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Author Beata Megyesi; Alicia Fornes; Nils Kopal; Benedek Lang edit  url
openurl 
  Title Historical Cryptology Type (up) Book Chapter
  Year 2024 Publication Learning and Experiencing Cryptography with CrypTool and SageMath Abbreviated Journal  
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  Abstract Historical cryptology studies (original) encrypted manuscripts, often handwritten sources, produced in our history. These historical sources can be found in archives, often hidden without any indexing and therefore hard to locate. Once found they need to be digitized and turned into a machine-readable text format before they can be deciphered with computational methods. The focus of historical cryptology is not primarily the development of sophisticated algorithms for decipherment, but rather the entire process of analysis of the encrypted source from collection and digitization to transcription and decryption. The process also includes the interpretation and contextualization of the message set in its historical context. There are many challenges on the way, such as mistakes made by the scribe, errors made by the transcriber, damaged pages, handwriting styles that are difficult to interpret, historical languages from various time periods, and hidden underlying language of the message. Ciphertexts vary greatly in terms of their code system and symbol sets used with more or less distinguishable symbols. Ciphertexts can be embedded in clearly written text, or shorter or longer sequences of cleartext can be embedded in the ciphertext. The ciphers used mostly in historical times are substitutions (simple, homophonic, or polyphonic), with or without nomenclatures, encoded as digits or symbol sequences, with or without spaces. So the circumstances are different from those in modern cryptography which focuses on methods (algorithms) and their strengths and assumes that the algorithm is applied correctly. For both historical and modern cryptology, attack vectors outside the algorithm are applied like implementation flaws and side-channel attacks. In this chapter, we give an introduction to the field of historical cryptology and present an overview of how researchers today process historical encrypted sources.  
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  Call Number Admin @ si @ MFK2024 Serial 4020  
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Author Joan Mas; Gemma Sanchez; Josep Llados edit  openurl
  Title An Incremental Parser to Recognize Diagram Symbols and Gestures represented by Adjacency Grammars Type (up) Book Whole
  Year 2006 Publication Graphics Recognition: Ten Years Review and Future Perspectives, W. Liu, J. Llados (Eds.), LNCS 3926: 252–263 Abbreviated Journal  
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  Notes DAG Approved no  
  Call Number DAG @ dag @ MSL2006a Serial 711  
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Author Oriol Ramos Terrades edit  openurl
  Title Linear Combination of Multiresolution Descriptors: Application to Graphics Recognition Type (up) Book Whole
  Year 2006 Publication PhD Thesis, Universitat Autonoma de Barcelona-CVC & Universite Nancy 2 Abbreviated Journal  
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  Corporate Author Thesis Ph.D. thesis  
  Publisher Place of Publication Editor Salvatore Antoine Tabbone;Ernest Valveny  
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  Notes DAG Approved no  
  Call Number DAG @ dag @ Ram2006 Serial 713  
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Author Josep Llados edit  isbn
openurl 
  Title Computer Vision: Progress of Research and Development Type (up) Book Whole
  Year 2006 Publication 1st CVC Internal Workshop Computer Vision: Progress of Research and Development, Abbreviated Journal  
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  Publisher Place of Publication Editor J. Llados (ed.),  
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  ISSN ISBN 84-933652-8-9 Medium  
  Area Expedition Conference CVCRD  
  Notes DAG Approved no  
  Call Number DAG @ dag @ Lla2006b Serial 766  
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Author W. Liu; Josep Llados edit  openurl
  Title Graphics Recognition. Ten Years Review and Future Perspectives Type (up) Book Whole
  Year 2006 Publication 6th International Workshop Abbreviated Journal  
  Volume 3926 Issue Pages  
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  Address Hong Kong (China)  
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  Series Editor Series Title Abbreviated Series Title LNCS  
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  Area Expedition Conference GREC  
  Notes DAG Approved no  
  Call Number DAG @ dag @ LiL2006 Serial 800  
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Author Josep Llados; W. Liu; Jean-Marc Ogier edit  openurl
  Title Seventh IAPR International Workshop on Graphics Recognition GREC 2007 Type (up) Book Whole
  Year 2007 Publication Abbreviated Journal  
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  Address Curitiba (Brazil)  
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  Notes DAG Approved no  
  Call Number DAG @ dag @ LLO2007 Serial 835  
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Author Liu Wenyin; Josep Llados; Jean-Marc Ogier edit  isbn
openurl 
  Title Graphics Recognition. Recent Advances and New Opportunities. Type (up) Book Whole
  Year 2008 Publication 7th International Workshop, Selected Papers, Abbreviated Journal  
  Volume 5046 Issue Pages  
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  Address Curitiba (Brazil)  
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  Language Summary Language Original Title  
  Series Editor Series Title Abbreviated Series Title LNCS  
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  ISSN ISBN 978-3-540-88184-1 Medium  
  Area Expedition Conference GREC  
  Notes DAG Approved no  
  Call Number DAG @ dag @ WLO2008 Serial 1012  
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Author Alfons Juan-Ciscar; Gemma Sanchez edit  openurl
  Title PRIS 2008. Pattern Recognition in Information Systems. Proceedings of the 8th international Workshop on Pattern Recognition in Information systems – PRIS 2008, in conjunction with ICEIS 2008 Type (up) Book Whole
  Year 2008 Publication Abbreviated Journal  
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  Address Barcelona (Spain)  
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  Notes DAG Approved no  
  Call Number DAG @ dag @ JuS2008 Serial 1054  
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Author Dena Bazazian edit  isbn
openurl 
  Title Fully Convolutional Networks for Text Understanding in Scene Images Type (up) Book Whole
  Year 2018 Publication PhD Thesis, Universitat Autonoma de Barcelona-CVC Abbreviated Journal  
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  Abstract Text understanding in scene images has gained plenty of attention in the computer vision community and it is an important task in many applications as text carries semantically rich information about scene content and context. For instance, reading text in a scene can be applied to autonomous driving, scene understanding or assisting visually impaired people. The general aim of scene text understanding is to localize and recognize text in scene images. Text regions are first localized in the original image by a trained detector model and afterwards fed into a recognition module. The tasks of localization and recognition are highly correlated since an inaccurate localization can affect the recognition task.
The main purpose of this thesis is to devise efficient methods for scene text understanding. We investigate how the latest results on deep learning can advance text understanding pipelines. Recently, Fully Convolutional Networks (FCNs) and derived methods have achieved a significant performance on semantic segmentation and pixel level classification tasks. Therefore, we took benefit of the strengths of FCN approaches in order to detect text in natural scenes. In this thesis we have focused on two challenging tasks of scene text understanding which are Text Detection and Word Spotting. For the task of text detection, we have proposed an efficient text proposal technique in scene images. We have considered the Text Proposals method as the baseline which is an approach to reduce the search space of possible text regions in an image. In order to improve the Text Proposals method we combined it with Fully Convolutional Networks to efficiently reduce the number of proposals while maintaining the same level of accuracy and thus gaining a significant speed up. Our experiments demonstrate that this text proposal approach yields significantly higher recall rates than the line based text localization techniques, while also producing better-quality localization. We have also applied this technique on compressed images such as videos from wearable egocentric cameras. For the task of word spotting, we have introduced a novel mid-level word representation method. We have proposed a technique to create and exploit an intermediate representation of images based on text attributes which roughly correspond to character probability maps. Our representation extends the concept of Pyramidal Histogram Of Characters (PHOC) by exploiting Fully Convolutional Networks to derive a pixel-wise mapping of the character distribution within candidate word regions. We call this representation the Soft-PHOC. Furthermore, we show how to use Soft-PHOC descriptors for word spotting tasks through an efficient text line proposal algorithm. To evaluate the detected text, we propose a novel line based evaluation along with the classic bounding box based approach. We test our method on incidental scene text images which comprises real-life scenarios such as urban scenes. The importance of incidental scene text images is due to the complexity of backgrounds, perspective, variety of script and language, short text and little linguistic context. All of these factors together makes the incidental scene text images challenging.
 
