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Author Albert Gordo; Florent Perronnin; Ernest Valveny edit   pdf
url  doi
openurl 
  Title (down) Large-scale document image retrieval and classification with runlength histograms and binary embeddings Type Journal Article
  Year 2013 Publication Pattern Recognition Abbreviated Journal PR  
  Volume 46 Issue 7 Pages 1898-1905  
  Keywords visual document descriptor; compression; large-scale; retrieval; classification  
  Abstract We present a new document image descriptor based on multi-scale runlength
histograms. This descriptor does not rely on layout analysis and can be
computed efficiently. We show how this descriptor can achieve state-of-theart
results on two very different public datasets in classification and retrieval
tasks. Moreover, we show how we can compress and binarize these descriptors
to make them suitable for large-scale applications. We can achieve state-ofthe-
art results in classification using binary descriptors of as few as 16 to 64
bits.
 
  Address  
  Corporate Author Thesis  
  Publisher Elsevier Place of Publication Editor  
  Language Summary Language Original Title  
  Series Editor Series Title Abbreviated Series Title  
  Series Volume Series Issue Edition  
  ISSN 0031-3203 ISBN Medium  
  Area Expedition Conference  
  Notes DAG; 600.042; 600.045; 605.203 Approved no  
  Call Number Admin @ si @ GPV2013 Serial 2306  
Permanent link to this record
 

 
Author Bhaskar Chakraborty; Jordi Gonzalez; Xavier Roca edit   pdf
url  doi
openurl 
  Title (down) Large scale continuous visual event recognition using max-margin Hough transformation framework Type Journal Article
  Year 2013 Publication Computer Vision and Image Understanding Abbreviated Journal CVIU  
  Volume 117 Issue 10 Pages 1356–1368  
  Keywords  
  Abstract In this paper we propose a novel method for continuous visual event recognition (CVER) on a large scale video dataset using max-margin Hough transformation framework. Due to high scalability, diverse real environmental state and wide scene variability direct application of action recognition/detection methods such as spatio-temporal interest point (STIP)-local feature based technique, on the whole dataset is practically infeasible. To address this problem, we apply a motion region extraction technique which is based on motion segmentation and region clustering to identify possible candidate “event of interest” as a preprocessing step. On these candidate regions a STIP detector is applied and local motion features are computed. For activity representation we use generalized Hough transform framework where each feature point casts a weighted vote for possible activity class centre. A max-margin frame work is applied to learn the feature codebook weight. For activity detection, peaks in the Hough voting space are taken into account and initial event hypothesis is generated using the spatio-temporal information of the participating STIPs. For event recognition a verification Support Vector Machine is used. An extensive evaluation on benchmark large scale video surveillance dataset (VIRAT) and as well on a small scale benchmark dataset (MSR) shows that the proposed method is applicable on a wide range of continuous visual event recognition applications having extremely challenging conditions.  
  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 1077-3142 ISBN Medium  
  Area Expedition Conference  
  Notes ISE Approved no  
  Call Number Admin @ si @ CGR2013 Serial 2413  
Permanent link to this record
 

 
Author Antoni Gurgui; Debora Gil; Enric Marti edit  url
doi  isbn
openurl 
  Title (down) Laplacian Unitary Domain for Texture Morphing Type Conference Article
  Year 2015 Publication Proceedings of the 10th International Conference on Computer Vision Theory and Applications VISIGRAPP2015 Abbreviated Journal  
  Volume 1 Issue Pages 693-699  
  Keywords Facial; metamorphosis;LaplacianMorphing  
  Abstract Deformation of expressive textures is the gateway to realistic computer synthesis of expressions. By their good mathematical properties and flexible formulation on irregular meshes, most texture mappings rely on solutions to the Laplacian in the cartesian space. In the context of facial expression morphing, this approximation can be seen from the opposite point of view by neglecting the metric. In this paper, we use the properties of the Laplacian in manifolds to present a novel approach to warping expressive facial images in order to generate a morphing between them.  
  Address Munich; Germany; February 2015  
  Corporate Author Thesis  
  Publisher SciTePress Place of Publication Editor  
  Language Summary Language Original Title  
  Series Editor Series Title Abbreviated Series Title  
  Series Volume Series Issue Edition  
  ISSN ISBN 978-989-758-089-5 Medium  
  Area Expedition Conference VISAPP  
  Notes IAM; 600.075 Approved no  
  Call Number Admin @ si @ GGM2015 Serial 2614  
Permanent link to this record
 

