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Author Hongxing Gao; Marçal Rusiñol; Dimosthenis Karatzas; Apostolos Antonacopoulos; Josep Llados
Title An interactive appearance-based document retrieval system for historical newspapers Type Conference Article
Year 2013 Publication Proceedings of the International Conference on Computer Vision Theory and Applications Abbreviated Journal
Volume Issue Pages 84-87
Keywords
Abstract In this paper we present a retrieval-based application aimed at assisting a user to semi-automatically segment an incoming flow of historical newspaper images by automatically detecting a particular type of pages based on their appearance. A visual descriptor is used to assess page similarity while a relevance feedback process allow refining the results iteratively. The application is tested on a large dataset of digitised historic newspapers.
Address Barcelona; February 2013
Corporate Author Thesis (up)
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 VISAPP
Notes DAG; 600.056; 600.045; 605.203 Approved no
Call Number Admin @ si @ GRK2013a Serial 2290
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Author Jaume Gibert; Ernest Valveny; Horst Bunke
Title Embedding of Graphs with Discrete Attributes Via Label Frequencies Type Journal Article
Year 2013 Publication International Journal of Pattern Recognition and Artificial Intelligence Abbreviated Journal IJPRAI
Volume 27 Issue 3 Pages 1360002-1360029
Keywords Discrete attributed graphs; graph embedding; graph classification
Abstract Graph-based representations of patterns are very flexible and powerful, but they are not easily processed due to the lack of learning algorithms in the domain of graphs. Embedding a graph into a vector space solves this problem since graphs are turned into feature vectors and thus all the statistical learning machinery becomes available for graph input patterns. In this work we present a new way of embedding discrete attributed graphs into vector spaces using node and edge label frequencies. The methodology is experimentally tested on graph classification problems, using patterns of different nature, and it is shown to be competitive to state-of-the-art classification algorithms for graphs, while being computationally much more efficient.
Address
Corporate Author Thesis (up)
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 Approved no
Call Number Admin @ si @ GVB2013 Serial 2305
Permanent link to this record
 
 
Author Muhammad Muzzamil Luqman; Jean-Yves Ramel; Josep Llados
Title Multilevel Analysis of Attributed Graphs for Explicit Graph Embedding in Vector Spaces Type Book Chapter
Year 2013 Publication Graph Embedding for Pattern Analysis Abbreviated Journal
Volume Issue Pages 1-26
Keywords
Abstract Ability to recognize patterns is among the most crucial capabilities of human beings for their survival, which enables them to employ their sophisticated neural and cognitive systems [1], for processing complex audio, visual, smell, touch, and taste signals. Man is the most complex and the best existing system of pattern recognition. Without any explicit thinking, we continuously compare, classify, and identify huge amount of signal data everyday [2], starting from the time we get up in the morning till the last second we fall asleep. This includes recognizing the face of a friend in a crowd, a spoken word embedded in noise, the proper key to lock the door, smell of coffee, the voice of a favorite singer, the recognition of alphabetic characters, and millions of more tasks that we perform on regular basis.
Address
Corporate Author Thesis (up)
Publisher Springer New York Place of Publication Editor
Language Summary Language Original Title
Series Editor Series Title Abbreviated Series Title
Series Volume Series Issue Edition
ISSN ISBN 978-1-4614-4456-5 Medium
Area Expedition Conference
Notes DAG Approved no
Call Number Admin @ si @ LRL2013b Serial 2271
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Author Jean-Marc Ogier; Wenyin Liu; Josep Llados (eds)
Title Graphics Recognition: Achievements, Challenges, and Evolution Type Book Whole
Year 2010 Publication 8th International Workshop GREC 2009. Abbreviated Journal
Volume 6020 Issue Pages
Keywords
Abstract
Address La Rochelle
Corporate Author Thesis (up)
Publisher Springer Link Place of Publication Editor Jean-Marc Ogier; Wenyin Liu; Josep Llados
Language Summary Language Original Title
Series Editor Series Title Lecture Notes in Computer Science Abbreviated Series Title LNCS
Series Volume Series Issue Edition
ISSN ISBN 978-3-642-13727-3 Medium
Area Expedition Conference GREC
Notes DAG Approved no
Call Number Admin @ si @ OLL2010 Serial 1976
Permanent link to this record
 
