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Author Lluis Pere de las Heras; Joan Mas; Gemma Sanchez; Ernest Valveny edit  openurl
  Title Descriptor-based Svm Wall Detector Type Conference Article
  Year 2011 Publication (up) 9th International Workshop on Graphic Recognition Abbreviated Journal  
  Volume Issue Pages  
  Keywords  
  Abstract Architectural floorplans exhibit a large variability in notation. Therefore, segmenting and identifying the elements of any kind of plan becomes a challenging task for approaches based on grouping structural primitives obtained by vectorization. Recently, a patch-based segmentation method working at pixel level and relying on the construction of a visual vocabulary has been proposed showing its adaptability to different notations by automatically learning the visual appearance of the elements in each different notation. In this paper we describe an evolution of this new approach in two directions: firstly we evaluate different features to obtain the description of every patch. Secondly, we train an SVM classifier to obtain the category of every patch instead of constructing a visual vocabulary. These modifications of the method have been tested for wall detection on two datasets of architectural floorplans with different notations and compared with the results obtained with the original approach.  
  Address  
  Corporate Author Thesis  
  Publisher Place of Publication Editor  
  Language Summary Language Original Title  
  Series Editor Series Title Abbreviated Series Title  
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  ISSN ISBN Medium  
  Area Expedition Conference GREC  
  Notes DAG Approved no  
  Call Number Admin @ si @ HMS2011b Serial 1819  
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Author Y. Patel; Lluis Gomez; Marçal Rusiñol; Dimosthenis Karatzas; C.V. Jawahar edit   pdf
url  doi
openurl 
  Title Self-Supervised Visual Representations for Cross-Modal Retrieval Type Conference Article
  Year 2019 Publication (up) ACM International Conference on Multimedia Retrieval Abbreviated Journal  
  Volume Issue Pages 182–186  
  Keywords  
  Abstract Cross-modal retrieval methods have been significantly improved in last years with the use of deep neural networks and large-scale annotated datasets such as ImageNet and Places. However, collecting and annotating such datasets requires a tremendous amount of human effort and, besides, their annotations are limited to discrete sets of popular visual classes that may not be representative of the richer semantics found on large-scale cross-modal retrieval datasets. In this paper, we present a self-supervised cross-modal retrieval framework that leverages as training data the correlations between images and text on the entire set of Wikipedia articles. Our method consists in training a CNN to predict: (1) the semantic context of the article in which an image is more probable to appear as an illustration, and (2) the semantic context of its caption. Our experiments demonstrate that the proposed method is not only capable of learning discriminative visual representations for solving vision tasks like classification, but that the learned representations are better for cross-modal retrieval when compared to supervised pre-training of the network on the ImageNet dataset.  
  Address Otawa; Canada; june 2019  
  Corporate Author Thesis  
  Publisher Place of Publication Editor  
  Language Summary Language Original Title  
  Series Editor Series Title Abbreviated Series Title  
  Series Volume Series Issue Edition  
  ISSN ISBN Medium  
  Area Expedition Conference ICMR  
  Notes DAG; 600.121; 600.129 Approved no  
  Call Number Admin @ si @ PGR2019 Serial 3288  
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Author Mohamed Ali Souibgui; Asma Bensalah; Jialuo Chen; Alicia Fornes; Michelle Waldispühl edit  url
doi  openurl
  Title A User Perspective on HTR methods for the Automatic Transcription of Rare Scripts: The Case of Codex Runicus Just Accepted Type Journal Article
  Year 2023 Publication (up) ACM Journal on Computing and Cultural Heritage Abbreviated Journal JOCCH  
  Volume 15 Issue 4 Pages 1-18  
  Keywords  
  Abstract Recent breakthroughs in Artificial Intelligence, Deep Learning and Document Image Analysis and Recognition have significantly eased the creation of digital libraries and the transcription of historical documents. However, for documents in rare scripts with few labelled training data available, current Handwritten Text Recognition (HTR) systems are too constraint. Moreover, research on HTR often focuses on technical aspects only, and rarely puts emphasis on implementing software tools for scholars in Humanities. In this article, we describe, compare and analyse different transcription methods for rare scripts. We evaluate their performance in a real use case of a medieval manuscript written in the runic script (Codex Runicus) and discuss advantages and disadvantages of each method from the user perspective. From this exhaustive analysis and comparison with a fully manual transcription, we raise conclusions and provide recommendations to scholars interested in using automatic transcription tools.  
  Address  
  Corporate Author Thesis  
  Publisher ACM Place of Publication Editor  
  Language Summary Language Original Title  
  Series Editor Series Title Abbreviated Series Title  
  Series Volume Series Issue Edition  
  ISSN ISBN Medium  
  Area Expedition Conference  
  Notes DAG; 600.121; 600.162; 602.230; 600.140 Approved no  
  Call Number Admin @ si @ SBC2023 Serial 3732  
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Author S.Chanda; Umapada Pal; Oriol Ramos Terrades edit  doi
openurl 
  Title Word-Wise Thai and Roman Script Identification Type Journal
  Year 2009 Publication (up) ACM Transactions on Asian Language Information Processing Abbreviated Journal TALIP  
  Volume 8 Issue 3 Pages 1-21  
  Keywords  
  Abstract In some Thai documents, a single text line of a printed document page may contain words of both Thai and Roman scripts. For the Optical Character Recognition (OCR) of such a document page it is better to identify, at first, Thai and Roman script portions and then to use individual OCR systems of the respective scripts on these identified portions. In this article, an SVM-based method is proposed for identification of word-wise printed Roman and Thai scripts from a single line of a document page. Here, at first, the document is segmented into lines and then lines are segmented into character groups (words). In the proposed scheme, we identify the script of a character group combining different character features obtained from structural shape, profile behavior, component overlapping information, topological properties, and water reservoir concept, etc. Based on the experiment on 10,000 data (words) we obtained 99.62% script identification accuracy from the proposed scheme.  
  Address  
  Corporate Author Thesis  
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  Language Summary Language Original Title  
  Series Editor Series Title Abbreviated Series Title  
  Series Volume Series Issue Edition  
  ISSN 1530-0226 ISBN Medium  
  Area Expedition Conference  
  Notes DAG Approved no  
  Call Number Admin @ si @ CPR2009f Serial 1869  
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Author Sounak Dey; Anjan Dutta; Juan Ignacio Toledo; Suman Ghosh; Josep Llados; Umapada Pal edit   pdf
url  openurl
  Title SigNet: Convolutional Siamese Network for Writer Independent Offline Signature Verification Type Miscellaneous
  Year 2018 Publication (up) Arxiv Abbreviated Journal  
  Volume Issue Pages  
  Keywords  
  Abstract Offline signature verification is one of the most challenging tasks in biometrics and document forensics. Unlike other verification problems, it needs to model minute but critical details between genuine and forged signatures, because a skilled falsification might often resembles the real signature with small deformation. This verification task is even harder in writer independent scenarios which is undeniably fiscal for realistic cases. In this paper, we model an offline writer independent signature verification task with a convolutional Siamese network. Siamese networks are twin networks with shared weights, which can be trained to learn a feature space where similar observations are placed in proximity. This is achieved by exposing the network to a pair of similar and dissimilar observations and minimizing the Euclidean distance between similar pairs while simultaneously maximizing it between dissimilar pairs. Experiments conducted on cross-domain datasets emphasize the capability of our network to model forgery in different languages (scripts) and handwriting styles. Moreover, our designed Siamese network, named SigNet, exceeds the state-of-the-art results on most of the benchmark signature datasets, which paves the way for further research in this direction.  
  Address  
  Corporate Author Thesis  
  Publisher Place of Publication Editor  
  Language Summary Language Original Title  
  Series Editor Series Title Abbreviated Series Title  
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  ISSN ISBN Medium  
  Area Expedition Conference  
  Notes DAG; 600.097; 600.121 Approved no  
  Call Number Admin @ si @ DDT2018 Serial 3085  
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Author Y. Patel; Lluis Gomez; Raul Gomez; Marçal Rusiñol; Dimosthenis Karatzas; C.V. Jawahar edit  openurl
  Title TextTopicNet-Self-Supervised Learning of Visual Features Through Embedding Images on Semantic Text Spaces Type Miscellaneous
  Year 2018 Publication (up) Arxiv Abbreviated Journal  
  Volume Issue Pages  
  Keywords  
  Abstract The immense success of deep learning based methods in computer vision heavily relies on large scale training datasets. These richly annotated datasets help the network learn discriminative visual features. Collecting and annotating such datasets requires a tremendous amount of human effort and annotations are limited to popular set of classes. As an alternative, learning visual features by designing auxiliary tasks which make use of freely available self-supervision has become increasingly popular in the computer vision community.
In this paper, we put forward an idea to take advantage of multi-modal context to provide self-supervision for the training of computer vision algorithms. We show that adequate visual features can be learned efficiently by training a CNN to predict the semantic textual context in which a particular image is more probable to appear as an illustration. More specifically we use popular text embedding techniques to provide the self-supervision for the training of deep CNN.		  
  Address  
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  Publisher Place of Publication Editor  
  Language Summary Language Original Title  
  Series Editor Series Title Abbreviated Series Title  
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  Area Expedition Conference  
  Notes DAG; 600.084; 601.338; 600.121 Approved no  
  Call Number Admin @ si @ PGG2018 Serial 3177  
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Author Francisco Cruz; Oriol Ramos Terrades edit  openurl
  Title A probabilistic framework for handwritten text line segmentation Type Miscellaneous
  Year 2018 Publication (up) Arxiv Abbreviated Journal  
  Volume Issue Pages  
  Keywords Document Analysis; Text Line Segmentation; EM algorithm; Probabilistic Graphical Models; Parameter Learning  
  Abstract We successfully combine Expectation-Maximization algorithm and variational
approaches for parameter learning and computing inference on Markov random fields. This is a general method that can be applied to many computer
vision tasks. In this paper, we apply it to handwritten text line segmentation.
