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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  |
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| 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 | ||||
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| 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; Y. Patel; Marçal Rusiñol; C.V. Jawahar; Dimosthenis Karatzas | ||||
| 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 |
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| 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 | ||||
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| Area | Expedition | Conference | CVPR | ||
| Notes | DAG; 600.084; 600.121 | Approved | no | ||
| Call Number | Admin @ si @ GPR2017 | Serial | 2889 | ||
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| 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 |
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| 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. |
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| Corporate Author | Thesis | Ph.D. thesis | |||
| Publisher | Place of Publication | Editor | Dimosthenis Karatzas | ||
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| Notes | DAG | Approved | no | ||
| Call Number | Admin @ si @ Gom2016 | