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Author Lluis Gomez; Dimosthenis Karatzas edit   pdf
doi  openurl
  Title A fine-grained approach to scene text script identification Type Conference Article
  Year 2016 Publication 12th IAPR Workshop on Document Analysis Systems Abbreviated Journal  
  Volume Issue (down) Pages 192-197  
  Keywords  
  Abstract This paper focuses on the problem of script identification in unconstrained scenarios. Script identification is an important prerequisite to recognition, and an indispensable condition for automatic text understanding systems designed for multi-language environments. Although widely studied for document images and handwritten documents, it remains an almost unexplored territory for scene text images. We detail a novel method for script identification in natural images that combines convolutional features and the Naive-Bayes Nearest Neighbor classifier. The proposed framework efficiently exploits the discriminative power of small stroke-parts, in a fine-grained classification framework. In addition, we propose a new public benchmark dataset for the evaluation of joint text detection and script identification in natural scenes. Experiments done in this new dataset demonstrate that the proposed method yields state of the art results, while it generalizes well to different datasets and variable number of scripts. The evidence provided shows that multi-lingual scene text recognition in the wild is a viable proposition. Source code of the proposed method is made available online.  
  Address Santorini; Grecia; April 2016  
  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 DAS  
  Notes DAG; 601.197; 600.084 Approved no  
  Call Number Admin @ si @ GoK2016b Serial 2863  
Permanent link to this record
 

 
Author Aura Hernandez-Sabate; Lluis Albarracin; Daniel Calvo; Nuria Gorgorio edit   pdf
openurl 
  Title EyeMath: Identifying Mathematics Problem Solving Processes in a RTS Video Game Type Conference Article
  Year 2016 Publication 5th International Conference Games and Learning Alliance Abbreviated Journal  
  Volume 10056 Issue (down) Pages 50-59  
  Keywords Simulation environment; Automated Driving; Driver-Vehicle interaction  
  Abstract Photorealistic virtual environments are crucial for developing and testing automated driving systems in a safe way during trials. As commercially available simulators are expensive and bulky, this paper presents a low-cost, extendable, and easy-to-use (LEE) virtual environment with the aim to highlight its utility for level 3 driving automation. In particular, an experiment is performed using the presented simulator to explore the influence of different variables regarding control transfer of the car after the system was driving autonomously in a highway scenario. The results show that the speed of the car at the time when the system needs to transfer the control to the human driver is critical.  
  Address  
  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 GALA  
  Notes ADAS;IAM; Approved no  
  Call Number HAC2016 Serial 2864  
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Author Saad Minhas; Aura Hernandez-Sabate; Shoaib Ehsan; Katerine Diaz; Ales Leonardis; Antonio Lopez; Klaus McDonald Maier edit   pdf
openurl 
  Title LEE: A photorealistic Virtual Environment for Assessing Driver-Vehicle Interactions in Self-Driving Mode Type Conference Article
  Year 2016 Publication 14th European Conference on Computer Vision Workshops Abbreviated Journal  
  Volume 9915 Issue (down) Pages 894-900  
  Keywords Simulation environment; Automated Driving; Driver-Vehicle interaction  
  Abstract Photorealistic virtual environments are crucial for developing and testing automated driving systems in a safe way during trials. As commercially available simulators are expensive and bulky, this paper presents a low-cost, extendable, and easy-to-use (LEE) virtual environment with the aim to highlight its utility for level 3 driving automation. In particular, an experiment is performed using the presented simulator to explore the influence of different variables regarding control transfer of the car after the system was driving autonomously in a highway scenario. The results show that the speed of the car at the time when the system needs to transfer the control to the human driver is critical.  
  Address Amsterdam; The Netherlands; October 2016  
  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 ECCVW  
  Notes ADAS;IAM; 600.085; 600.076 Approved no  
  Call Number MHE2016 Serial 2865  
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Author Simon Jégou; Michal Drozdzal; David Vazquez; Adriana Romero; Yoshua Bengio edit   pdf
url  doi
openurl 
  Title The One Hundred Layers Tiramisu: Fully Convolutional DenseNets for Semantic Segmentation Type Conference Article
  Year 2017 Publication IEEE Conference on Computer Vision and Pattern Recognition Workshops Abbreviated Journal  
  Volume Issue (down) Pages  
  Keywords Semantic Segmentation  
  Abstract State-of-the-art approaches for semantic image segmentation are built on Convolutional Neural Networks (CNNs). The typical segmentation architecture is composed of (a) a downsampling path responsible for extracting coarse semantic features, followed by (b) an upsampling path trained to recover the input image resolution at the output of the model and, optionally, (c) a post-processing module (e.g. Conditional Random Fields) to refine the model predictions.

