Home [171–180] << 181 182 183 184 185 186 187 188 189 190 >> [191–200]
List View
 | 
Citations
 | 
Details
   web
Records
Author Beata Megyesi; Bernhard Esslinger; Alicia Fornes; Nils Kopal; Benedek Lang; George Lasry; Karl de Leeuw; Eva Pettersson; Arno Wacker; Michelle Waldispuhl
Title Decryption of historical manuscripts: the DECRYPT project Type Journal Article
Year 2020 Publication Cryptologia Abbreviated Journal CRYPT
Volume 44 Issue 6 Pages 545-559
Keywords automatic decryption; cipher collection; historical cryptology; image transcription
Abstract Many historians and linguists are working individually and in an uncoordinated fashion on the identification and decryption of historical ciphers. This is a time-consuming process as they often work without access to automatic methods and processes that can accelerate the decipherment. At the same time, computer scientists and cryptologists are developing algorithms to decrypt various cipher types without having access to a large number of original ciphertexts. In this paper, we describe the DECRYPT project aiming at the creation of resources and tools for historical cryptology by bringing the expertise of various disciplines together for collecting data, exchanging methods for faster progress to transcribe, decrypt and contextualize historical encrypted manuscripts. We present our goals and work-in progress of a general approach for analyzing historical encrypted manuscripts using standardized methods and a new set of state-of-the-art tools. We release the data and tools as open-source hoping that all mentioned disciplines would benefit and contribute to the research infrastructure of historical cryptology.
Address
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
Notes DAG; 600.140; 600.121 Approved (down) no
Call Number Admin @ si @ MEF2020 Serial 3347
Permanent link to this record
 
 
Author Anjan Dutta; Pau Riba; Josep Llados; Alicia Fornes
Title Hierarchical Stochastic Graphlet Embedding for Graph-based Pattern Recognition Type Journal Article
Year 2020 Publication Neural Computing and Applications Abbreviated Journal NEUCOMA
Volume 32 Issue Pages 11579–11596
Keywords
Abstract Despite being very successful within the pattern recognition and machine learning community, graph-based methods are often unusable because of the lack of mathematical operations defined in graph domain. Graph embedding, which maps graphs to a vectorial space, has been proposed as a way to tackle these difficulties enabling the use of standard machine learning techniques. However, it is well known that graph embedding functions usually suffer from the loss of structural information. In this paper, we consider the hierarchical structure of a graph as a way to mitigate this loss of information. The hierarchical structure is constructed by topologically clustering the graph nodes and considering each cluster as a node in the upper hierarchical level. Once this hierarchical structure is constructed, we consider several configurations to define the mapping into a vector space given a classical graph embedding, in particular, we propose to make use of the stochastic graphlet embedding (SGE). Broadly speaking, SGE produces a distribution of uniformly sampled low-to-high-order graphlets as a way to embed graphs into the vector space. In what follows, the coarse-to-fine structure of a graph hierarchy and the statistics fetched by the SGE complements each other and includes important structural information with varied contexts. Altogether, these two techniques substantially cope with the usual information loss involved in graph embedding techniques, obtaining a more robust graph representation. This fact has been corroborated through a detailed experimental evaluation on various benchmark graph datasets, where we outperform the state-of-the-art methods.
Address
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
Notes DAG; 600.140; 600.121; 600.141 Approved (down) no
Call Number Admin @ si @ DRL2020 Serial 3348
Permanent link to this record
 
 
Author Pau Riba; Josep Llados; Alicia Fornes
Title Hierarchical graphs for coarse-to-fine error tolerant matching Type Journal Article
Year 2020 Publication Pattern Recognition Letters Abbreviated Journal PRL
Volume 134 Issue Pages 116-124
Keywords Hierarchical graph representation; Coarse-to-fine graph matching; Graph-based retrieval
Abstract During the last years, graph-based representations are experiencing a growing usage in visual recognition and retrieval due to their ability to capture both structural and appearance-based information. Thus, they provide a greater representational power than classical statistical frameworks. However, graph-based representations leads to high computational complexities usually dealt by graph embeddings or approximated matching techniques. Despite their representational power, they are very sensitive to noise and small variations of the input image. With the aim to cope with the time complexity and the variability present in the generated graphs, in this paper we propose to construct a novel hierarchical graph representation. Graph clustering techniques adapted from social media analysis have been used in order to contract a graph at different abstraction levels while keeping information about the topology. Abstract nodes attributes summarise information about the contracted graph partition. For the proposed representations, a coarse-to-fine matching technique is defined. Hence, small graphs are used as a filtering before more accurate matching methods are applied. This approach has been validated in real scenarios such as classification of colour images or retrieval of handwritten words (i.e. word spotting).
Address
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
Notes DAG; 600.097; 601.302; 603.057; 600.140; 600.121 Approved (down) no
Call Number Admin @ si @ RLF2020 Serial 3349
Permanent link to this record
 
