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Oriol Ramos Terrades; Ernest Valveny |
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Title |
Indexing Technical Symbols Using Ridgelets Transform |
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2004 |
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Graphics Recognition: Recent Advances and Perspectives, J. Llados, Y.B. Kwon (Eds.), Lecture Notes in Computer Science, 3088:177–187, ISBN: 3–540–22478–5 |
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503 |
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Ernest Valveny; Philippe Dosch |
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Title |
Symbol Recognition Contest: A Synthesis |
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2004 |
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Graphics Recognition: Recent Advances and Perspectives, J. Llados, Y.B. Kwon (Eds.), Lecture Notes in Computer Science, 3088:368–386, ISBN: 3–540–22478–5 |
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DAG @ dag @ VaD2004b |
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501 |
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Gemma Sanchez; Josep Llados |
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Title |
Syntactic models to represent perceptually regular repetitive patterns in graphic documents |
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Miscellaneous |
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2004 |
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Graphics Recognition: Recent Advances and Perspectives, Lecture Notes in Computer Science, J. Llados, Y.B. Kwon (Eds.), 3088:162–171 |
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DAG @ dag @ SaL2004 |
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462 |
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Joan Mas; Gemma Sanchez; Josep Llados |
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Title |
An Incremental Parser to Recognize Diagram Symbols and Gestures represented by Adjacency Grammars |
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2006 |
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Graphics Recognition: Ten Years Review and Future Perspectives, W. Liu, J. Llados (Eds.), LNCS 3926: 252–263 |
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DAG @ dag @ MSL2006a |
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711 |
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Alicia Fornes; Josep Llados; Gemma Sanchez |
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Title |
Primitive Segmentation in Old Handwritten Music Scores |
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2006 |
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Graphics Recognition: Ten Years Review and Future Perspectives, W. Liu, J. Llados (Eds.), LNCS 3926: 288–299 |
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DAG @ dag @ FLS2006 |
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697 |
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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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Philippe Dosch; Ernest Valveny |
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Title |
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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DAG @ dag @ DoV2006 |
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691 |
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Author |
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 |
Publication |
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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DAG @ dag @ DOL2008 |
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987 |
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Author |
Josep Llados; Marçal Rusiñol |
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Title |
Graphics Recognition Techniques |
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Year |
2014 |
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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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Abstract |
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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Year |
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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Author |
A.Kesidis; Dimosthenis Karatzas |
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Title |
Logo and Trademark Recognition |
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Year |
2014 |
Publication |
Handbook of Document Image Processing and Recognition |
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D |
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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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Abstract |
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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Author |
Alicia Fornes; Gemma Sanchez |
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Title |
Analysis and Recognition of Music Scores |
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Book Chapter |
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Year |
2014 |
Publication |
Handbook of Document Image Processing and Recognition |
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E |
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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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Author |
Julie Digne; Mariella Dimiccoli; Neus Sabater; Philippe Salembier |
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Title |
Neighborhood Filters and the Recovery of 3D Information |
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2015 |
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Handbook of Mathematical Methods in Imaging |
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III |
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1645-1673 |
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Following their success in image processing (see Chapter Local Smoothing Neighborhood Filters), neighborhood filters have been extended to 3D surface processing. This adaptation is not straightforward. It has led to several variants for surfaces depending on whether the surface is defined as a mesh, or as a raw data point set. The image gray level in the bilateral similarity measure is replaced by a geometric information such as the normal or the curvature. The first section of this chapter reviews the variants of 3D mesh bilateral filters and compares them to the simplest possible isotropic filter, the mean curvature motion.In a second part, this chapter reviews applications of the bilateral filter to a data composed of a sparse depth map (or of depth cues) and of the image on which they have been computed. Such sparse depth cues can be obtained by stereovision or by psychophysical techniques. The underlying assumption to these applications is that pixels with similar intensity around a region are likely to have similar depths. Therefore, when diffusing depth information with a bilateral filter based on locality and color similarity, the discontinuities in depth are assured to be consistent with the color discontinuities, which is generally a desirable property. In the reviewed applications, this ends up with the reconstruction of a dense perceptual depth map from the joint data of an image and of depth cues. |
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Springer New York |
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978-1-4939-0789-2 |
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MILAB |
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Admin @ si @ DDS2015 |
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2710 |
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Author |
Javier Marin; David Geronimo; David Vazquez; Antonio Lopez |
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Title |
Pedestrian Detection: Exploring Virtual Worlds |
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Year |
2012 |
Publication |
Handbook of Pattern Recognition: Methods and Application |
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5 |
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145-162 |
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Virtual worlds; Pedestrian Detection; Domain Adaptation |
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Abstract |
Handbook of pattern recognition will include contributions from university educators and active research experts. This Handbook is intended to serve as a basic reference on methods and applications of pattern recognition. The primary aim of this handbook is providing the community of pattern recognition with a readable, easy to understand resource that covers introductory, intermediate and advanced topics with equal clarity. Therefore, the Handbook of pattern recognition can serve equally well as reference resource and as classroom textbook. Contributions cover all methods, techniques and applications of pattern recognition. A tentative list of relevant topics might include: 1- Statistical, structural, syntactic pattern recognition. 2- Neural networks, machine learning, data mining. 3- Discrete geometry, algebraic, graph-based techniques for pattern recognition. 4- Face recognition, Signal analysis, image coding and processing, shape and texture analysis. 5- Document processing, text and graphics recognition, digital libraries. 6- Speech recognition, music analysis, multimedia systems. 7- Natural language analysis, information retrieval. 8- Biometrics, biomedical pattern analysis and information systems. 9- Other scientific, engineering, social and economical applications of pattern recognition. 10- Special hardware architectures, software packages for pattern recognition. |
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iConcept Press |
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English |
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978-1-477554-82-1 |
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ADAS |
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no |
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ADAS @ adas @ MGV2012 |
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1979 |
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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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Book Chapter |
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Year |
2020 |
Publication |
Handwritten Historical Document Analysis, Recognition, and Retrieval – State of the Art and Future Trends |
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Publisher |
World Scientific |
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ISBN |
978-981-120-323-7 |
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Notes |
DAG; 600.140; 600.121 |
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Call Number |
Admin @ si @ FLP2020 |
Serial |
3350 |
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