Recently, a new CNN architecture, Densely Connected Convolutional Networks (DenseNets), has shown excellent results on image classification tasks. The idea of DenseNets is based on the observation that if each layer is directly connected to every other layer in a feed-forward fashion then the network will be more accurate and easier to train.

In this paper, we extend DenseNets to deal with the problem of semantic segmentation. We achieve state-of-the-art results on urban scene benchmark datasets such as CamVid and Gatech, without any further post-processing module nor pretraining. Moreover, due to smart construction of the model, our approach has much less parameters than currently published best entries for these datasets.
 
  Address Honolulu; USA; July 2017  
  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 CVPRW  
  Notes MILAB; ADAS; 600.076; 600.085; 601.281 Approved no  
  Call Number ADAS @ adas @ JDV2016 Serial 2866  
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Author Arash Akbarinia; C. Alejandro Parraga edit   pdf
openurl 
  Title Biologically plausible boundary detection Type Conference Article
  Year 2016 Publication 27th British Machine Vision Conference Abbreviated Journal  
  Volume Issue (down) Pages  
  Keywords  
  Abstract Edges are key components of any visual scene to the extent that we can recognise objects merely by their silhouettes. The human visual system captures edge information through neurons in the visual cortex that are sensitive to both intensity discontinuities and particular orientations. The “classical approach” assumes that these cells are only responsive to the stimulus present within their receptive fields, however, recent studies demonstrate that surrounding regions and inter-areal feedback connections influence their responses significantly. In this work we propose a biologically-inspired edge detection model in which orientation selective neurons are represented through the first derivative of a Gaussian function resembling double-opponent cells in the primary visual cortex (V1). In our model we account for four kinds of surround, i.e. full, far, iso- and orthogonal-orientation, whose contributions are contrast-dependant. The output signal from V1 is pooled in its perpendicular direction by larger V2 neurons employing a contrast-variant centre-surround kernel. We further introduce a feedback connection from higher-level visual areas to the lower ones. The results of our model on two benchmark datasets show a big improvement compared to the current non-learning and biologically-inspired state-of-the-art algorithms while being competitive to the learning-based methods.  
  Address York; UK; September 2016  
  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 BMVC  
  Notes NEUROBIT; 600.068; 600.072 Approved no  
  Call Number Admin @ si @ AkP2016a Serial 2867  
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Author Azadeh S. Mozafari; David Vazquez; Mansour Jamzad; Antonio Lopez edit   pdf
openurl 
  Title Node-Adapt, Path-Adapt and Tree-Adapt:Model-Transfer Domain Adaptation for Random Forest Type Miscellaneous
  Year 2016 Publication Arxiv Abbreviated Journal  
  Volume Issue (down) Pages  
  Keywords Domain Adaptation; Pedestrian detection; Random Forest  
  Abstract Random Forest (RF) is a successful paradigm for learning classifiers due to its ability to learn from large feature spaces and seamlessly integrate multi-class classification, as well as the achieved accuracy and processing efficiency. However, as many other classifiers, RF requires domain adaptation (DA) provided that there is a mismatch between the training (source) and testing (target) domains which provokes classification degradation. Consequently, different RF-DA methods have been proposed, which not only require target-domain samples but revisiting the source-domain ones, too. As novelty, we propose three inherently different methods (Node-Adapt, Path-Adapt and Tree-Adapt) that only require the learned source-domain RF and a relatively few target-domain samples for DA, i.e. source-domain samples do not need to be available. To assess the performance of our proposals we focus on image-based object detection, using the pedestrian detection problem as challenging proof-of-concept. Moreover, we use the RF with expert nodes because it is a competitive patch-based pedestrian model. We test our Node-, Path- and Tree-Adapt methods in standard benchmarks, showing that DA is largely achieved.  
