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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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G.Thorvaldsen; Joana Maria Pujadas-Mora; T.Andersen ; L.Eikvil; Josep Llados; Alicia Fornes; Anna Cabre |
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Title |
A Tale of two Transcriptions |
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2015 |
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Historical Life Course Studies |
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2 |
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1-19 |
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Nominative Sources; Census; Vital Records; Computer Vision; Optical Character Recognition; Word Spotting |
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non-indexed
This article explains how two projects implement semi-automated transcription routines: for census sheets in Norway and marriage protocols from Barcelona. The Spanish system was created to transcribe the marriage license books from 1451 to 1905 for the Barcelona area; one of the world’s longest series of preserved vital records. Thus, in the Project “Five Centuries of Marriages” (5CofM) at the Autonomous University of Barcelona’s Center for Demographic Studies, the Barcelona Historical Marriage Database has been built. More than 600,000 records were transcribed by 150 transcribers working online. The Norwegian material is cross-sectional as it is the 1891 census, recorded on one sheet per person. This format and the underlining of keywords for several variables made it more feasible to semi-automate data entry than when many persons are listed on the same page. While Optical Character Recognition (OCR) for printed text is scientifically mature, computer vision research is now focused on more difficult problems such as handwriting recognition. In the marriage project, document analysis methods have been proposed to automatically recognize the marriage licenses. Fully automatic recognition is still a challenge, but some promising results have been obtained. In Spain, Norway and elsewhere the source material is available as scanned pictures on the Internet, opening up the possibility for further international cooperation concerning automating the transcription of historic source materials. Like what is being done in projects to digitize printed materials, the optimal solution is likely to be a combination of manual transcription and machine-assisted recognition also for hand-written sources. |
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2352-6343 |
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DAG; 600.077; 602.006 |
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Admin @ si @ TPA2015 |
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2582 |
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Joana Maria Pujadas-Mora; Alicia Fornes; Oriol Ramos Terrades; Josep Llados; Jialuo Chen; Miquel Valls-Figols; Anna Cabre |
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Title |
The Barcelona Historical Marriage Database and the Baix Llobregat Demographic Database. From Algorithms for Handwriting Recognition to Individual-Level Demographic and Socioeconomic Data |
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2022 |
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Historical Life Course Studies |
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HLCS |
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12 |
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99-132 |
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Individual demographic databases; Computer vision, Record linkage; Social mobility; Inequality; Migration; Word spotting; Handwriting recognition; Local censuses; Marriage Licences |
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The Barcelona Historical Marriage Database (BHMD) gathers records of the more than 600,000 marriages celebrated in the Diocese of Barcelona and their taxation registered in Barcelona Cathedral's so-called Marriage Licenses Books for the long period 1451–1905 and the BALL Demographic Database brings together the individual information recorded in the population registers, censuses and fiscal censuses of the main municipalities of the county of Baix Llobregat (Barcelona). In this ongoing collection 263,786 individual observations have been assembled, dating from the period between 1828 and 1965 by December 2020. The two databases started as part of different interdisciplinary research projects at the crossroads of Historical Demography and Computer Vision. Their construction uses artificial intelligence and computer vision methods as Handwriting Recognition to reduce the time of execution. However, its current state still requires some human intervention which explains the implemented crowdsourcing and game sourcing experiences. Moreover, knowledge graph techniques have allowed the application of advanced record linkage to link the same individuals and families across time and space. Moreover, we will discuss the main research lines using both databases developed so far in historical demography. |
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June 23, 2022 |
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DAG; 600.121; 600.162; 602.230; 600.140 |
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Admin @ si @ PFR2022 |
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3737 |
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Author |
Alicia Fornes; Josep Llados; Joana Maria Pujadas-Mora |
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Title |
Browsing of the Social Network of the Past: Information Extraction from Population Manuscript Images |
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2020 |
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Handwritten Historical Document Analysis, Recognition, and Retrieval – State of the Art and Future Trends |
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World Scientific |
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978-981-120-323-7 |
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DAG; 600.140; 600.121 |
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Admin @ si @ FLP2020 |
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3350 |
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Author |
Josep Llados; Marçal Rusiñol |
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Title |
Graphics Recognition Techniques |
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Book Chapter |
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Year |
2014 |
Publication |
Handbook of Document Image Processing and Recognition |
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D |
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489-521 |
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Dimension recognition; Graphics recognition; Graphic-rich documents; Polygonal approximation; Raster-to-vector conversion; Texture-based primitive extraction; Text-graphics separation |
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This chapter describes the most relevant approaches for the analysis of graphical documents. The graphics recognition pipeline can be splitted into three tasks. The low level or lexical task extracts the basic units composing the document. The syntactic level is focused on the structure, i.e., how graphical entities are constructed, and involves the location and classification of the symbols present in the document. The third level is a functional or semantic level, i.e., it models what the graphical symbols do and what they mean in the context where they appear. This chapter covers the lexical level, while the next two chapters are devoted to the syntactic and semantic level, respectively. The main problems reviewed in this chapter are raster-to-vector conversion (vectorization algorithms) and the separation of text and graphics components. The research and industrial communities have provided standard methods achieving reasonable performance levels. Hence, graphics recognition techniques can be considered to be in a mature state from a scientific point of view. Additionally this chapter provides insights on some related problems, namely, the extraction and recognition of dimensions in engineering drawings, and the recognition of hatched and tiled patterns. Both problems are usually associated, even integrated, in the vectorization process. |