 
Author Alicia Fornes; Josep Llados; Joana Maria Pujadas-Mora
Title Browsing of the Social Network of the Past: Information Extraction from Population Manuscript Images Type Book Chapter
Year 2020 Publication Handwritten Historical Document Analysis, Recognition, and Retrieval – State of the Art and Future Trends Abbreviated Journal
Volume Issue Pages
Keywords
Abstract
Address
Corporate Author Thesis
Publisher World Scientific Place of Publication Editor
Language Summary Language Original Title
Series Editor Series Title Abbreviated Series Title
Series Volume Series Issue Edition
ISSN ISBN 978-981-120-323-7 Medium
Area Expedition Conference
Notes DAG; 600.140; 600.121 Approved (down) no
Call Number Admin @ si @ FLP2020 Serial 3350
Permanent link to this record
 
 
Author Joana Maria Pujadas-Mora; Alicia Fornes; Josep Llados; Gabriel Brea-Martinez; Miquel Valls-Figols
Title The Baix Llobregat (BALL) Demographic Database, between Historical Demography and Computer Vision (nineteenth–twentieth centuries Type Book Chapter
Year 2019 Publication Nominative Data in Demographic Research in the East and the West: monograph Abbreviated Journal
Volume Issue Pages 29-61
Keywords
Abstract The Baix Llobregat (BALL) Demographic Database is an ongoing database project containing individual census data from the Catalan region of Baix Llobregat (Spain) during the nineteenth and twentieth centuries. The BALL Database is built within the project ‘NETWORKS: Technology and citizen innovation for building historical social networks to understand the demographic past’ directed by Alícia Fornés from the Center for Computer Vision and Joana Maria Pujadas-Mora from the Center for Demographic Studies, both at the Universitat Autònoma de Barcelona, funded by the Recercaixa program (2017–2019).
Its webpage is http://dag.cvc.uab.es/xarxes/.The aim of the project is to develop technologies facilitating massive digitalization of demographic sources, and more specifically the padrones (local censuses), in order to reconstruct historical ‘social’ networks employing computer vision technology. Such virtual networks can be created thanks to the linkage of nominative records compiled in the local censuses across time and space. Thus, digitized versions of individual and family lifespans are established, and individuals and families can be located spatially.
Address
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 978-5-7996-2656-3 Medium
Area Expedition Conference
Notes DAG; 600.121 Approved (down) no
Call Number Admin @ si @ PFL2019 Serial 3351
Permanent link to this record
 
 
Author Jialuo Chen; M.A.Souibgui; Alicia Fornes; Beata Megyesi
Title A Web-based Interactive Transcription Tool for Encrypted Manuscripts Type Conference Article
Year 2020 Publication 3rd International Conference on Historical Cryptology Abbreviated Journal
Volume Issue Pages 52-59
Keywords
Abstract Manual transcription of handwritten text is a time consuming task. In the case of encrypted manuscripts, the recognition is even more complex due to the huge variety of alphabets and symbol sets. To speed up and ease this process, we present a web-based tool aimed to (semi)-automatically transcribe the encrypted sources. The user uploads one or several images of the desired encrypted document(s) as input, and the system returns the transcription(s). This process is carried out in an interactive fashion with
the user to obtain more accurate results. For discovering and testing, the developed web tool is freely available.
Address Virtual; June 2020
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 HistoCrypt
Notes DAG; 600.140; 602.230; 600.121 Approved (down) no
Call Number Admin @ si @ CSF2020 Serial 3447
Permanent link to this record
 