 
Author Naveen Onkarappa; Angel Sappa edit  doi
isbn  openurl
  Title (down) Laplacian Derivative based Regularization for Optical Flow Estimation in Driving Scenario Type Conference Article
  Year 2013 Publication 15th International Conference on Computer Analysis of Images and Patterns Abbreviated Journal  
  Volume 8048 Issue Pages 483-490  
  Keywords Optical flow; regularization; Driver Assistance Systems; Performance Evaluation  
  Abstract Existing state of the art optical flow approaches, which are evaluated on standard datasets such as Middlebury, not necessarily have a similar performance when evaluated on driving scenarios. This drop on performance is due to several challenges arising on real scenarios during driving. Towards this direction, in this paper, we propose a modification to the regularization term in a variational optical flow formulation, that notably improves the results, specially in driving scenarios. The proposed modification consists on using the Laplacian derivatives of flow components in the regularization term instead of gradients of flow components. We show the improvements in results on a standard real image sequences dataset (KITTI).  
  Address York; UK; August 2013  
  Corporate Author Thesis  
  Publisher Springer Berlin Heidelberg Place of Publication Editor  
  Language Summary Language Original Title  
  Series Editor Series Title Abbreviated Series Title LNCS  
  Series Volume Series Issue Edition  
  ISSN 0302-9743 ISBN 978-3-642-40245-6 Medium  
  Area Expedition Conference CAIP  
  Notes ADAS; 600.055; 601.215 Approved no  
  Call Number Admin @ si @ OnS2013b Serial 2244  
Permanent link to this record
 

 
Author David Lloret; C. Mariño; Joan Serrat; Antonio Lopez; Juan J. Villanueva edit  openurl
  Title (down) Landmark-based registration of full SLO video sequences. Type Miscellaneous
  Year 2001 Publication Proceedings of the IX Spanish Symposium on Pattern Recognition and Image Analysis,1:189–194. Abbreviated Journal  
  Volume Issue Pages  
  Keywords  
  Abstract  
  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 ADAS Approved no  
  Call Number ADAS @ adas @ LMS2001 Serial 116  
Permanent link to this record
 

 
Author Arturo Fuentes; F. Javier Sanchez; Thomas Voncina; Jorge Bernal edit  doi
openurl 
  Title (down) LAMV: Learning to Predict Where Spectators Look in Live Music Performances Type Conference Article
  Year 2021 Publication 16th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications Abbreviated Journal  
  Volume 5 Issue Pages 500-507  
  Keywords  
  Abstract The advent of artificial intelligence has supposed an evolution on how different daily work tasks are performed. The analysis of cultural content has seen a huge boost by the development of computer-assisted methods that allows easy and transparent data access. In our case, we deal with the automation of the production of live shows, like music concerts, aiming to develop a system that can indicate the producer which camera to show based on what each of them is showing. In this context, we consider that is essential to understand where spectators look and what they are interested in so the computational method can learn from this information. The work that we present here shows the results of a first preliminary study in which we compare areas of interest defined by human beings and those indicated by an automatic system. Our system is based on the extraction of motion textures from dynamic Spatio-Temporal Volumes (STV) and then analyzing the patterns by means of texture analysis techniques. We validate our approach over several video sequences that have been labeled by 16 different experts. Our method is able to match those relevant areas identified by the experts, achieving recall scores higher than 80% when a distance of 80 pixels between method and ground truth is considered. Current performance shows promise when detecting abnormal peaks and movement trends.  
  Address Virtual; February 2021  
  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 VISIGRAPP  
  Notes MV; ISE; 600.119; Approved no  
  Call Number Admin @ si @ FSV2021 Serial 3570  
Permanent link to this record
 

 
Author Naila Murray; Eduard Vazquez edit   pdf
openurl 
  Title (down) Lacuna Restoration: How to choose a neutral colour? Type Conference Article
  Year 2010 Publication Proceedings of The CREATE 2010 Conference Abbreviated Journal  
  Volume Issue Pages 248–252  
  Keywords  
  Abstract Painting restoration which involves filling in material loss (called lacuna) is a complex process. Several standard techniques exist to tackle lacuna restoration,
and this article focuses on those techniques that employ a “neutral” colour to mask the defect. Restoration experts often disagree on the choice of such a colour and in fact, the concept of a neutral colour is controversial. We posit that a neutral colour is one that attracts relatively little visual attention for a specific lacuna. We conducted an eye tracking experiment to compare two common neutral
colour selection methods, specifically the most common local colour and the mean local colour. Results obtained demonstrate that the most common local colour triggers less visual attention in general. Notwithstanding, we have observed instances in which the most common colour triggers a significant amount of attention when subjects spent time resolving their confusion about whether or not a lacuna was part of the painting.
 
