized in conjunction with CVPR 2018. Computer vision and machine learning have made incredible progress over the past years, yet the number of female researchers is still low both in academia and industry. WiCV is organized to raise visibility of female researchers, to increase the collaboration,
and to provide mentorship and give opportunities to femaleidentifying junior researchers in the field. In its fourth year, we are proud to present the changes and improvements over the past years, summary of statistics for presenters and attendees, followed by expectations from future generations. |
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Address ![sorted by Address field, ascending order (up)](http://refbase.cvc.uab.es/img/sort_asc.gif) |
Salt Lake City; USA; June 2018 |
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WiCV |
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DAG; 600.121; 600.129 |
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Admin @ si @ DBR2018 |
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3222 |
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Arka Ujjal Dey; Suman Ghosh; Ernest Valveny |
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Title |
Don't only Feel Read: Using Scene text to understand advertisements |
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2018 |
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IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops |
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We propose a framework for automated classification of Advertisement Images, using not just Visual features but also Textual cues extracted from embedded text. Our approach takes inspiration from the assumption that Ad images contain meaningful textual content, that can provide discriminative semantic interpretetion, and can thus aid in classifcation tasks. To this end, we develop a framework using off-the-shelf components, and demonstrate the effectiveness of Textual cues in semantic Classfication tasks. |
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Address ![sorted by Address field, ascending order (up)](http://refbase.cvc.uab.es/img/sort_asc.gif) |
Salt Lake City; Utah; USA; June 2018 |
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DAG; 600.121; 600.129 |
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Admin @ si @ DGV2018 |
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3551 |
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Francesc Net; Marc Folia; Pep Casals; Lluis Gomez |
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Title |
Transductive Learning for Near-Duplicate Image Detection in Scanned Photo Collections |
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Conference Article |
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2023 |
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17th International Conference on Document Analysis and Recognition |
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14191 |
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3-17 |
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Image deduplication; Near-duplicate images detection; Transductive Learning; Photographic Archives; Deep Learning |
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This paper presents a comparative study of near-duplicate image detection techniques in a real-world use case scenario, where a document management company is commissioned to manually annotate a collection of scanned photographs. Detecting duplicate and near-duplicate photographs can reduce the time spent on manual annotation by archivists. This real use case differs from laboratory settings as the deployment dataset is available in advance, allowing the use of transductive learning. We propose a transductive learning approach that leverages state-of-the-art deep learning architectures such as convolutional neural networks (CNNs) and Vision Transformers (ViTs). Our approach involves pre-training a deep neural network on a large dataset and then fine-tuning the network on the unlabeled target collection with self-supervised learning. The results show that the proposed approach outperforms the baseline methods in the task of near-duplicate image detection in the UKBench and an in-house private dataset. |
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Address ![sorted by Address field, ascending order (up)](http://refbase.cvc.uab.es/img/sort_asc.gif) |
San Jose; CA; USA; August 2023 |
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ICDAR |
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Admin @ si @ NFC2023 |
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3859 |
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