  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 @ MVJ2016 Serial 2868  
Permanent link to this record
 

 
Author Pau Riba; Josep Llados; Alicia Fornes edit   pdf
doi  openurl
  Title Error-tolerant coarse-to-fine matching model for hierarchical graphs Type Conference Article
  Year 2017 Publication 11th IAPR-TC-15 International Workshop on Graph-Based Representations in Pattern Recognition Abbreviated Journal  
  Volume 10310 Issue (down) Pages 107-117  
  Keywords Graph matching; Hierarchical graph; Graph-based representation; Coarse-to-fine matching  
  Abstract Graph-based representations are effective tools to capture structural information from visual elements. However, retrieving a query graph from a large database of graphs implies a high computational complexity. Moreover, these representations are very sensitive to noise or small changes. In this work, a novel hierarchical graph representation is designed. Using graph clustering techniques adapted from graph-based social media analysis, we propose to generate a hierarchy able to deal with different levels of abstraction while keeping information about the topology. For the proposed representations, a coarse-to-fine matching method is defined. These approaches are validated using real scenarios such as classification of colour images and handwritten word spotting.  
  Address Anacapri; Italy; May 2017  
  Corporate Author Thesis  
  Publisher Springer International Publishing Place of Publication Editor Pasquale Foggia; Cheng-Lin Liu; Mario Vento  
  Language Summary Language Original Title  
  Series Editor Series Title Abbreviated Series Title  
  Series Volume Series Issue Edition  
  ISSN ISBN Medium  
  Area Expedition Conference GbRPR  
  Notes DAG; 600.097; 601.302; 600.121 Approved no  
  Call Number Admin @ si @ RLF2017a Serial 2951  
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Author Veronica Romero; Alicia Fornes; Enrique Vidal; Joan Andreu Sanchez edit   pdf
isbn  openurl
  Title Information Extraction in Handwritten Marriage Licenses Books Using the MGGI Methodology Type Conference Article
  Year 2017 Publication 8th Iberian Conference on Pattern Recognition and Image Analysis Abbreviated Journal  
  Volume 10255 Issue (down) Pages 287-294  
  Keywords Handwritten Text Recognition; Information extraction; Language modeling; MGGI; Categories-based language model  
  Abstract Historical records of daily activities provide intriguing insights into the life of our ancestors, useful for demographic and genealogical research. For example, marriage license books have been used for centuries by ecclesiastical and secular institutions to register marriages. These books follow a simple structure of the text in the records with a evolutionary vocabulary, mainly composed of proper names that change along the time. This distinct vocabulary makes automatic transcription and semantic information extraction difficult tasks. In previous works we studied the use of category-based language models and how a Grammatical Inference technique known as MGGI could improve the accuracy of these tasks. In this work we analyze the main causes of the semantic errors observed in previous results and apply a better implementation of the MGGI technique to solve these problems. Using the resulting language model, transcription and information extraction experiments have been carried out, and the results support our proposed approach.  
  Address Faro; Portugal; June 2017  
  Corporate Author Thesis  
  Publisher Place of Publication Editor L.A. Alexandre; J.Salvador Sanchez; Joao M. F. Rodriguez  
  Language Summary Language Original Title  
  Series Editor Series Title Abbreviated Series Title LNCS  
  Series Volume Series Issue Edition  
  ISSN ISBN 978-3-319-58837-7 Medium  
  Area Expedition Conference IbPRIA  
  Notes DAG; 602.006; 600.097; 600.121 Approved no  
  Call Number Admin @ si @ RFV2017 Serial 2952  
Permanent link to this record
 

 
Author Youssef El Rhabi; Simon Loic; Brun Luc; Josep Llados; Felipe Lumbreras edit  doi
openurl 
  Title Information Theoretic Rotationwise Robust Binary Descriptor Learning Type Conference Article
  Year 2016 Publication Joint IAPR International Workshops on Statistical Techniques in Pattern Recognition (SPR) and Structural and Syntactic Pattern Recognition (SSPR) Abbreviated Journal  
  Volume Issue (down) Pages 368-378  
  Keywords  
  Abstract In this paper, we propose a new data-driven approach for binary descriptor selection. In order to draw a clear analysis of common designs, we present a general information-theoretic selection paradigm. It encompasses several standard binary descriptor construction schemes, including a recent state-of-the-art one named BOLD. We pursue the same endeavor to increase the stability of the produced descriptors with respect to rotations. To achieve this goal, we have designed a novel offline selection criterion which is better adapted to the online matching procedure. The effectiveness of our approach is demonstrated on two standard datasets, where our descriptor is compared to BOLD and to several classical descriptors. In particular, it emerges that our approach can reproduce equivalent if not better performance as BOLD while relying on twice shorter descriptors. Such an improvement can be influential for real-time applications.  