  Address Gjovik, Norway  
  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 CREATE  
  Notes CIC Approved no  
  Call Number Admin @ si @ MuV2010 Serial 1297  
Permanent link to this record
 

 
Author Oualid M. Benkarim; Petia Radeva; Laura Igual edit   pdf
doi  isbn
openurl 
  Title (down) Label Consistent Multiclass Discriminative Dictionary Learning for MRI Segmentation Type Conference Article
  Year 2014 Publication 8th Conference on Articulated Motion and Deformable Objects Abbreviated Journal  
  Volume 8563 Issue Pages 138-147  
  Keywords MRI segmentation; sparse representation; discriminative dic- tionary learning; multiclass classi cation  
  Abstract The automatic segmentation of multiple subcortical structures in brain Magnetic Resonance Images (MRI) still remains a challenging task. In this paper, we address this problem using sparse representation and discriminative dictionary learning, which have shown promising results in compression, image denoising and recently in MRI segmentation. Particularly, we use multiclass dictionaries learned from a set of brain atlases to simultaneously segment multiple subcortical structures.
We also impose dictionary atoms to be specialized in one given class using label consistent K-SVD, which can alleviate the bias produced by unbalanced libraries, present when dealing with small structures. The proposed method is compared with other state of the art approaches for the segmentation of the Basal Ganglia of 35 subjects of a public dataset.
The promising results of the segmentation method show the eciency of the multiclass discriminative dictionary learning algorithms in MRI segmentation problems.
 
  Address Palma de Mallorca; July 2014  
  Corporate Author Thesis  
  Publisher Springer International Publishing Place of Publication Editor  
  Language Summary Language Original Title  
  Series Editor Series Title Abbreviated Series Title LNCS  
  Series Volume Series Issue Edition  
  ISSN 0302-9743 ISBN 978-3-319-08848-8 Medium  
  Area Expedition Conference AMDO  
  Notes MILAB; OR Approved no  
  Call Number Admin @ si @ BRI2014 Serial 2494  
Permanent link to this record
 

 
Author Oriol Vicente; Alicia Fornes; Ramon Valdes edit   pdf
isbn  openurl
  Title (down) La Xarxa d Humanitats Digitals de la UABCie: una estructura inteligente para la investigación y la transferencia en Humanidades Type Conference Article
  Year 2017 Publication 3rd Congreso Internacional de Humanidades Digitales Hispánicas. Sociedad Internacional Abbreviated Journal  
  Volume Issue Pages 281-383  
  Keywords  
  Abstract  
  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 978-84-697-5692-8 Medium  
  Area Expedition Conference HDH  
  Notes DAG; 600.121 Approved no  
  Call Number Admin @ si @ VFV2017 Serial 3060  
Permanent link to this record
 

 
Author Alicia Fornes; Josep Llados; Oriol Ramos Terrades; Marçal Rusiñol edit   pdf
openurl 
  Title (down) La Visió per Computador com a Eina per a la Interpretació Automàtica de Fonts Documentals Type Journal
  Year 2016 Publication Lligall, Revista Catalana d'Arxivística Abbreviated Journal  
  Volume 39 Issue Pages 20-46  
  Keywords  
  Abstract  
  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 Approved no  
  Call Number Admin @ si @ FLR2016 Serial 2897  
Permanent link to this record
 

 
Author Robert Benavente; C. Alejandro Parraga; Maria Vanrell edit  url
isbn  openurl
  Title (down) La influencia del contexto en la definicion de las fronteras entre las categorias cromaticas Type Conference Article
  Year 2010 Publication 9th Congreso Nacional del Color Abbreviated Journal  
  Volume Issue Pages 92–95  
  Keywords Categorización del color; Apariencia del color; Influencia del contexto; Patrones de Mondrian; Modelos paramétricos  
  Abstract En este artículo presentamos los resultados de un experimento de categorización de color en el que las muestras se presentaron sobre un fondo multicolor (Mondrian) para simular los efectos del contexto. Los resultados se comparan con los de un experimento previo que, utilizando un paradigma diferente, determinó las fronteras sin tener en cuenta el contexto. El análisis de los resultados muestra que las fronteras obtenidas con el experimento en contexto presentan menos confusión que las obtenidas en el experimento sin contexto.  
  Address Alicante (Spain)  
  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 978-84-9717-144-1 Medium  
  Area Expedition Conference CNC  
  Notes CIC Approved no  
  Call Number CAT @ cat @ BPV2010 Serial 1327  
Permanent link to this record
 

