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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  Title Local Binary Pattern for Word Spotting in Handwritten Historical Document Type Conference Article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 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  Keywords Local binary patterns; Spatial sampling; Learning-free; Word spotting; Handwritten; Historical document analysis; Large-scale data  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 This paper presents a simple innovative learning-free method for word spotting from large scale historical documents combining Local Binary Pattern (LBP) and spatial sampling.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 (up) 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 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 Pages  
  Keywords GPU; CUDA; Stixels; Autonomous Driving  
  Abstract  
  Address Silicon Valley; USA; May 2017  
  Corporate Author Thesis  
  Publisher (up) 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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Author David Vazquez; Jorge Bernal; F. Javier Sanchez; Gloria Fernandez Esparrach; Antonio Lopez; Adriana Romero; Michal Drozdzal; Aaron Courville edit   pdf
openurl 
  Title A Benchmark for Endoluminal Scene Segmentation of Colonoscopy Images Type Conference Article
  Year 2017 Publication 31st International Congress and Exhibition on Computer Assisted Radiology and Surgery Abbreviated Journal  
  Volume Issue Pages  
  Keywords Deep Learning; Medical Imaging  
  Abstract Colorectal cancer (CRC) is the third cause of cancer death worldwide. Currently, the standard approach to reduce CRC-related mortality is to perform regular screening in search for polyps and colonoscopy is the screening tool of choice. The main limitations of this screening procedure are polyp miss-rate and inability to perform visual assessment of polyp malignancy. These drawbacks can be reduced by designing Decision Support Systems (DSS) aiming to help clinicians in the different stages of the procedure by providing endoluminal scene segmentation. Thus, in this paper, we introduce an extended benchmark of colonoscopy image, with the hope of establishing a new strong benchmark for colonoscopy image analysis research. We provide new baselines on this dataset by training standard fully convolutional networks (FCN) for semantic segmentation and significantly outperforming, without any further post-processing, prior results in endoluminal scene segmentation.  
  Address  
  Corporate Author Thesis  
  Publisher (up) 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 CARS  
  Notes ADAS; MV; 600.075; 600.085; 600.076; 601.281; 600.118 Approved no  
  Call Number ADAS @ adas @ VBS2017a Serial 2880  
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Author David Geronimo; David Vazquez; Arturo de la Escalera edit  url
openurl 
  Title Vision-Based Advanced Driver Assistance Systems Type Book Chapter
  Year 2017 Publication Computer Vision in Vehicle Technology: Land, Sea, and Air Abbreviated Journal  
  Volume Issue Pages  
  Keywords ADAS; Autonomous Driving  
  Abstract  
  Address  
  Corporate Author Thesis  
  Publisher (up) 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; 600.118 Approved no  
  Call Number ADAS @ adas @ GVE2017 Serial 2881  
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Author Antoni Gurgui; Debora Gil; Enric Marti; Vicente Grau edit  doi
openurl 
  Title Left-Ventricle Basal Region Constrained Parametric Mapping to Unitary Domain Type Conference Article
  Year 2016 Publication 7th International Workshop on Statistical Atlases & Computational Modelling of the Heart Abbreviated Journal  
  Volume 10124 Issue Pages 163-171  
  Keywords Laplacian; Constrained maps; Parameterization; Basal ring  
  Abstract Due to its complex geometry, the basal ring is often omitted when putting different heart geometries into correspondence. In this paper, we present the first results on a new mapping of the left ventricle basal rings onto a normalized coordinate system using a fold-over free approach to the solution to the Laplacian. To guarantee correspondences between different basal rings, we imposed some internal constrained positions at anatomical landmarks in the normalized coordinate system. To prevent internal fold-overs, constraints are handled by cutting the volume into regions defined by anatomical features and mapping each piece of the volume separately. Initial results presented in this paper indicate that our method is able to handle internal constrains without introducing fold-overs and thus guarantees one-to-one mappings between different basal ring geometries.  