  Address Mérida; Mexico; November 2016  
  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 S+SSPR  
  Notes DAG; ADAS; 600.097; 600.086 Approved no  
  Call Number Admin @ si @ RLL2016 Serial 2871  
Permanent link to this record
 

 
Author Pau Riba; Josep Llados; Alicia Fornes; Anjan Dutta edit  url
openurl 
  Title Large-scale graph indexing using binary embeddings of node contexts for information spotting in document image databases Type Journal Article
  Year 2017 Publication Pattern Recognition Letters Abbreviated Journal PRL  
  Volume 87 Issue (down) Pages 203-211  
  Keywords  
  Abstract Graph-based representations are experiencing a growing usage in visual recognition and retrieval due to their representational power in front of classical appearance-based representations. However, retrieving a query graph from a large dataset of graphs implies a high computational complexity. The most important property for a large-scale retrieval is the search time complexity to be sub-linear in the number of database examples. With this aim, in this paper we propose a graph indexation formalism applied to visual retrieval. A binary embedding is defined as hashing keys for graph nodes. Given a database of labeled graphs, graph nodes are complemented with vectors of attributes representing their local context. Then, each attribute vector is converted to a binary code applying a binary-valued hash function. Therefore, graph retrieval is formulated in terms of finding target graphs in the database whose nodes have a small Hamming distance from the query nodes, easily computed with bitwise logical operators. As an application example, we validate the performance of the proposed methods in different real scenarios such as handwritten word spotting in images of historical documents or symbol spotting in architectural floor plans.  
  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; 602.006; 603.053; 600.121 Approved no  
  Call Number RLF2017b Serial 2873  
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Author Pau Riba; Alicia Fornes; Josep Llados edit  isbn
openurl 
  Title Towards the Alignment of Handwritten Music Scores Type Conference Article
  Year 2015 Publication 11th IAPR International Workshop on Graphics Recognition Abbreviated Journal  
  Volume Issue (down) Pages  
  Keywords  
  Abstract It is very common to find different versions of the same music work in archives of Opera Theaters. These differences correspond to modifications and annotations from the musicians. From the musicologist point of view, these variations are very interesting and deserve study. This paper explores the alignment of music scores as a tool for automatically detecting the passages that contain such differences. Given the difficulties in the recognition of handwritten music scores, our goal is to align the music scores and at the same time, avoid the recognition of music elements as much as possible. After removing the staff lines, braces and ties, the bar lines are detected. Then, the bar units are described as a whole using the Blurred Shape Model. The bar units alignment is performed by using Dynamic Time Warping. The analysis of the alignment path is used to detect the variations in the music scores. The method has been evaluated on a subset of the CVC-MUSCIMA dataset, showing encouraging results.  
  Address Nancy; France; August 2015  
  Corporate Author Thesis  
  Publisher Springer International Publishing Place of Publication Editor Bart Lamiroy; Rafael Dueire Lins  
  Language Summary Language Original Title  
  Series Editor Series Title Abbreviated Series Title LNCS  
  Series Volume Series Issue Edition  
  ISSN ISBN 978-3-319-52158-9 Medium  
  Area Expedition Conference GREC  
  Notes DAG Approved no  
  Call Number Admin @ si @ Serial 2874  
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Author Anjan Dutta; Umapada Pal; Josep Llados edit  url
openurl 
  Title Compact Correlated Features for Writer Independent Signature Verification Type Conference Article
  Year 2016 Publication 23rd International Conference on Pattern Recognition Abbreviated Journal  
  Volume Issue (down) Pages  
  Keywords  
  Abstract This paper considers the offline signature verification problem which is considered to be an important research line in the field of pattern recognition. In this work we propose hybrid features that consider the local features and their global statistics in the signature image. This has been done by creating a vocabulary of histogram of oriented gradients (HOGs). We impose weights on these local features based on the height information of water reservoirs obtained from the signature. Spatial information between local features are thought to play a vital role in considering the geometry of the signatures which distinguishes the originals from the forged ones. Nevertheless, learning a condensed set of higher order neighbouring features based on visual words, e.g., doublets and triplets, continues to be a challenging problem as possible combinations of visual words grow exponentially. To avoid this explosion of size, we create a code of local pairwise features which are represented as joint descriptors. Local features are paired based on the edges of a graph representation built upon the Delaunay triangulation. We reveal the advantage of combining both type of visual codebooks (order one and pairwise) for signature verification task. This is validated through an encouraging result on two benchmark datasets viz. CEDAR and GPDS300.  