We conduct several experiments that demonstrate that our method deal with
common issues of this task, such as complex document layout or non-latin
scripts. The obtained results prove that our method achieve state-of-theart performance on different benchmark datasets without any particular fine
tuning step.		  
  Address  
  Corporate Author Thesis  
  Publisher Place of Publication Editor  
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  Area Expedition Conference  
  Notes DAG; 600.097; 600.121 Approved no  
  Call Number Admin @ si @ CrR2018 Serial 3253  
Permanent link to this record
 
 
Author Oriol Ramos Terrades; Albert Berenguel; Debora Gil edit   pdf
url  openurl
  Title A flexible outlier detector based on a topology given by graph communities Type Miscellaneous
  Year 2020 Publication (up) Arxiv Abbreviated Journal  
  Volume Issue Pages  
  Keywords  
  Abstract Outlier, or anomaly, detection is essential for optimal performance of machine learning methods and statistical predictive models. It is not just a technical step in a data cleaning process but a key topic in many fields such as fraudulent document detection, in medical applications and assisted diagnosis systems or detecting security threats. In contrast to population-based methods, neighborhood based local approaches are simple flexible methods that have the potential to perform well in small sample size unbalanced problems. However, a main concern of local approaches is the impact that the computation of each sample neighborhood has on the method performance. Most approaches use a distance in the feature space to define a single neighborhood that requires careful selection of several parameters. This work presents a local approach based on a local measure of the heterogeneity of sample labels in the feature space considered as a topological manifold. Topology is computed using the communities of a weighted graph codifying mutual nearest neighbors in the feature space. This way, we provide with a set of multiple neighborhoods able to describe the structure of complex spaces without parameter fine tuning. The extensive experiments on real-world data sets show that our approach overall outperforms, both, local and global strategies in multi and single view settings.  
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  Notes IAM; DAG; 600.139; 600.145; 600.140; 600.121 Approved no  
  Call Number Admin @ si @ RBG2020 Serial 3475  
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Author Pau Riba; Andreas Fischer; Josep Llados; Alicia Fornes edit   pdf
url  openurl
  Title Learning Graph Edit Distance by Graph NeuralNetworks Type Miscellaneous
  Year 2020 Publication (up) Arxiv Abbreviated Journal  
  Volume Issue Pages  
  Keywords  
  Abstract The emergence of geometric deep learning as a novel framework to deal with graph-based representations has faded away traditional approaches in favor of completely new methodologies. In this paper, we propose a new framework able to combine the advances on deep metric learning with traditional approximations of the graph edit distance. Hence, we propose an efficient graph distance based on the novel field of geometric deep learning. Our method employs a message passing neural network to capture the graph structure, and thus, leveraging this information for its use on a distance computation. The performance of the proposed graph distance is validated on two different scenarios. On the one hand, in a graph retrieval of handwritten words~\ie~keyword spotting, showing its superior performance when compared with (approximate) graph edit distance benchmarks. On the other hand, demonstrating competitive results for graph similarity learning when compared with the current state-of-the-art on a recent benchmark dataset.  
  Address  
  Corporate Author Thesis  
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  Language Summary Language Original Title  
  Series Editor Series Title Abbreviated Series Title  
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  ISSN ISBN Medium  
  Area Expedition Conference  
  Notes DAG; 600.121; 600.140; 601.302 Approved no  
  Call Number Admin @ si @ RFL2020 Serial 3555  
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Author Pau Riba; Sounak Dey; Ali Furkan Biten; Josep Llados edit   pdf
openurl 
  Title Localizing Infinity-shaped fishes: Sketch-guided object localization in the wild Type Miscellaneous
  Year 2021 Publication (up) Arxiv Abbreviated Journal  
  Volume Issue Pages  
  Keywords  
  Abstract This work investigates the problem of sketch-guided object localization (SGOL), where human sketches are used as queries to conduct the object localization in natural images. In this cross-modal setting, we first contribute with a tough-to-beat baseline that without any specific SGOL training is able to outperform the previous works on a fixed set of classes. The baseline is useful to analyze the performance of SGOL approaches based on available simple yet powerful methods. We advance prior arts by proposing a sketch-conditioned DETR (DEtection TRansformer) architecture which avoids a hard classification and alleviates the domain gap between sketches and images to localize object instances. Although the main goal of SGOL is focused on object detection, we explored its natural extension to sketch-guided instance segmentation. This novel task allows to move towards identifying the objects at pixel level, which is of key importance in several applications. We experimentally demonstrate that our model and its variants significantly advance over previous state-of-the-art results. All training and testing code of our model will be released to facilitate future researchhttps://github.com/priba/sgol_wild.  
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  Series Editor Series Title Abbreviated Series Title  
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  ISSN ISBN Medium  
  Area Expedition Conference  
  Notes DAG; 600.121 Approved no  
  Call Number Admin @ si @ RDB2021 Serial 3674  
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