  Address November 2018  
  Corporate Author Thesis Ph.D. thesis  
  Publisher Ediciones Graficas Rey Place of Publication Editor Dimosthenis Karatzas;Andrew Bagdanov  
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  ISSN ISBN 978-84-948531-1-1 Medium  
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  Notes DAG; 600.121 Approved no  
  Call Number Admin @ si @ Baz2018 Serial 3220  
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Author Marçal Rusiñol edit  openurl
  Title Geometric and Structural-based Symbol Spotting. Application to Focused Retrieval in Graphic Document Collections Type (up) Book Whole
  Year 2009 Publication PhD Thesis, Universitat Autonoma de Barcelona-CVC Abbreviated Journal  
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  Abstract Usually, pattern recognition systems consist of two main parts. On the one hand, the data acquisition and, on the other hand, the classification of this data on a certain category. In order to recognize which category a certain query element belongs to, a set of pattern models must be provided beforehand. An off-line learning stage is needed to train the classifier and to offer a robust classification of the patterns. Within the pattern recognition field, we are interested in the recognition of graphics and, in particular, on the analysis of documents rich in graphical information. In this context, one of the main concerns is to see if the proposed systems remain scalable with respect to the data volume so as it can handle growing amounts of symbol models. In order to avoid to work with a database of reference symbols, symbol spotting and on-the-fly symbol recognition methods have been introduced in the past years.

Generally speaking, the symbol spotting problem can be defined as the identification of a set of regions of interest from a document image which are likely to contain an instance of a certain queriedn symbol without explicitly applying the whole pattern recognition scheme. Our application framework consists on indexing a collection of graphic-rich document images. This collection is
queried by example with a single instance of the symbol to look for and, by means of symbol spotting methods we retrieve the regions of interest where the symbol is likely to appear within the documents. This kind of applications are known as focused retrieval methods.

In order that the focused retrieval application can handle large collections of documents there is a need to provide an efficient access to the large volume of information that might be stored. We use indexing strategies in order to efficiently retrieve by similarity the locations where a certain part of the symbol appears. In that scenario, graphical patterns should be used as indices for accessing and navigating the collection of documents.
These indexing mechanism allow the user to search for similar elements using graphical information rather than textual queries.

Along this thesis we present a spotting architecture and different methods aiming to build a complete focused retrieval application dealing with a graphic-rich document collections. In addition, a protocol to evaluate the performance of symbol
spotting systems in terms of recognition abilities, location accuracy and scalability is proposed.
 
  Address Barcelona (Spain)  
  Corporate Author Thesis Ph.D. thesis  
  Publisher Ediciones Graficas Rey Place of Publication Editor Josep Llados  
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  Notes DAG Approved no  
  Call Number DAG @ dag @ Rus2009 Serial 1264  
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