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Author A. Sanfeliu; Juan J. Villanueva
Title An approach of visual motion analysis Type Journal Article
Year 2005 Publication Pattern Recognition Letters Abbreviated Journal PRL
Volume 26 Issue 3 Pages 355–368
Keywords
Abstract IF: 1.138
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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 (up) Approved no
Call Number ISE @ ise @ SaV2005 Serial 561
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Author Fadi Dornaika; Angel Sappa
Title Rigid and Non-rigid Face Motion Tracking by Aligning Texture Maps and Stereo 3D Models Type Journal Article
Year 2007 Publication Pattern Recognition Letters Abbreviated Journal PRL
Volume 28 Issue 15 Pages 2116-2126
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Abstract
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Series Editor Series Title Abbreviated Series Title
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Notes (up) ADAS Approved no
Call Number ADAS @ adas @ DoS2007c Serial 877
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Author Fadi Dornaika; Angel Sappa
Title Instantaneous 3D motion from image derivatives using the Least Trimmed Square Regression Type Journal Article
Year 2009 Publication Pattern Recognition Letters Abbreviated Journal PRL
Volume 30 Issue 5 Pages 535–543
Keywords
Abstract This paper presents a new technique to the instantaneous 3D motion estimation. The main contributions are as follows. First, we show that the 3D camera or scene velocity can be retrieved from image derivatives only assuming that the scene contains a dominant plane. Second, we propose a new robust algorithm that simultaneously provides the Least Trimmed Square solution and the percentage of inliers-the non-contaminated data. Experiments on both synthetic and real image sequences demonstrated the effectiveness of the developed method. Those experiments show that the new robust approach can outperform classical robust schemes.
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Corporate Author Thesis
Publisher Elsevier Science Inc. Place of Publication Editor
Language Summary Language Original Title
Series Editor Series Title Abbreviated Series Title
Series Volume Series Issue Edition
ISSN 0167-8655 ISBN Medium
Area Expedition Conference
Notes (up) ADAS Approved no
Call Number ADAS @ adas @ DoS2009a Serial 1115
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Author Fernando Barrera; Felipe Lumbreras; Angel Sappa
Title Multispectral Piecewise Planar Stereo using Manhattan-World Assumption Type Journal Article
Year 2013 Publication Pattern Recognition Letters Abbreviated Journal PRL
Volume 34 Issue 1 Pages 52-61
Keywords Multispectral stereo rig; Dense disparity maps from multispectral stereo; Color and infrared images
Abstract This paper proposes a new framework for extracting dense disparity maps from a multispectral stereo rig. The system is constructed with an infrared and a color camera. It is intended to explore novel multispectral stereo matching approaches that will allow further extraction of semantic information. The proposed framework consists of three stages. Firstly, an initial sparse disparity map is generated by using a cost function based on feature matching in a multiresolution scheme. Then, by looking at the color image, a set of planar hypotheses is defined to describe the surfaces on the scene. Finally, the previous stages are combined by reformulating the disparity computation as a global minimization problem. The paper has two main contributions. The first contribution combines mutual information with a shape descriptor based on gradient in a multiresolution scheme. The second contribution, which is based on the Manhattan-world assumption, extracts a dense disparity representation using the graph cut algorithm. Experimental results in outdoor scenarios are provided showing the validity of the proposed framework.
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Series Editor Series Title Abbreviated Series Title
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Notes (up) ADAS; 600.054; 600.055; 605.203 Approved no
Call Number Admin @ si @ BLS2013 Serial 2245
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Author Jaume Amores; Petia Radeva
Title Registration and Retrieval of Highly Elastic Bodies using Contextual Information Type Journal Article
Year 2005 Publication Pattern Recognition Letters Abbreviated Journal PRL
Volume 26 Issue 11 Pages 1720–1731
Keywords
Abstract IF: 1.138
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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 (up) ADAS;MILAB Approved no
Call Number ADAS @ adas @ AmR2005b Serial 592
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Author Jaume Amores; N. Sebe; Petia Radeva
Title Boosting the distance estimation: Application to the K-Nearest Neighbor Classifier Type Journal Article
Year 2006 Publication Pattern Recognition Letters Abbreviated Journal PRL
Volume 27 Issue 3 Pages 201–209
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Publisher Place of Publication Editor
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Series Editor Series Title Abbreviated Series Title
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Notes (up) ADAS;MILAB Approved no
Call Number ADAS @ adas @ ASR2006 Serial 643
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Author A. Pujol; Jordi Vitria; Felipe Lumbreras; Juan J. Villanueva
Title Topological principal component analysis for face encoding and recognition Type Journal Article
Year 2001 Publication Pattern Recognition Letters Abbreviated Journal PRL
Volume 22 Issue 6-7 Pages 769–776
Keywords
Abstract IF: 0.552
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Corporate Author Thesis
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 (up) ADAS;OR;MV Approved no
Call Number ADAS @ adas @ PVL2001 Serial 155
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Author Ivet Rafegas; Maria Vanrell; Luis A Alexandre; G. Arias
Title Understanding trained CNNs by indexing neuron selectivity Type Journal Article
Year 2020 Publication Pattern Recognition Letters Abbreviated Journal PRL
Volume 136 Issue Pages 318-325
Keywords
Abstract The impressive performance of Convolutional Neural Networks (CNNs) when solving different vision problems is shadowed by their black-box nature and our consequent lack of understanding of the representations they build and how these representations are organized. To help understanding these issues, we propose to describe the activity of individual neurons by their Neuron Feature visualization and quantify their inherent selectivity with two specific properties. We explore selectivity indexes for: an image feature (color); and an image label (class membership). Our contribution is a framework to seek or classify neurons by indexing on these selectivity properties. It helps to find color selective neurons, such as a red-mushroom neuron in layer Conv4 or class selective neurons such as dog-face neurons in layer Conv5 in VGG-M, and establishes a methodology to derive other selectivity properties. Indexing on neuron selectivity can statistically draw how features and classes are represented through layers in a moment when the size of trained nets is growing and automatic tools to index neurons can be helpful.
