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Author Youssef El Rhabi; Simon Loic; Brun Luc; Josep Llados; Felipe Lumbreras
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 (up) Issue 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
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Author Anjan Dutta; Umapada Pal; Josep Llados
Title Compact Correlated Features for Writer Independent Signature Verification Type Conference Article
Year 2016 Publication 23rd International Conference on Pattern Recognition Abbreviated Journal
Volume (up) Issue 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
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 (up) 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 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 A. Carvajal Ayala; Dennis Romero; Angel Sappa
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 (up) 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 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 Albert Berenguel; Oriol Ramos Terrades; Josep Llados; Cristina Cañero
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 (up) 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 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
Permanent link to this record
 
 
Author Lluis Gomez
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 (up) 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 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
Permanent link to this record
 
 
Author Ivet Rafegas; Maria Vanrell
Title Color spaces emerging from deep convolutional networks Type Conference Article
Year 2016 Publication 24th Color and Imaging Conference Abbreviated Journal
Volume (up) Issue Pages 225-230
Keywords
Abstract Award for the best interactive session
Defining color spaces that provide a good encoding of spatio-chromatic properties of color surfaces is an open problem in color science [8, 22]. Related to this, in computer vision the fusion of color with local image features has been studied and evaluated [16]. In human vision research, the cells which are selective to specific color hues along the visual pathway are also a focus of attention [7, 14]. In line with these research aims, in this paper we study how color is encoded in a deep Convolutional Neural Network (CNN) that has been trained on more than one million natural images for object recognition. These convolutional nets achieve impressive performance in computer vision, and rival the representations in human brain. In this paper we explore how color is represented in a CNN architecture that can give some intuition about efficient spatio-chromatic representations. In convolutional layers the activation of a neuron is related to a spatial filter, that combines spatio-chromatic representations. We use an inverted version of it to explore the properties. Using a series of unsupervised methods we classify different type of neurons depending on the color axes they define and we propose an index of color-selectivity of a neuron. We estimate the main color axes that emerge from this trained net and we prove that colorselectivity of neurons decreases from early to deeper layers.
Address San Diego; USA; 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 CIC
Notes CIC Approved no
Call Number Admin @ si @ RaV2016a Serial 2894
Permanent link to this record
 
 
Author Ivet Rafegas; Maria Vanrell
Title Colour Visual Coding in trained Deep Neural Networks Type Abstract
Year 2016 Publication European Conference on Visual Perception Abbreviated Journal
Volume (up) Issue Pages
Keywords
Abstract
Address Barcelona; Spain; August 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 ECVP
Notes CIC Approved no
Call Number Admin @ si @ RaV2016b Serial 2895
Permanent link to this record
 
 
Author Arash Akbarinia; C. Alejandro Parraga
Title Dynamically Adjusted Surround Contrast Enhances Boundary Detection, European Conference on Visual Perception Type Conference Article
Year 2016 Publication European Conference on Visual Perception Abbreviated Journal
Volume (up) Issue Pages
Keywords
Abstract
Address Barcelona; Spain; August 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 ECVP
Notes NEUROBIT Approved no
Call Number Admin @ si @ AkP2016b Serial 2900
Permanent link to this record
 
 
Author Arnau Baro; Pau Riba; Alicia Fornes
Title Towards the recognition of compound music notes in handwritten music scores Type Conference Article
Year 2016 Publication 15th international conference on Frontiers in Handwriting Recognition Abbreviated Journal
Volume (up) Issue Pages
Keywords
Abstract The recognition of handwritten music scores still remains an open problem. The existing approaches can only deal with very simple handwritten scores mainly because of the variability in the handwriting style and the variability in the composition of groups of music notes (i.e. compound music notes). In this work we focus on this second problem and propose a method based on perceptual grouping for the recognition of compound music notes. Our method has been tested using several handwritten music scores of the CVC-MUSCIMA database and compared with a commercial Optical Music Recognition (OMR) software. Given that our method is learning-free, the obtained results are promising.
Address Shenzhen; China; October 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 2167-6445 ISBN Medium
Area Expedition Conference ICFHR
Notes DAG; 600.097 Approved no
Call Number Admin @ si @ BRF2016 Serial 2903
Permanent link to this record
 