  Address Athens; October 2016  
  Corporate Author Thesis  
  Publisher (up) 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 STACOM  
  Notes IAM; Approved no  
  Call Number Admin @ si @ GGM2016 Serial 2884  
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Author Carles Sanchez; Debora Gil; Jorge Bernal; F. Javier Sanchez; Marta Diez-Ferrer; Antoni Rosell edit   pdf
openurl 
  Title Navigation Path Retrieval from Videobronchoscopy using Bronchial Branches Type Conference Article
  Year 2016 Publication 19th International Conference on Medical Image Computing and Computer Assisted Intervention Workshops Abbreviated Journal  
  Volume 9401 Issue Pages 62-70  
  Keywords Bronchoscopy navigation; Lumen center; Brochial branches; Navigation path; Videobronchoscopy  
  Abstract Bronchoscopy biopsy can be used to diagnose lung cancer without risking complications of other interventions like transthoracic needle aspiration. During bronchoscopy, the clinician has to navigate through the bronchial tree to the target lesion. A main drawback is the difficulty to check whether the exploration is following the correct path. The usual guidance using fluoroscopy implies repeated radiation of the clinician, while alternative systems (like electromagnetic navigation) require specific equipment that increases intervention costs. We propose to compute the navigated path using anatomical landmarks extracted from the sole analysis of videobronchoscopy images. Such landmarks allow matching the current exploration to the path previously planned on a CT to indicate clinician whether the planning is being correctly followed or not. We present a feasibility study of our landmark based CT-video matching using bronchoscopic videos simulated on a virtual bronchoscopy interactive interface.  
  Address Quebec; Canada; September 2016  
  Corporate Author Thesis  
  Publisher (up) 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 MICCAIW  
  Notes IAM; MV; 600.060; 600.075 Approved no  
  Call Number Admin @ si @ SGB2016 Serial 2885  
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Author Juan A. Carvajal Ayala; Dennis Romero; Angel Sappa edit   pdf
doi  openurl
  Title Fine-tuning based deep convolutional networks for lepidopterous genus recognition Type Conference Article
  Year 2016 Publication 21st Ibero American Congress on Pattern Recognition Abbreviated Journal  
  Volume Issue Pages 467-475  
  Keywords  
  Abstract This paper describes an image classification approach oriented to identify specimens of lepidopterous insects at Ecuadorian ecological reserves. This work seeks to contribute to studies in the area of biology about genus of butterflies and also to facilitate the registration of unrecognized specimens. The proposed approach is based on the fine-tuning of three widely used pre-trained Convolutional Neural Networks (CNNs). This strategy is intended to overcome the reduced number of labeled images. Experimental results with a dataset labeled by expert biologists is presented, reaching a recognition accuracy above 92%.  
  Address Lima; Perú; November 2016  
  Corporate Author Thesis  
  Publisher (up) 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 CIARP  
  Notes ADAS; 600.086 Approved no  
  Call Number Admin @ si @ CRS2016 Serial 2913  
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Author H.Martin Kjer; Jens Fagertuna; Sergio Vera; Debora Gil; Miguel Angel Gonzalez Ballester; Rasmus R. Paulsena edit   pdf
url  openurl
  Title Free-form image registration of human cochlear uCT data using skeleton similarity as anatomical prior Type Journal Article
  Year 2016 Publication Patter Recognition Letters Abbreviated Journal PRL  
  Volume 76 Issue 1 Pages 76-82  
  Keywords  
  Abstract  
  Address  
  Corporate Author Thesis  
  Publisher (up) 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; 600.060 Approved no  
  Call Number Admin @ si @ MFV2017b Serial 2941  
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Author Albert Berenguel; Oriol Ramos Terrades; Josep Llados; Cristina Cañero edit  doi
openurl 
  Title Banknote counterfeit detection through background texture printing analysis Type Conference Article
  Year 2016 Publication 12th IAPR Workshop on Document Analysis Systems Abbreviated Journal  
  Volume Issue Pages  
  Keywords  
  Abstract This paper is focused on the detection of counterfeit photocopy banknotes. The main difficulty is to work on a real industrial scenario without any constraint about the acquisition device and with a single image. The main contributions of this paper are twofold: first the adaptation and performance evaluation of existing approaches to classify the genuine and photocopy banknotes using background texture printing analysis, which have not been applied into this context before. Second, a new dataset of Euro banknotes images acquired with several cameras under different luminance conditions to evaluate these methods. Experiments on the proposed algorithms show that mixing SIFT features and sparse coding dictionaries achieves quasi perfect classification using a linear SVM with the created dataset. Approaches using dictionaries to cover all possible texture variations have demonstrated to be robust and outperform the state-of-the-art methods using the proposed benchmark.  
  Address Rumania; May 2016  
  Corporate Author Thesis  
  Publisher (up) 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; 600.061; 601.269; 600.097 Approved no  
  Call Number Admin @ si @ BRL2016 Serial 2950  
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Author Lluis Gomez; Dimosthenis Karatzas edit   pdf
url  openurl
  Title TextProposals: a Text‐specific Selective Search Algorithm for Word Spotting in the Wild Type Journal Article
  Year 2017 Publication Pattern Recognition Abbreviated Journal PR  
  Volume 70 Issue Pages 60-74  
  Keywords  
  Abstract Motivated by the success of powerful while expensive techniques to recognize words in a holistic way (Goel et al., 2013; Almazán et al., 2014; Jaderberg et al., 2016) object proposals techniques emerge as an alternative to the traditional text detectors. In this paper we introduce a novel object proposals method that is specifically designed for text. We rely on a similarity based region grouping algorithm that generates a hierarchy of word hypotheses. Over the nodes of this hierarchy it is possible to apply a holistic word recognition method in an efficient way.