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Notes (up) CIC; 600.087; 600.140; 600.118 Approved no
Call Number Admin @ si @ RVL2019 Serial 3310
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Author Oriol Ramos Terrades; Ernest Valveny
Title A new use of the ridgelets transform for describing linear singularities in images Type Journal Article
Year 2006 Publication Pattern Recognition Letters Abbreviated Journal PRL
Volume 27 Issue 6 Pages 587–596
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Abstract
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Publisher Place of Publication Editor
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Notes (up) DAG Approved no
Call Number DAG @ dag @ RaV2006a Serial 635
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Author Miquel Ferrer; Ernest Valveny; F. Serratosa
Title Median graph: A new exact algorithm using a distance based on the maximum common subgraph Type Journal Article
Year 2009 Publication Pattern Recognition Letters Abbreviated Journal PRL
Volume 30 Issue 5 Pages 579–588
Keywords
Abstract Median graphs have been presented as a useful tool for capturing the essential information of a set of graphs. Nevertheless, computation of optimal solutions is a very hard problem. In this work we present a new and more efficient optimal algorithm for the median graph computation. With the use of a particular cost function that permits the definition of the graph edit distance in terms of the maximum common subgraph, and a prediction function in the backtracking algorithm, we reduce the size of the search space, avoiding the evaluation of a great amount of states and still obtaining the exact median. We present a set of experiments comparing our new algorithm against the previous existing exact algorithm using synthetic data. In addition, we present the first application of the exact median graph computation to real data and we compare the results against an approximate algorithm based on genetic search. These experimental results show that our algorithm outperforms the previous existing exact algorithm and in addition show the potential applicability of the exact solutions to real problems.
Address
Corporate Author Thesis
Publisher Elsevier Science Inc. Place of Publication Editor
Language Summary Language Original Title
Series Editor Series Title Abbreviated Series Title
Series Volume Series Issue Edition
ISSN 0167-8655 ISBN Medium
Area Expedition Conference
Notes (up) DAG Approved no
Call Number DAG @ dag @ FVS2009a Serial 1114
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Author Marçal Rusiñol; Agnes Borras; Josep Llados
Title Relational Indexing of Vectorial Primitives for Symbol Spotting in Line-Drawing Images Type Journal Article
Year 2010 Publication Pattern Recognition Letters Abbreviated Journal PRL
Volume 31 Issue 3 Pages 188–201
Keywords Document image analysis and recognition, Graphics recognition, Symbol spotting ,Vectorial representations, Line-drawings
Abstract This paper presents a symbol spotting approach for indexing by content a database of line-drawing images. As line-drawings are digital-born documents designed by vectorial softwares, instead of using a pixel-based approach, we present a spotting method based on vector primitives. Graphical symbols are represented by a set of vectorial primitives which are described by an off-the-shelf shape descriptor. A relational indexing strategy aims to retrieve symbol locations into the target documents by using a combined numerical-relational description of 2D structures. The zones which are likely to contain the queried symbol are validated by a Hough-like voting scheme. In addition, a performance evaluation framework for symbol spotting in graphical documents is proposed. The presented methodology has been evaluated with a benchmarking set of architectural documents achieving good performance results.