 
Author Yaxing Wang; L. Zhang; Joost Van de Weijer
Title Ensembles of generative adversarial networks Type Conference Article
Year 2016 Publication 30th Annual Conference on Neural Information Processing Systems Worshops Abbreviated Journal
Volume (up) Issue Pages
Keywords
Abstract Ensembles are a popular way to improve results of discriminative CNNs. The
combination of several networks trained starting from different initializations
improves results significantly. In this paper we investigate the usage of ensembles of GANs. The specific nature of GANs opens up several new ways to construct ensembles. The first one is based on the fact that in the minimax game which is played to optimize the GAN objective the generator network keeps on changing even after the network can be considered optimal. As such ensembles of GANs can be constructed based on the same network initialization but just taking models which have different amount of iterations. These so-called self ensembles are much faster to train than traditional ensembles. The second method, called cascade GANs, redirects part of the training data which is badly modeled by the first GAN to another GAN. In experiments on the CIFAR10 dataset we show that ensembles of GANs obtain model probability distributions which better model the data distribution. In addition, we show that these improved results can be obtained at little additional computational cost.
Address Barcelona; Spain; 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 NIPSW
Notes LAMP; 600.068 Approved no
Call Number Admin @ si @ WZW2016 Serial 2905
Permanent link to this record
 
 
Author Guim Perarnau; Joost Van de Weijer; Bogdan Raducanu; Jose Manuel Alvarez
Title Invertible conditional gans for image editing Type Conference Article
Year 2016 Publication 30th Annual Conference on Neural Information Processing Systems Worshops Abbreviated Journal
Volume (up) Issue Pages
Keywords
Abstract Generative Adversarial Networks (GANs) have recently demonstrated to successfully approximate complex data distributions. A relevant extension of this model is conditional GANs (cGANs), where the introduction of external information allows to determine specific representations of the generated images. In this work, we evaluate encoders to inverse the mapping of a cGAN, i.e., mapping a real image into a latent space and a conditional representation. This allows, for example, to reconstruct and modify real images of faces conditioning on arbitrary attributes.
Additionally, we evaluate the design of cGANs. The combination of an encoder
with a cGAN, which we call Invertible cGAN (IcGAN), enables to re-generate real
images with deterministic complex modifications.
Address Barcelona; Spain; 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 NIPSW
Notes LAMP; ADAS; 600.068 Approved no
Call Number Admin @ si @ PWR2016 Serial 2906
Permanent link to this record
 
 
Author Joana Maria Pujadas-Mora; Alicia Fornes; Josep Llados; Anna Cabre
Title Bridging the gap between historical demography and computing: tools for computer-assisted transcription and the analysis of demographic sources Type Book Chapter
Year 2016 Publication The future of historical demography. Upside down and inside out Abbreviated Journal
Volume (up) Issue Pages 127-131
Keywords
Abstract
Address
Corporate Author Thesis
Publisher Acco Publishers Place of Publication Editor K.Matthijs; S.Hin; H.Matsuo; J.Kok
Language Summary Language Original Title
Series Editor Series Title Abbreviated Series Title
Series Volume Series Issue Edition
ISSN ISBN 978-94-6292-722-3 Medium
Area Expedition Conference
Notes DAG; 600.097 Approved no
Call Number Admin @ si @ PFL2016 Serial 2907
Permanent link to this record
 
 
Author Oriol Vicente; Alicia Fornes; Ramon Valdes
Title The Digital Humanities Network of the UABCie: a smart structure of research and social transference for the digital humanities Type Conference Article
Year 2016 Publication Digital Humanities Centres: Experiences and Perspectives Abbreviated Journal
Volume (up) Issue Pages
Keywords
Abstract
Address Warsaw; Poland; 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 DHLABS
Notes DAG; 600.097 Approved no
Call Number Admin @ si @ VFV2016 Serial 2908
Permanent link to this record
 
 
Author Veronica Romero; Alicia Fornes; Enrique Vidal; Joan Andreu Sanchez
Title Using the MGGI Methodology for Category-based Language Modeling in Handwritten Marriage Licenses Books Type Conference Article
Year 2016 Publication 15th international conference on Frontiers in Handwriting Recognition Abbreviated Journal
Volume (up) Issue Pages
Keywords
Abstract Handwritten marriage licenses books have been used for centuries by ecclesiastical and secular institutions to register marriages. The information contained in these historical documents is useful for demography studies and
genealogical research, among others. Despite the generally simple structure of the text in these documents, automatic transcription and semantic information extraction is difficult due to the distinct and evolutionary vocabulary, which is composed mainly of proper names that change along the time. In previous
works we studied the use of category-based language models to both improve the automatic transcription accuracy and make easier the extraction of semantic information. Here we analyze the main causes of the semantic errors observed in previous results and apply a Grammatical Inference technique known as MGGI to improve the semantic accuracy of the language model obtained. Using this language model, full handwritten text recognition experiments have been carried out, with results supporting the interest of the proposed approach.
Address Shenzhen; China; October 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 ICFHR
Notes DAG; 600.097; 602.006 Approved no
Call Number Admin @ si @ RFV2016 Serial 2909
Permanent link to this record