 
Author Veronica Romero; Emilio Granell; Alicia Fornes; Enrique Vidal; Joan Andreu Sanchez
Title Information Extraction in Handwritten Marriage Licenses Books Type Conference Article
Year 2019 Publication 5th International Workshop on Historical Document Imaging and Processing Abbreviated Journal
Volume Issue Pages 66-71
Keywords
Abstract Handwritten marriage licenses books are characterized by a simple structure of the text in the records with an evolutionary vocabulary, mainly composed of proper names that change along the time. This distinct vocabulary makes automatic transcription and semantic information extraction difficult tasks. Previous works have shown that the use of category-based language models and a Grammatical Inference technique known as MGGI can improve the accuracy of these
tasks. However, the application of the MGGI algorithm requires an a priori knowledge to label the words of the training strings, that is not always easy to obtain. In this paper we study how to automatically obtain the information required by the MGGI algorithm using a technique based on Confusion Networks. Using the resulting language model, full handwritten text recognition and information extraction experiments have been carried out with results supporting the proposed approach.
Address Sydney; Australia; September 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 HIP
Notes DAG; 600.140; 600.121 Approved (down) no
Call Number Admin @ si @ RGF2019 Serial 3352
Permanent link to this record
 
 
Author Manuel Carbonell; Joan Mas; Mauricio Villegas; Alicia Fornes; Josep Llados
Title End-to-End Handwritten Text Detection and Transcription in Full Pages Type Conference Article
Year 2019 Publication 2nd International Workshop on Machine Learning Abbreviated Journal
Volume 5 Issue Pages 29-34
Keywords Handwritten Text Recognition; Layout Analysis; Text segmentation; Deep Neural Networks; Multi-task learning
Abstract When transcribing handwritten document images, inaccuracies in the text segmentation step often cause errors in the subsequent transcription step. For this reason, some recent methods propose to perform the recognition at paragraph level. But still, errors in the segmentation of paragraphs can affect
the transcription performance. In this work, we propose an end-to-end framework to transcribe full pages. The joint text detection and transcription allows to remove the layout analysis requirement at test time. The experimental results show that our approach can achieve comparable results to models that assume
segmented paragraphs, and suggest that joining the two tasks brings an improvement over doing the two tasks separately.
Address Sydney; Australia; September 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 ICDAR WML
Notes DAG; 600.140; 601.311; 600.140 Approved (down) no
Call Number Admin @ si @ CMV2019 Serial 3353
Permanent link to this record
 
 
Author Asma Bensalah; Pau Riba; Alicia Fornes; Josep Llados
Title Shoot less and Sketch more: An Efficient Sketch Classification via Joining Graph Neural Networks and Few-shot Learning Type Conference Article
Year 2019 Publication 13th IAPR International Workshop on Graphics Recognition Abbreviated Journal
Volume Issue Pages 80-85
Keywords Sketch classification; Convolutional Neural Network; Graph Neural Network; Few-shot learning
Abstract With the emergence of the touchpad devices and drawing tablets, a new era of sketching started afresh. However, the recognition of sketches is still a tough task due to the variability of the drawing styles. Moreover, in some application scenarios there is few labelled data available for training,
which imposes a limitation for deep learning architectures. In addition, in many cases there is a need to generate models able to adapt to new classes. In order to cope with these limitations, we propose a method based on few-shot learning and graph neural networks for classifying sketches aiming for an efficient neural model. We test our approach with several databases of
sketches, showing promising results.
Address Sydney; Australia; September 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 GREC
Notes DAG; 600.140; 601.302; 600.121 Approved (down) no
Call Number Admin @ si @ BRF2019 Serial 3354
Permanent link to this record
 
