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Author  |
Marçal Rusiñol; J. Chazalon; Jean-Marc Ogier; Josep Llados |
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Title |
A Comparative Study of Local Detectors and Descriptors for Mobile Document Classification |
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2015 |
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13th International Conference on Document Analysis and Recognition ICDAR2015 |
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596-600 |
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In this paper we conduct a comparative study of local key-point detectors and local descriptors for the specific task of mobile document classification. A classification architecture based on direct matching of local descriptors is used as baseline for the comparative study. A set of four different key-point
detectors and four different local descriptors are tested in all the possible combinations. The experiments are conducted in a database consisting of 30 model documents acquired on 6 different backgrounds, totaling more than 36.000 test images. |
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Nancy; France; August 2015 |
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DAG; 600.084; 600.61; 601.223; 600.077 |
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Admin @ si @ RCO2015 |
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2684 |
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Marçal Rusiñol; J. Chazalon; Katerine Diaz |
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Title |
Augmented Songbook: an Augmented Reality Educational Application for Raising Music Awareness |
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Journal Article |
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Year |
2018 |
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Multimedia Tools and Applications |
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MTAP |
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77 |
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11 |
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13773-13798 |
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Augmented reality; Document image matching; Educational applications |
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This paper presents the development of an Augmented Reality mobile application which aims at sensibilizing young children to abstract concepts of music. Such concepts are, for instance, the musical notation or the idea of rhythm. Recent studies in Augmented Reality for education suggest that such technologies have multiple benefits for students, including younger ones. As mobile document image acquisition and processing gains maturity on mobile platforms, we explore how it is possible to build a markerless and real-time application to augment the physical documents with didactic animations and interactive virtual content. Given a standard image processing pipeline, we compare the performance of different local descriptors at two key stages of the process. Results suggest alternatives to the SIFT local descriptors, regarding result quality and computational efficiency, both for document model identification and perspective transform estimation. All experiments are performed on an original and public dataset we introduce here. |
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DAG; ADAS; 600.084; 600.121; 600.118; 600.129 |
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Admin @ si @ RCD2018 |
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2996 |
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Marçal Rusiñol; Josep Llados |
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Title |
Symbol Spotting in Technical Drawings Using Vectorial Signatures |
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Miscellaneous |
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2005 |
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6th IAPR International Workshop on Graphics Recognition (GREC 2005), 35–45 |
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Hong Kong |
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DAG @ dag @ RuL2005 |
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579 |
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Marçal Rusiñol; Josep Llados |
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Title |
Symbol Spotting in Technical Drawings Using Vectorial Signatures |
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2006 |
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Graphics Recognition: Ten Years Review and Future Perspectives, W. Liu, J. Llados (Eds.), LNCS 3926: 35–46 |
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DAG @ dag @ RuL2006a |
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719 |
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Author |
Marçal Rusiñol; Josep Llados |
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Title |
A Region-Based Hashing Approach for Symbol Spotting in Thechnical Documents |
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2007 |
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Seventh IAPR International Workshop on Graphics Recognition |
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41–42 |
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Curitiba (Brazil) |
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J. Llados, W. Liu, J.M. Ogier |
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GREC |
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DAG @ dag @ RuL2007a |
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846 |
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Author |
Marçal Rusiñol; Josep Llados |
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Title |
A Region-Based Hashing Approach for Symbol Spotting in Technical Documents |
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2008 |
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Graphics Recognition: Recent Advances and New Opportunities |
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5046 |
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104–113 |
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W. Lius, J. Llados, J.M. Ogier |
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DAG @ dag @ RuL2008a |
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959 |
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Author |
Marçal Rusiñol; Josep Llados |
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Title |
Word and Symbol Spotting using Spatial Organization of Local Descriptors |
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2008 |
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Proceedings of the 8th IAPR International Workshop on Document Analysis Systems, |
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489–496 |
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Nara (Japan) |
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DAS |
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DAG |
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DAG @ dag @ RuL2008b |
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1059 |
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Author |
Marçal Rusiñol; Josep Llados |
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Title |
A Performance Evaluation Protocol for Symbol Spotting Systems in Terms of Recognition and Location Indices |
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Journal Article |
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2009 |
Publication |
International Journal on Document Analysis and Recognition |
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IJDAR |
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12 |
Issue |
2 |
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83-96 |
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Keywords |
Performance evaluation; Symbol Spotting; Graphics Recognition |
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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. |
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1433-2833 |
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DAG |
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DAG @ dag @ RuL2009a |
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1166 |
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Author |
Marçal Rusiñol; Josep Llados |
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Title |
Logo Spotting by a Bag-of-words Approach for Document Categorization |
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Conference Article |
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2009 |
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10th International Conference on Document Analysis and Recognition |
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111–115 |
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In this paper we present a method for document categorization which processes incoming document images such as invoices or receipts. The categorization of these document images is done in terms of the presence of a certain graphical logo detected without segmentation. The graphical logos are described by a set of local features and the categorization of the documents is performed by the use of a bag-of-words model. Spatial coherence rules are added to reinforce the correct category hypothesis, aiming also to spot the logo inside the document image. Experiments which demonstrate the effectiveness of this system on a large set of real data are presented. |
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Barcelona; Spain |
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1520-5363 |
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978-1-4244-4500-4 |
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ICDAR |
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DAG |
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DAG @ dag @ RuL2009b |
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1179 |
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Author |
Marçal Rusiñol; Josep Llados |
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Title |
Symbol Spotting in Digital Libraries:Focused Retrieval over Graphic-rich Document Collections |
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2010 |
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Symbol Spotting in Digital Libraries:Focused Retrieval over Graphic-rich Document Collections |
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Focused Retrieval , Graphical Pattern Indexation,Graphics Recognition ,Pattern Recognition , Performance Evaluation , Symbol Description ,Symbol Spotting |
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The specific problem of symbol recognition in graphical documents requires additional techniques to those developed for character recognition. The most well-known obstacle is the so-called Sayre paradox: Correct recognition requires good segmentation, yet improvement in segmentation is achieved using information provided by the recognition process. This dilemma can be avoided by techniques that identify sets of regions containing useful information. Such symbol-spotting methods allow the detection of symbols in maps or technical drawings without having to fully segment or fully recognize the entire content.
This unique text/reference provides a complete, integrated and large-scale solution to the challenge of designing a robust symbol-spotting method for collections of graphic-rich documents. The book examines a number of features and descriptors, from basic photometric descriptors commonly used in computer vision techniques to those specific to graphical shapes, presenting a methodology which can be used in a wide variety of applications. Additionally, readers are supplied with an insight into the problem of performance evaluation of spotting methods. Some very basic knowledge of pattern recognition, document image analysis and graphics recognition is assumed. |
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978-1-84996-208-7 |
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DAG @ dag @ RuL2010a |
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1292 |
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