Address
Corporate Author Thesis
Publisher Elsevier 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 (up) DAG Approved no
Call Number DAG @ dag @ RBL2010 Serial 1177
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Author Jose Antonio Rodriguez; Florent Perronnin; Gemma Sanchez; Josep Llados
Title Unsupervised writer adaptation of whole-word HMMs with application to word-spotting Type Journal Article
Year 2010 Publication Pattern Recognition Letters Abbreviated Journal PRL
Volume 31 Issue 8 Pages 742–749
Keywords Word-spotting; Handwriting recognition; Writer adaptation; Hidden Markov model; Document analysis
Abstract In this paper we propose a novel approach for writer adaptation in a handwritten word-spotting task. The method exploits the fact that the semi-continuous hidden Markov model separates the word model parameters into (i) a codebook of shapes and (ii) a set of word-specific parameters.

Our main contribution is to employ this property to derive writer-specific word models by statistically adapting an initial universal codebook to each document. This process is unsupervised and does not even require the appearance of the keyword(s) in the searched document. Experimental results show an increase in performance when this adaptation technique is applied. To the best of our knowledge, this is the first work dealing with adaptation for word-spotting. The preliminary version of this paper obtained an IBM Best Student Paper Award at the 19th International Conference on Pattern Recognition.
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Corporate Author Thesis
Publisher Elsevier Place of Publication Editor
Language Summary Language Original Title
Series Editor Series Title Abbreviated Series Title
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ISSN ISBN Medium
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Notes (up) DAG Approved no
Call Number DAG @ dag @ RPS2010 Serial 1290
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Author Jaume Gibert; Ernest Valveny; Horst Bunke
Title Feature Selection on Node Statistics Based Embedding of Graphs Type Journal Article
Year 2012 Publication Pattern Recognition Letters Abbreviated Journal PRL
Volume 33 Issue 15 Pages 1980–1990
Keywords Structural pattern recognition; Graph embedding; Feature ranking; PCA; Graph classification
Abstract Representing a graph with a feature vector is a common way of making statistical machine learning algorithms applicable to the domain of graphs. Such a transition from graphs to vectors is known as graphembedding. A key issue in graphembedding is to select a proper set of features in order to make the vectorial representation of graphs as strong and discriminative as possible. In this article, we propose features that are constructed out of frequencies of node label representatives. We first build a large set of features and then select the most discriminative ones according to different ranking criteria and feature transformation algorithms. On different classification tasks, we experimentally show that only a small significant subset of these features is needed to achieve the same classification rates as competing to state-of-the-art methods.
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Series Editor Series Title Abbreviated Series Title
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Notes (up) DAG Approved no
Call Number Admin @ si @ GVB2012b Serial 1993
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Author Lluis Gomez; Ali Furkan Biten; Ruben Tito; Andres Mafla; Marçal Rusiñol; Ernest Valveny; Dimosthenis Karatzas
Title Multimodal grid features and cell pointers for scene text visual question answering Type Journal Article
Year 2021 Publication Pattern Recognition Letters Abbreviated Journal PRL
Volume 150 Issue Pages 242-249
Keywords
Abstract This paper presents a new model for the task of scene text visual question answering. In this task questions about a given image can only be answered by reading and understanding scene text. Current state of the art models for this task make use of a dual attention mechanism in which one attention module attends to visual features while the other attends to textual features. A possible issue with this is that it makes difficult for the model to reason jointly about both modalities. To fix this problem we propose a new model that is based on an single attention mechanism that attends to multi-modal features conditioned to the question. The output weights of this attention module over a grid of multi-modal spatial features are interpreted as the probability that a certain spatial location of the image contains the answer text to the given question. Our experiments demonstrate competitive performance in two standard datasets with a model that is faster than previous methods at inference time. Furthermore, we also provide a novel analysis of the ST-VQA dataset based on a human performance study. Supplementary material, code, and data is made available through this link.
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Notes (up) DAG; 600.084; 600.121 Approved no
Call Number Admin @ si @ GBT2021 Serial 3620
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Author Dena Bazazian; Raul Gomez; Anguelos Nicolaou; Lluis Gomez; Dimosthenis Karatzas; Andrew Bagdanov
Title Fast: Facilitated and accurate scene text proposals through fcn guided pruning Type Journal Article
Year 2019 Publication Pattern Recognition Letters Abbreviated Journal PRL
Volume 119 Issue Pages 112-120
Keywords
Abstract Class-specific text proposal algorithms can efficiently reduce the search space for possible text object locations in an image. In this paper we combine the Text Proposals algorithm with Fully Convolutional Networks to efficiently reduce the number of proposals while maintaining the same recall level and thus gaining a significant speed up. Our experiments demonstrate that such text proposal approaches yield significantly higher recall rates than state-of-the-art text localization techniques, while also producing better-quality localizations. Our results on the ICDAR 2015 Robust Reading Competition (Challenge 4) and the COCO-text datasets show that, when combined with strong word classifiers, this recall margin leads to state-of-the-art results in end-to-end scene text recognition.
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Notes (up) DAG; 600.084; 600.121; 600.129 Approved no
Call Number Admin @ si @ BGN2019 Serial 3342
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