Our experiments demonstrate that the presented method is superior in its ability of producing good quality word proposals when compared with class-independent algorithms. We show impressive recall rates with a few thousand proposals in different standard benchmarks, including focused or incidental text datasets, and multi-language scenarios. Moreover, the combination of our object proposals with existing whole-word recognizers (Almazán et al., 2014; Jaderberg et al., 2016) shows competitive performance in end-to-end word spotting, and, in some benchmarks, outperforms previously published results. Concretely, in the challenging ICDAR2015 Incidental Text dataset, we overcome in more than 10% F-score the best-performing method in the last ICDAR Robust Reading Competition (Karatzas, 2015). Source code of the complete end-to-end system is available at https://github.com/lluisgomez/TextProposals.
 
  Address  
  Corporate Author Thesis  
  Publisher (up) 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; 600.084; 601.197; 600.121; 600.129 Approved no  
  Call Number Admin @ si @ GoK2017 Serial 2886  
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Author Lluis Gomez; Anguelos Nicolaou; Dimosthenis Karatzas edit   pdf
doi  openurl
  Title Improving patch‐based scene text script identification with ensembles of conjoined networks Type Journal Article
  Year 2017 Publication Pattern Recognition Abbreviated Journal PR  
  Volume 67 Issue Pages 85-96  
  Keywords  
  Abstract  
  Address  
  Corporate Author Thesis  
  Publisher (up) 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; 600.084; 600.121; 600.129 Approved no  
  Call Number Admin @ si @ GNK2017 Serial 2887  
Permanent link to this record
 

 
Author Lluis Gomez; Y. Patel; Marçal Rusiñol; C.V. Jawahar; Dimosthenis Karatzas edit   pdf
url  doi
openurl 
  Title Self‐supervised learning of visual features through embedding images into text topic spaces Type Conference Article
  Year 2017 Publication 30th IEEE Conference on Computer Vision and Pattern Recognition Abbreviated Journal  
  Volume Issue Pages  
  Keywords  
  Abstract End-to-end training from scratch of current deep architectures for new computer vision problems would require Imagenet-scale datasets, and this is not always possible. In this paper we present a method that is able to take advantage of freely available multi-modal content to train computer vision algorithms without human supervision. We put forward the idea of performing self-supervised learning of visual features by mining a large scale corpus of multi-modal (text and image) documents. We show that discriminative visual features can be learnt efficiently by training a CNN to predict the semantic context in which a particular image is more probable to appear as an illustration. For this we leverage the hidden semantic structures discovered in the text corpus with a well-known topic modeling technique. Our experiments demonstrate state of the art performance in image classification, object detection, and multi-modal retrieval compared to recent self-supervised or natural-supervised approaches.  
  Address Honolulu; Hawaii; July 2017  
  Corporate Author Thesis  
  Publisher (up) 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 CVPR  
  Notes DAG; 600.084; 600.121 Approved no  
  Call Number Admin @ si @ GPR2017 Serial 2889  
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Author Marc Sunset Perez; Marc Comino Trinidad; Dimosthenis Karatzas; Antonio Chica Calaf; Pere Pau Vazquez Alcocer edit  url
openurl 
  Title Development of general‐purpose projection‐based augmented reality systems Type Journal
  Year 2016 Publication IADIs international journal on computer science and information systems Abbreviated Journal IADIs  
  Volume 11 Issue 2 Pages 1-18  
  Keywords  
  Abstract Despite the large amount of methods and applications of augmented reality, there is little homogenizatio n on the software platforms that support them. An exception may be the low level control software that is provided by some high profile vendors such as Qualcomm and Metaio. However, these provide fine grain modules for e.g. element tracking. We are more co ncerned on the application framework, that includes the control of the devices working together for the development of the AR experience. In this paper we describe the development of a software framework for AR setups. We concentrate on the modular design of the framework, but also on some hard problems such as the calibration stage, crucial for projection – based AR. The developed framework is suitable and has been tested in AR applications using camera – projector pairs, for both fixed and nomadic setups  
  Address  
  Corporate Author Thesis  
  Publisher (up) 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; 600.084 Approved no  
  Call Number Admin @ si @ SCK2016 Serial 2890  
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Author Lluis Gomez edit  openurl
  Title Exploiting Similarity Hierarchies for Multi-script Scene Text Understanding Type Book Whole
  Year 2016 Publication PhD Thesis, Universitat Autonoma de Barcelona-CVC Abbreviated Journal  
  Volume Issue Pages  
  Keywords  
  Abstract This thesis addresses the problem of automatic scene text understanding in unconstrained conditions. In particular, we tackle the tasks of multi-language and arbitrary-oriented text detection, tracking, and script identification in natural scenes.