 
Author Pau Riba; Anjan Dutta; Lutz Goldmann; Alicia Fornes; Oriol Ramos Terrades; Josep Llados
Title Table Detection in Invoice Documents by Graph Neural Networks Type Conference Article
Year 2019 Publication 15th International Conference on Document Analysis and Recognition Abbreviated Journal
Volume Issue Pages 122-127
Keywords
Abstract Tabular structures in documents offer a complementary dimension to the raw textual data, representing logical or quantitative relationships among pieces of information. In digital mail room applications, where a large amount of
administrative documents must be processed with reasonable accuracy, the detection and interpretation of tables is crucial. Table recognition has gained interest in document image analysis, in particular in unconstrained formats (absence of rule lines, unknown information of rows and columns). In this work, we propose a graph-based approach for detecting tables in document images. Instead of using the raw content (recognized text), we make use of the location, context and content type, thus it is purely a structure perception approach, not dependent on the language and the quality of the text
reading. Our framework makes use of Graph Neural Networks (GNNs) in order to describe the local repetitive structural information of tables in invoice documents. Our proposed model has been experimentally validated in two invoice datasets and achieved encouraging results. Additionally, due to the scarcity
of benchmark datasets for this task, we have contributed to the community a novel dataset derived from the RVL-CDIP invoice data. It will be publicly released to facilitate future research.
Address Sydney; Australia; September 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 ICDAR
Notes DAG; 600.140; 601.302; 602.167; 600.121; 600.141 Approved (down) no
Call Number Admin @ si @ RDG2019 Serial 3355
Permanent link to this record
 
 
Author Ekta Vats; Anders Hast; Alicia Fornes
Title Training-Free and Segmentation-Free Word Spotting using Feature Matching and Query Expansion Type Conference Article
Year 2019 Publication 15th International Conference on Document Analysis and Recognition Abbreviated Journal
Volume Issue Pages 1294-1299
Keywords Word spotting; Segmentation-free; Trainingfree; Query expansion; Feature matching
Abstract Historical handwritten text recognition is an interesting yet challenging problem. In recent times, deep learning based methods have achieved significant performance in handwritten text recognition. However, handwriting recognition using deep learning needs training data, and often, text must be previously segmented into lines (or even words). These limitations constrain the application of HTR techniques in document collections, because training data or segmented words are not always available. Therefore, this paper proposes a training-free and segmentation-free word spotting approach that can be applied in unconstrained scenarios. The proposed word spotting framework is based on document query word expansion and relaxed feature matching algorithm, which can easily be parallelised. Since handwritten words posses distinct shape and characteristics, this work uses a combination of different keypoint detectors
and Fourier-based descriptors to obtain a sufficient degree of relaxed matching. The effectiveness of the proposed method is empirically evaluated on well-known benchmark datasets using standard evaluation measures. The use of informative features along with query expansion significantly contributed in efficient performance of the proposed method.
Address Sydney; Australia; September 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 ICDAR
Notes DAG; 600.140; 600.121 Approved (down) no
Call Number Admin @ si @ VHF2019 Serial 3356
Permanent link to this record
 
 
Author Debora Gil; Carles Sanchez; Agnes Borras; Marta Diez-Ferrer; Antoni Rosell
Title Segmentation of Distal Airways using Structural Analysis Type Journal Article
Year 2019 Publication PloS one Abbreviated Journal Plos
Volume 14 Issue 12 Pages
Keywords
Abstract Segmentation of airways in Computed Tomography (CT) scans is a must for accurate support of diagnosis and intervention of many pulmonary disorders. In particular, lung cancer diagnosis would benefit from segmentations reaching most distal airways. We present a method that combines descriptors of bronchi local appearance and graph global structural analysis to fine-tune thresholds on the descriptors adapted for each bronchial level. We have compared our method to the top performers of the EXACT09 challenge and to a commercial software for biopsy planning evaluated in an own-collected data-base of high resolution CT scans acquired under different breathing conditions. Results on EXACT09 data show that our method provides a high leakage reduction with minimum loss in airway detection. Results on our data-base show the reliability across varying breathing conditions and a competitive performance for biopsy planning compared to a commercial solution.
Address
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
Notes IAM; 600.139; 600.145 Approved (down) no
Call Number Admin @ si @ GSB2019 Serial 3357
Permanent link to this record
 