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Springer London |
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D. Doermann; K. Tombre |
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978-0-85729-858-4 |
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DAG; 600.077 |
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Admin @ si @ LlR2014 |
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2380 |
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Author |
Salvatore Tabbone; Oriol Ramos Terrades |
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Title |
An Overview of Symbol Recognition |
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2014 |
Publication |
Handbook of Document Image Processing and Recognition |
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D |
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523-551 |
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Pattern recognition; Shape descriptors; Structural descriptors; Symbolrecognition; Symbol spotting |
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According to the Cambridge Dictionaries Online, a symbol is a sign, shape, or object that is used to represent something else. Symbol recognition is a subfield of general pattern recognition problems that focuses on identifying, detecting, and recognizing symbols in technical drawings, maps, or miscellaneous documents such as logos and musical scores. This chapter aims at providing the reader an overview of the different existing ways of describing and recognizing symbols and how the field has evolved to attain a certain degree of maturity. |
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Springer London |
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D. Doermann; K. Tombre |
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978-0-85729-858-4 |
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DAG; 600.077 |
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Admin @ si @ TaT2014 |
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2489 |
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A.Kesidis; Dimosthenis Karatzas |
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Title |
Logo and Trademark Recognition |
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2014 |
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Handbook of Document Image Processing and Recognition |
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591-646 |
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Logo recognition; Logo removal; Logo spotting; Trademark registration; Trademark retrieval systems |
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The importance of logos and trademarks in nowadays society is indisputable, variably seen under a positive light as a valuable service for consumers or a negative one as a catalyst of ever-increasing consumerism. This chapter discusses the technical approaches for enabling machines to work with logos, looking into the latest methodologies for logo detection, localization, representation, recognition, retrieval, and spotting in a variety of media. This analysis is presented in the context of three different applications covering the complete depth and breadth of state of the art techniques. These are trademark retrieval systems, logo recognition in document images, and logo detection and removal in images and videos. This chapter, due to the very nature of logos and trademarks, brings together various facets of document image analysis spanning graphical and textual content, while it links document image analysis to other computer vision domains, especially when it comes to the analysis of real-scene videos and images. |
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Springer London |
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D. Doermann; K. Tombre |
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978-0-85729-858-4 |
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DAG; 600.077 |
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Admin @ si @ KeK2014 |
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2425 |
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Alicia Fornes; Gemma Sanchez |
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Title |
Analysis and Recognition of Music Scores |
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2014 |
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Handbook of Document Image Processing and Recognition |
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749-774 |
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The analysis and recognition of music scores has attracted the interest of researchers for decades. Optical Music Recognition (OMR) is a classical research field of Document Image Analysis and Recognition (DIAR), whose aim is to extract information from music scores. Music scores contain both graphical and textual information, and for this reason, techniques are closely related to graphics recognition and text recognition. Since music scores use a particular diagrammatic notation that follow the rules of music theory, many approaches make use of context information to guide the recognition and solve ambiguities. This chapter overviews the main Optical Music Recognition (OMR) approaches. Firstly, the different methods are grouped according to the OMR stages, namely, staff removal, music symbol recognition, and syntactical analysis. Secondly, specific approaches for old and handwritten music scores are reviewed. Finally, online approaches and commercial systems are also commented. |
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Springer London |
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D. Doermann; K. Tombre |
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978-0-85729-860-7 |
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DAG; ADAS; 600.076; 600.077 |
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Admin @ si @ FoS2014 |
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2484 |
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Mathieu Nicolas Delalandre; Jean-Marc Ogier; Josep Llados |
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Title |
A Fast Cbir System of Old Ornamental Letter |
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2008 |
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Graphics Reognition: Recent Advances and New Opportunities |
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5046 |
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135–144 |
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W. Liu, J. Llados, J.M. Ogier |
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987 |
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Philippe Dosch; Ernest Valveny |
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Report on the Second Symbol Recognition Contest |
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2006 |
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Graphics Recognition: Ten Years Review and Future Perspectives, W. Liu, J. Llados (Eds.), LNCS 3926: 381–397 |
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