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Alfons Juan-Ciscar; Gemma Sanchez |
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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 |
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2008 |
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Barcelona (Spain) |
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DAG @ dag @ JuS2008 |
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1054 |
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Miquel Ferrer; Ernest Valveny; F. Serratosa; K. Riesen; Horst Bunke |
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An Approximate Algorith for Median Graph Computation using Graph Embedding |
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2008 |
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19th International Conference on Pattern Recognition. |
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Tampa, USA |
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DAG @ dag @ FVS2008a |
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1064 |
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Dimosthenis Karatzas; Marçal Rusiñol; Coen Antens; Miquel Ferrer |
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Segmentation Robust to the Vignette Effect for Machine Vision Systems |
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2008 |
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19th International Conference on Pattern Recognition |
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The vignette effect (radial fall-off) is commonly encountered in images obtained through certain image acquisition setups and can seriously hinder automatic analysis processes. In this paper we present a fast and efficient method for dealing with vignetting in the context of object segmentation in an existing industrial inspection setup. The vignette effect is modelled here as a circular, non-linear gradient. The method estimates the gradient parameters and employs them to perform segmentation. Segmentation results on a variety of images indicate that the presented method is able to successfully tackle the vignette effect. |
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Tampa, USA |
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DAG @ dag @ KRA2008 |
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1065 |
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Partha Pratim Roy; Umapada Pal; Josep Llados; F. Kimura |
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Convex Hull based Approach for Multi-oriented Character Recognition form Graphical Documents |
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2008 |
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19th International Conference on Pattern Recognition |
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Tampa (Florida) |
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DAG @ dag @ RPL2008d |
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1073 |
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Jose Antonio Rodriguez; Florent Perronnin; Gemma Sanchez; Josep Llados |
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Unsupervised writer style adaptation for handwritten word spotting |
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2008 |
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Pattern Recognition. 19th International Conference on, IBM Best Student Paper Award. |
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DAG @ dag @ RPS2008 |
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1077 |
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Author |
Partha Pratim Roy; Josep Llados; Umapada Pal |
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A Complete System for Detection and Recognition of Text in Graphical Documents using Background Information |
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2009 |
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5th International Conference on Computer Vision Theory and Applications |
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Lisboa, Portugal |
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978-989-8111-69-2 |
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VISAPP |
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DAG @ dag @ RLP2009 |
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1238 |
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Author |
Dena Bazazian |
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Title |
Fully Convolutional Networks for Text Understanding in Scene Images |
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2018 |
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PhD Thesis, Universitat Autonoma de Barcelona-CVC |
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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. |
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November 2018 |
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Ph.D. thesis |
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Ediciones Graficas Rey |
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Dimosthenis Karatzas;Andrew Bagdanov |
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978-84-948531-1-1 |
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DAG; 600.121 |
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Admin @ si @ Baz2018 |
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3220 |
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Author |
Marçal Rusiñol |
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Geometric and Structural-based Symbol Spotting. Application to Focused Retrieval in Graphic Document Collections |
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2009 |
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PhD Thesis, Universitat Autonoma de Barcelona-CVC |
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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. |
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Barcelona (Spain) |
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Ph.D. thesis |
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Ediciones Graficas Rey |
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Josep Llados |
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DAG @ dag @ Rus2009 |
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1264 |
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Author |
Agnes Borras |
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Contributions to the Content-Based Image Retrieval Using Pictorial Queries |
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2009 |
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PhD Thesis, Universitat Autonoma de Barcelona-CVC |
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The broad access to digital cameras, personal computers and Internet, has lead to the generation of large volumes of data in digital form. If we want an effective usage of this huge amount of data, we need automatic tools to allow the retrieval of relevant information. Image data is a particular type of information that requires specific techniques of description and indexing. The computer vision field that studies these kind of techniques is called Content-Based Image Retrieval (CBIR). Instead of using text-based descriptions, a system of CBIR deals on properties that are inherent in the images themselves. Hence, the feature-based description provides a universal via of image expression in contrast with the more than 6000 languages spoken in the world.
Nowadays, the CBIR is a dynamic focus of research that has derived in important applications for many professional groups. The potential fields of application can be such diverse as: the medical domain, the crime prevention, the protection of the intel- lectual property, the journalism, the graphic design, the web search, the preservation of cultural heritage, etc.
The definition on the role of the user is a key point in the development of a CBIR application. The user is in charge to formulate the queries from which the images are retrieved. We have centered our attention on the image retrieval techniques that use queries based on pictorial information. We have identified a taxonomy composed by four main query paradigms: query-by-selection, query-by-iconic-composition, query- by-sketch and query-by-paint. Each one of these paradigms allows a different degree of user expressivity. From a simple image selection, to a complete painting of the query, the user takes control of the input in the CBIR system.
Along the chapters of this thesis we have analyzed the influence that each query paradigm imposes in the internal operations of a CBIR system. Moreover, we have proposed a set of contributions that we have exemplified in the context of a final application. |
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Barcelona (Spain) |
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Ph.D. thesis |
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Ediciones Graficas Rey |
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Bellaterra |
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Josep Llados |
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DAG @ dag @ Bor2009; IAM @ iam @ Bor2009 |
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1269 |
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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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