 
Author Jian Yang; Alejandro F. Frangi; Jing-Yu Yang; David Zhang; Zhong Jin edit  openurl
  Title (down) KPCA Plus LDA: A Complete Kernel Fisher Discriminant Framework for Feature Extraction and Recognition Type Journal
  Year 2005 Publication IEEE Transactions on Pattern Analysis and Machine Intelligence, 27(2):230–244 (IF: 3.810) Abbreviated Journal  
  Volume Issue Pages  
  Keywords  
  Abstract  
  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 Approved no  
  Call Number Admin @ si @ YFY2005a Serial 516  
Permanent link to this record
 

 
Author Edgar Riba; D. Mishkin; Daniel Ponsa; E. Rublee; G. Bradski edit   pdf
url  doi
openurl 
  Title (down) Kornia: an Open Source Differentiable Computer Vision Library for PyTorch Type Conference Article
  Year 2020 Publication IEEE Winter Conference on Applications of Computer Vision Abbreviated Journal  
  Volume Issue Pages  
  Keywords  
  Abstract  
  Address Aspen; Colorado; USA; March 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 WACV  
  Notes MSIAU; 600.122; 600.130 Approved no  
  Call Number Admin @ si @ RMP2020 Serial 3291  
Permanent link to this record
 

 
Author Christophe Rigaud; Clement Guerin; Dimosthenis Karatzas; Jean-Christophe Burie; Jean-Marc Ogier edit  doi
openurl 
  Title (down) Knowledge-driven understanding of images in comic books Type Journal Article
  Year 2015 Publication International Journal on Document Analysis and Recognition Abbreviated Journal IJDAR  
  Volume 18 Issue 3 Pages 199-221  
  Keywords Document Understanding; comics analysis; expert system  
  Abstract Document analysis is an active field of research, which can attain a complete understanding of the semantics of a given document. One example of the document understanding process is enabling a computer to identify the key elements of a comic book story and arrange them according to a predefined domain knowledge. In this study, we propose a knowledge-driven system that can interact with bottom-up and top-down information to progressively understand the content of a document. We model the comic book’s and the image processing domains knowledge for information consistency analysis. In addition, different image processing methods are improved or developed to extract panels, balloons, tails, texts, comic characters and their semantic relations in an unsupervised way.  
  Address  
  Corporate Author Thesis  
  Publisher Springer Berlin Heidelberg Place of Publication Editor  
  Language Summary Language Original Title  
  Series Editor Series Title Abbreviated Series Title  
  Series Volume Series Issue Edition  
  ISSN 1433-2833 ISBN Medium  
  Area Expedition Conference  
  Notes DAG; 600.056; 600.077 Approved no  
  Call Number RGK2015 Serial 2595  
Permanent link to this record
 

 
Author B. Gautam; Oriol Ramos Terrades; Joana Maria Pujadas-Mora; Miquel Valls-Figols edit   pdf
url  openurl
  Title (down) Knowledge graph based methods for record linkage Type Journal Article
  Year 2020 Publication Pattern Recognition Letters Abbreviated Journal PRL  
  Volume 136 Issue Pages 127-133  
  Keywords  
  Abstract Nowadays, it is common in Historical Demography the use of individual-level data as a consequence of a predominant life-course approach for the understanding of the demographic behaviour, family transition, mobility, etc. Advanced record linkage is key since it allows increasing the data complexity and its volume to be analyzed. However, current methods are constrained to link data from the same kind of sources. Knowledge graph are flexible semantic representations, which allow to encode data variability and semantic relations in a structured manner.

In this paper we propose the use of knowledge graph methods to tackle record linkage tasks. The proposed method, named WERL, takes advantage of the main knowledge graph properties and learns embedding vectors to encode census information. These embeddings are properly weighted to maximize the record linkage performance. We have evaluated this method on benchmark data sets and we have compared it to related methods with stimulating and satisfactory results.
 
  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 no  
  Call Number Admin @ si @ GRP2020 Serial 3453  
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