 
Author Isabel Guitart; Jordi Conesa; Luis Villarejo; Agata Lapedriza; David Masip; Antoni Perez; Elena Planas
Title Opinion Mining on Educational Resources at the Open University of Catalonia Type Conference Article
Year 2013 Publication 3rd International Workshop on Adaptive Learning via Interactive, Collaborative and Emotional approaches. In conjunction with CISIS 2013: The 7th International Conference on Complex, Intelligent, and Software Intensive Systems Abbreviated Journal
Volume Issue Pages 385 - 390
Keywords
Abstract In order to make improvements to teaching, it is vital to know what students think of the way they are taught. With that purpose in mind, exhaustively analyzing the forums associated with the subjects taught at the Universitat Oberta de Cataluya (UOC) would be extremely helpful, as the university's students often post comments on their learning experiences in them. Exploiting the content of such forums is not a simple undertaking. The volume of data involved is very large, and performing the task manually would require a great deal of effort from lecturers. As a first step to solve this problem, we propose a tool to automatically analyze the posts in forums of communities of UOC students and teachers, with a view to systematically mining the opinions they contain. This article defines the architecture of such tool and explains how lexical-semantic and language technology resources can be used to that end. For pilot testing purposes, the tool has been used to identify students' opinions on the UOC's Business Intelligence master's degree course during the last two years. The paper discusses the results of such test. The contribution of this paper is twofold. Firstly, it demonstrates the feasibility of using natural language parsing techniques to help teachers to make decisions. Secondly, it introduces a simple tool that can be refined and adapted to a virtual environment for the purpose in question.
Address
Corporate Author Thesis (up)
Publisher Place of Publication Editor
Language Summary Language Original Title
Series Editor Series Title Abbreviated Series Title
Series Volume Series Issue Edition
ISSN ISBN 978-0-7695-4992-7 Medium
Area Expedition Conference ALICE
Notes OR;MV Approved no
Call Number GCV2013 Serial 2268
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Author Jose Manuel Alvarez; Theo Gevers; Ferran Diego; Antonio Lopez
Title Road Geometry Classification by Adaptative Shape Models Type Journal Article
Year 2013 Publication IEEE Transactions on Intelligent Transportation Systems Abbreviated Journal TITS
Volume 14 Issue 1 Pages 459-468
Keywords road detection
Abstract Vision-based road detection is important for different applications in transportation, such as autonomous driving, vehicle collision warning, and pedestrian crossing detection. Common approaches to road detection are based on low-level road appearance (e.g., color or texture) and neglect of the scene geometry and context. Hence, using only low-level features makes these algorithms highly depend on structured roads, road homogeneity, and lighting conditions. Therefore, the aim of this paper is to classify road geometries for road detection through the analysis of scene composition and temporal coherence. Road geometry classification is proposed by building corresponding models from training images containing prototypical road geometries. We propose adaptive shape models where spatial pyramids are steered by the inherent spatial structure of road images. To reduce the influence of lighting variations, invariant features are used. Large-scale experiments show that the proposed road geometry classifier yields a high recognition rate of 73.57% ± 13.1, clearly outperforming other state-of-the-art methods. Including road shape information improves road detection results over existing appearance-based methods. Finally, it is shown that invariant features and temporal information provide robustness against disturbing imaging conditions.
Address
Corporate Author Thesis (up)
Publisher Place of Publication Editor
Language Summary Language Original Title
Series Editor Series Title Abbreviated Series Title
Series Volume Series Issue Edition
ISSN 1524-9050 ISBN Medium
Area Expedition Conference
Notes ADAS;ISE Approved no
Call Number Admin @ si @ AGD2013;; ADAS @ adas @ Serial 2269
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Author Marçal Rusiñol; R.Roset; Josep Llados; C.Montaner
Title Automatic Index Generation of Digitized Map Series by Coordinate Extraction and Interpretation Type Conference Article
Year 2011 Publication In Proceedings of the Sixth International Workshop on Digital Technologies in Cartographic Heritage Abbreviated Journal
Volume Issue Pages
Keywords
Abstract
Address
Corporate Author Thesis (up)
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 CartoHerit
Notes DAG Approved no
Call Number Admin @ si @ RRL2011b Serial 1978
Permanent link to this record
 