For this we have developed a set of generic methods that build on top of the basic observation that text has always certain key visual and structural characteristics that are independent of the language or script in which it is written. Text instances in any
language or script are always formed as groups of similar atomic parts, being them either individual characters, small stroke parts, or even whole words in the case of cursive text. This holistic (sumof-parts) and recursive perspective has lead us to explore different variants of the “segmentation and grouping” paradigm of computer vision.
Scene text detection methodologies are usually based in classification of individual regions or patches, using a priory knowledge for a given script or language. Human perception of text, on the other hand, is based on perceptual organization through which
text emerges as a perceptually significant group of atomic objects.
In this thesis, we argue that the text detection problem must be posed as the detection of meaningful groups of regions. We address the problem of text detection in natural scenes from a hierarchical perspective, making explicit use of the recursive nature of text, aiming directly to the detection of region groupings corresponding to text within a hierarchy produced by an agglomerative similarity clustering process over individual regions. We propose an optimal way to construct such an hierarchy introducing a feature space designed to produce text group hypothese with high recall and a novel stopping rule combining a discriminative classifier and a probabilistic measure of group meaningfulness based in perceptual organization. Within this generic framework, we design a text-specific object proposals algorithm that, contrary to existing generic object proposals methods, aims directly to the detection of text regions groupings. For this, we abandon the rigid definition of “what is text” of traditional specialized text detectors, and move towards more fuzzy perspective of grouping-based object proposals methods.
Then, we present a hybrid algorithm for detection and tracking of scene text where the notion of region groupings plays also a central role. By leveraging the structural arrangement of text group components between consecutive frames we can improve
the overall tracking performance of the system.
Finally, since our generic detection framework is inherently designed for multi-language environments, we focus on the problem of script identification in order to build a multi-language end-toend reading system. Facing this problem with state of the art CNN classifiers is not straightforward, as they fail to address a key
characteristic of scene text instances: their extremely variable aspect ratio. Instead of resizing input images to a fixed size as in the typical use of holistic CNN classifiers, we propose a patch-based classification framework in order to preserve discriminative parts of the image that are characteristic of its class. We describe a novel method based on the use of ensembles of conjoined networks to jointly learn discriminative stroke-parts representations and their relative importance in a patch-based classification scheme.
 
  Address  
  Corporate Author Thesis Ph.D. thesis  
  Publisher (up) Place of Publication Editor Dimosthenis Karatzas  
  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 @ Gom2016 Serial 2891  
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Author Ivet Rafegas; Javier Vazquez; Robert Benavente; Maria Vanrell; Susana Alvarez edit  url
openurl 
  Title Enhancing spatio-chromatic representation with more-than-three color coding for image description Type Journal Article
  Year 2017 Publication Journal of the Optical Society of America A Abbreviated Journal JOSA A  
  Volume 34 Issue 5 Pages 827-837  
  Keywords  
  Abstract Extraction of spatio-chromatic features from color images is usually performed independently on each color channel. Usual 3D color spaces, such as RGB, present a high inter-channel correlation for natural images. This correlation can be reduced using color-opponent representations, but the spatial structure of regions with small color differences is not fully captured in two generic Red-Green and Blue-Yellow channels. To overcome these problems, we propose a new color coding that is adapted to the specific content of each image. Our proposal is based on two steps: (a) setting the number of channels to the number of distinctive colors we find in each image (avoiding the problem of channel correlation), and (b) building a channel representation that maximizes contrast differences within each color channel (avoiding the problem of low local contrast). We call this approach more-than-three color coding (MTT) to enhance the fact that the number of channels is adapted to the image content. The higher color complexity an image has, the more channels can be used to represent it. Here we select distinctive colors as the most predominant in the image, which we call color pivots, and we build the new color coding using these color pivots as a basis. To evaluate the proposed approach we measure its efficiency in an image categorization task. We show how a generic descriptor improves its performance at the description level when applied on the MTT coding.  
  Address  
  Corporate Author Thesis  
  Publisher (up) 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 CIC; 600.087 Approved no  
  Call Number Admin @ si @ RVB2017 Serial 2892  
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