 
Author Marta Ligero; Guillermo Torres; Carles Sanchez; Katerine Diaz; Raquel Perez; Debora Gil
Title Selection of Radiomics Features based on their Reproducibility Type Conference Article
Year 2019 Publication 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society Abbreviated Journal
Volume Issue Pages 403-408
Keywords
Abstract Dimensionality reduction is key to alleviate machine learning artifacts in clinical applications with Small Sample Size (SSS) unbalanced datasets. Existing methods rely on either the probabilistic distribution of training data or the discriminant power of the reduced space, disregarding the impact of repeatability and uncertainty in features.In the present study is proposed the use of reproducibility of radiomics features to select features with high inter-class correlation coefficient (ICC). The reproducibility includes the variability introduced in the image acquisition, like medical scans acquisition parameters and convolution kernels, that affects intensity-based features and tumor annotations made by physicians, that influences morphological descriptors of the lesion.For the reproducibility of radiomics features three studies were conducted on cases collected at Vall Hebron Oncology Institute (VHIO) on responders to oncology treatment. The studies focused on the variability due to the convolution kernel, image acquisition parameters, and the inter-observer lesion identification. The features selected were those features with a ICC higher than 0.7 in the three studies.The selected features based on reproducibility were evaluated for lesion malignancy classification using a different database. Results show better performance compared to several state-of-the-art methods including Principal Component Analysis (PCA), Kernel Discriminant Analysis via QR decomposition (KDAQR), LASSO, and an own built Convolutional Neural Network.
Address Berlin; Alemanya; July 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 EMBC
Notes IAM; 600.139; 600.145 Approved (down) no
Call Number Admin @ si @ LTS2019 Serial 3358
Permanent link to this record
 
 
Author Debora Gil; Antonio Esteban Lansaque; Sebastian Stefaniga; Mihail Gaianu; Carles Sanchez
Title Data Augmentation from Sketch Type Conference Article
Year 2019 Publication International Workshop on Uncertainty for Safe Utilization of Machine Learning in Medical Imaging Abbreviated Journal
Volume 11840 Issue Pages 155-162
Keywords Data augmentation; cycleGANs; Multi-objective optimization
Abstract State of the art machine learning methods need huge amounts of data with unambiguous annotations for their training. In the context of medical imaging this is, in general, a very difficult task due to limited access to clinical data, the time required for manual annotations and variability across experts. Simulated data could serve for data augmentation provided that its appearance was comparable to the actual appearance of intra-operative acquisitions. Generative Adversarial Networks (GANs) are a powerful tool for artistic style transfer, but lack a criteria for selecting epochs ensuring also preservation of intra-operative content.

We propose a multi-objective optimization strategy for a selection of cycleGAN epochs ensuring a mapping between virtual images and the intra-operative domain preserving anatomical content. Our approach has been applied to simulate intra-operative bronchoscopic videos and chest CT scans from virtual sketches generated using simple graphical primitives.
Address Shenzhen; China; October 2019
Corporate Author Thesis
Publisher Place of Publication Editor
Language Summary Language Original Title
Series Editor Series Title Abbreviated Series Title LNCS
Series Volume Series Issue Edition
ISSN ISBN Medium
Area Expedition Conference CLIP
Notes IAM; 600.145; 601.337; 600.139; 600.145 Approved (down) no
Call Number Admin @ si @ GES2019 Serial 3359
Permanent link to this record
 
 
Author Carles Sanchez; Miguel Viñas; Coen Antens; Agnes Borras; Debora Gil
Title Back to Front Architecture for Diagnosis as a Service Type Conference Article
Year 2018 Publication 20th International Symposium on Symbolic and Numeric Algorithms for Scientific Computing Abbreviated Journal
Volume Issue Pages 343-346
Keywords
Abstract Software as a Service (SaaS) is a cloud computing model in which a provider hosts applications in a server that customers use via internet. Since SaaS does not require to install applications on customers' own computers, it allows the use by multiple users of highly specialized software without extra expenses for hardware acquisition or licensing. A SaaS tailored for clinical needs not only would alleviate licensing costs, but also would facilitate easy access to new methods for diagnosis assistance. This paper presents a SaaS client-server architecture for Diagnosis as a Service (DaaS). The server is based on docker technology in order to allow execution of softwares implemented in different languages with the highest portability and scalability. The client is a content management system allowing the design of websites with multimedia content and interactive visualization of results allowing user editing. We explain a usage case that uses our DaaS as crowdsourcing platform in a multicentric pilot study carried out to evaluate the clinical benefits of a software for assessment of central airway obstruction.
Address Timisoara; Rumania; September 2018
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 SYNASC
Notes IAM; 600.145 Approved (down) no
Call Number Admin @ si @ SVA2018 Serial 3360
Permanent link to this record