 
Author Yainuvis Socarras; David Vazquez; Antonio Lopez; David Geronimo; Theo Gevers
Title Improving HOG with Image Segmentation: Application to Human Detection Type Conference Article
Year 2012 Publication 11th International Conference on Advanced Concepts for Intelligent Vision Systems Abbreviated Journal
Volume 7517 Issue Pages 178-189
Keywords Segmentation; Pedestrian Detection
Abstract In this paper we improve the histogram of oriented gradients (HOG), a core descriptor of state-of-the-art object detection, by the use of higher-level information coming from image segmentation. The idea is to re-weight the descriptor while computing it without increasing its size. The benefits of the proposal are two-fold: (i) to improve the performance of the detector by enriching the descriptor information and (ii) take advantage of the information of image segmentation, which in fact is likely to be used in other stages of the detection system such as candidate generation or refinement.
We test our technique in the INRIA person dataset, which was originally developed to test HOG, embedding it in a human detection system. The well-known segmentation method, mean-shift (from smaller to larger super-pixels), and different methods to re-weight the original descriptor (constant, region-luminance, color or texture-dependent) has been evaluated. We achieve performance improvements of 4:47% in detection rate through the use of differences of color between contour pixel neighborhoods as re-weighting function.
Address Brno, Czech Republic
Corporate Author Thesis (up)
Publisher Springer Berlin Heidelberg Place of Publication Editor J. Blanc-Talon et al.
Language English Summary Language Original Title
Series Editor Series Title Abbreviated Series Title LNCS
Series Volume Series Issue Edition
ISSN 0302-9743 ISBN 978-3-642-33139-8 Medium
Area Expedition Conference ACIVS
Notes ADAS;ISE Approved no
Call Number ADAS @ adas @ SLV2012 Serial 1980
Permanent link to this record
 
 
Author David Vazquez; Antonio Lopez; Daniel Ponsa
Title Unsupervised Domain Adaptation of Virtual and Real Worlds for Pedestrian Detection Type Conference Article
Year 2012 Publication 21st International Conference on Pattern Recognition Abbreviated Journal
Volume Issue Pages 3492 - 3495
Keywords Pedestrian Detection; Domain Adaptation; Virtual worlds
Abstract Vision-based object detectors are crucial for different applications. They rely on learnt object models. Ideally, we would like to deploy our vision system in the scenario where it must operate, and lead it to self-learn how to distinguish the objects of interest, i.e., without human intervention. However, the learning of each object model requires labelled samples collected through a tiresome manual process. For instance, we are interested in exploring the self-training of a pedestrian detector for driver assistance systems. Our first approach to avoid manual labelling consisted in the use of samples coming from realistic computer graphics, so that their labels are automatically available [12]. This would make possible the desired self-training of our pedestrian detector. However, as we showed in [14], between virtual and real worlds it may be a dataset shift. In order to overcome it, we propose the use of unsupervised domain adaptation techniques that avoid human intervention during the adaptation process. In particular, this paper explores the use of the transductive SVM (T-SVM) learning algorithm in order to adapt virtual and real worlds for pedestrian detection (Fig. 1).
Address Tsukuba Science City, Japan
Corporate Author Thesis (up)
Publisher IEEE Place of Publication Tsukuba Science City, JAPAN Editor
Language Summary Language Original Title
Series Editor Series Title Abbreviated Series Title
Series Volume Series Issue Edition
ISSN 1051-4651 ISBN 978-1-4673-2216-4 Medium
Area Expedition Conference ICPR
Notes ADAS Approved no
Call Number ADAS @ adas @ VLP2012 Serial 1981
Permanent link to this record
 