  Address Cancun; Mexico; December 2016  
  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 ICPR  
  Notes DAG; 600.097 Approved no  
  Call Number Admin @ si @ DPL2016 Serial 2875  
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Author Sounak Dey; Anguelos Nicolaou; Josep Llados; Umapada Pal edit   pdf
doi  openurl
  Title Local Binary Pattern for Word Spotting in Handwritten Historical Document Type Conference Article
  Year 2016 Publication Joint IAPR International Workshops on Statistical Techniques in Pattern Recognition (SPR) and Structural and Syntactic Pattern Recognition (SSPR) Abbreviated Journal  
  Volume Issue (down) Pages 574-583  
  Keywords Local binary patterns; Spatial sampling; Learning-free; Word spotting; Handwritten; Historical document analysis; Large-scale data  
  Abstract Digital libraries store images which can be highly degraded and to index this kind of images we resort to word spotting as our information retrieval system. Information retrieval for handwritten document images is more challenging due to the difficulties in complex layout analysis, large variations of writing styles, and degradation or low quality of historical manuscripts. This paper presents a simple innovative learning-free method for word spotting from large scale historical documents combining Local Binary Pattern (LBP) and spatial sampling. This method offers three advantages: firstly, it operates in completely learning free paradigm which is very different from unsupervised learning methods, secondly, the computational time is significantly low because of the LBP features, which are very fast to compute, and thirdly, the method can be used in scenarios where annotations are not available. Finally, we compare the results of our proposed retrieval method with other methods in the literature and we obtain the best results in the learning free paradigm.  
  Address Merida; Mexico; December 2016  
  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 S+SSPR  
  Notes DAG; 600.097; 602.006; 603.053 Approved no  
  Call Number Admin @ si @ DNL2016 Serial 2876  
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Author Juan Ignacio Toledo; Sebastian Sudholt; Alicia Fornes; Jordi Cucurull; A. Fink; Josep Llados edit   pdf
url  isbn
openurl 
  Title Handwritten Word Image Categorization with Convolutional Neural Networks and Spatial Pyramid Pooling Type Conference Article
  Year 2016 Publication Joint IAPR International Workshops on Statistical Techniques in Pattern Recognition (SPR) and Structural and Syntactic Pattern Recognition (SSPR) Abbreviated Journal  
  Volume 10029 Issue (down) Pages 543-552  
  Keywords Document image analysis; Word image categorization; Convolutional neural networks; Named entity detection  
  Abstract The extraction of relevant information from historical document collections is one of the key steps in order to make these documents available for access and searches. The usual approach combines transcription and grammars in order to extract semantically meaningful entities. In this paper, we describe a new method to obtain word categories directly from non-preprocessed handwritten word images. The method can be used to directly extract information, being an alternative to the transcription. Thus it can be used as a first step in any kind of syntactical analysis. The approach is based on Convolutional Neural Networks with a Spatial Pyramid Pooling layer to deal with the different shapes of the input images. We performed the experiments on a historical marriage record dataset, obtaining promising results.  
  Address Merida; Mexico; December 2016  
  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 ISBN 978-3-319-49054-0 Medium  
  Area Expedition Conference S+SSPR  
  Notes DAG; 600.097; 602.006 Approved no  
  Call Number Admin @ si @ TSF2016 Serial 2877  
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Author Daniel Hernandez; Antonio Espinosa; David Vazquez; Antonio Lopez; Juan Carlos Moure edit   pdf
openurl 
  Title Embedded Real-time Stixel Computation Type Conference Article
  Year 2017 Publication GPU Technology Conference Abbreviated Journal  
  Volume Issue (down) Pages  
  Keywords GPU; CUDA; Stixels; Autonomous Driving  
  Abstract  
  Address Silicon Valley; USA; May 2017  
  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 GTC  
  Notes ADAS; 600.118 Approved no  
  Call Number ADAS @ adas @ HEV2017a Serial 2879  
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