 
Author Jon Almazan; Alicia Fornes; Ernest Valveny
Title A non-rigid appearance model for shape description and recognition Type Journal Article
Year 2012 Publication Pattern Recognition Abbreviated Journal PR
Volume 45 Issue 9 Pages 3105--3113
Keywords Shape recognition; Deformable models; Shape modeling; Hand-drawn recognition
Abstract In this paper we describe a framework to learn a model of shape variability in a set of patterns. The framework is based on the Active Appearance Model (AAM) and permits to combine shape deformations with appearance variability. We have used two modifications of the Blurred Shape Model (BSM) descriptor as basic shape and appearance features to learn the model. These modifications permit to overcome the rigidity of the original BSM, adapting it to the deformations of the shape to be represented. We have applied this framework to representation and classification of handwritten digits and symbols. We show that results of the proposed methodology outperform the original BSM approach.
Address
Corporate Author Thesis (up)
Publisher 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 Approved no
Call Number DAG @ dag @ AFV2012 Serial 1982
Permanent link to this record
 
 
Author Jon Almazan; David Fernandez; Alicia Fornes; Josep Llados; Ernest Valveny
Title A Coarse-to-Fine Approach for Handwritten Word Spotting in Large Scale Historical Documents Collection Type Conference Article
Year 2012 Publication 13th International Conference on Frontiers in Handwriting Recognition Abbreviated Journal
Volume Issue Pages 453-458
Keywords
Abstract In this paper we propose an approach for word spotting in handwritten document images. We state the problem from a focused retrieval perspective, i.e. locating instances of a query word in a large scale dataset of digitized manuscripts. We combine two approaches, namely one based on word segmentation and another one segmentation-free. The first approach uses a hashing strategy to coarsely prune word images that are unlikely to be instances of the query word. This process is fast but has a low precision due to the errors introduced in the segmentation step. The regions containing candidate words are sent to the second process based on a state of the art technique from the visual object detection field. This discriminative model represents the appearance of the query word and computes a similarity score. In this way we propose a coarse-to-fine approach achieving a compromise between efficiency and accuracy. The validation of the model is shown using a collection of old handwritten manuscripts. We appreciate a substantial improvement in terms of precision regarding the previous proposed method with a low computational cost increase.
Address
Corporate Author Thesis (up)
Publisher Place of Publication Editor
Language Summary Language Original Title
Series Editor Series Title Abbreviated Series Title
Series Volume Series Issue Edition
ISSN ISBN 978-1-4673-2262-1 Medium
Area Expedition Conference ICFHR
Notes DAG Approved no
Call Number DAG @ dag @ AFF2012 Serial 1983
Permanent link to this record
 
 
Author Jon Almazan; Albert Gordo; Alicia Fornes; Ernest Valveny
Title Efficient Exemplar Word Spotting Type Conference Article
Year 2012 Publication 23rd British Machine Vision Conference Abbreviated Journal
Volume Issue Pages 67.1- 67.11
Keywords
Abstract In this paper we propose an unsupervised segmentation-free method for word spotting in document images.
Documents are represented with a grid of HOG descriptors, and a sliding window approach is used to locate the document regions that are most similar to the query. We use the exemplar SVM framework to produce a better representation of the query in an unsupervised way. Finally, the document descriptors are precomputed and compressed with Product Quantization. This offers two advantages: first, a large number of documents can be kept in RAM memory at the same time. Second, the sliding window becomes significantly faster since distances between quantized HOG descriptors can be precomputed. Our results significantly outperform other segmentation-free methods in the literature, both in accuracy and in speed and memory usage.
Address
Corporate Author Thesis (up)
Publisher Place of Publication Editor
Language Summary Language Original Title
Series Editor Series Title Abbreviated Series Title
Series Volume Series Issue Edition
ISSN ISBN 1-901725-46-4 Medium
Area Expedition Conference BMVC
Notes DAG Approved no
Call Number DAG @ dag @ AGF2012 Serial 1984
Permanent link to this record
 
 
Author Ferran Poveda; Enric Marti; Debora Gil; Francesc Carreras; Manel Ballester
Title Helical Structure of Ventricular Anatomy by Diffusion Tensor Cardiac MR Tractography Type Journal Article
Year 2012 Publication Journal of American College of Cardiology Abbreviated Journal JACC
Volume 5 Issue 7 Pages 754-755
Keywords
Abstract It is widely accepted that myocardial fiber architecture plays a critical role in myocardial contractility and relaxation (1). However, there is a lack of consensus about the distribution of the myocardial fibers and their spatial arrangement in the left and right ventricles. An understanding of the cardiac architecture should benefit the ventricular functional assessment, left ventricular reconstructive surgery planning, or resynchronization therapy in heart failure. Researchers have proposed several conceptual models to describe the architecture of the heart, ranging from gross dissection to histological presentation. The cardiac mesh model (2) proposes that the myocytes are arranged longitudinally and radially change their angulation along the myocardial depth. By contrast, the helical ventricular myocardial model states that the ventricular myocardium is a continuous anatomical helical layout of myocardial fibers (1
Address
Corporate Author Thesis (up)
Publisher Place of Publication Editor
Language Summary Language Original Title
Series Editor Series Title Abbreviated Series Title
Series Volume Series Issue Edition
ISSN 1936-878X ISBN Medium
Area Expedition Conference
Notes IAM Approved no
Call Number IAM @ iam @ PMG2012 Serial 1985
Permanent link to this record
 
 
Author Ferran Poveda; Debora Gil;Enric Marti
Title Multi-resolution DT-MRI cardiac tractography Type Conference Article
Year 2012 Publication Statistical Atlases And Computational Models Of The Heart: Imaging and Modelling Challenges Abbreviated Journal
Volume 7746 Issue Pages 270-277
Keywords
Abstract Even using objective measures from DT-MRI no consensus about myocardial architecture has been achieved so far. Streamlining provides good reconstructions at low level of detail, but falls short to give global abstract interpretations. In this paper, we present a multi-resolution methodology that is able to produce simplified representations of cardiac architecture. Our approach produces a reduced set of tracts that are representative of the main geometric features of myocardial anatomical structure. Experiments show that fiber geometry is preserved along reductions, which validates the simplified model for interpretation of cardiac architecture.
Address Nice, France
Corporate Author Thesis (up)
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 978-3-642-36960-5 Medium
Area Expedition Conference STACOM
Notes IAM Approved no
Call Number IAM @ iam @ PGM2012 Serial 1986
Permanent link to this record
 
 
Author Jose Carlos Rubio; Joan Serrat; Antonio Lopez; N. Paragios
Title Image Contextual Representation and Matching through Hierarchies and Higher Order Graphs Type Conference Article
Year 2012 Publication 21st International Conference on Pattern Recognition Abbreviated Journal
Volume Issue Pages 2664 - 2667
Keywords
Abstract We present a region matching algorithm which establishes correspondences between regions from two segmented images. An abstract graph-based representation conceals the image in a hierarchical graph, exploiting the scene properties at two levels. First, the similarity and spatial consistency of the image semantic objects is encoded in a graph of commute times. Second, the cluttered regions of the semantic objects are represented with a shape descriptor. Many-to-many matching of regions is specially challenging due to the instability of the segmentation under slight image changes, and we explicitly handle it through high order potentials. We demonstrate the matching approach applied to images of world famous buildings, captured under different conditions, showing the robustness of our method to large variations in illumination and viewpoint.
Address Tsukuba Science City, Japan
Corporate Author Thesis (up)
Publisher Place of Publication Editor
Language Summary Language Original Title
Series Editor Series Title Abbreviated Series Title
Series Volume Series Issue Edition
ISSN 1051-4651 ISBN 978-1-4673-2216-4 Medium
Area Expedition Conference ICPR
Notes ADAS Approved no
Call Number Admin @ si @ RSL2012a; Serial 2032
Permanent link to this record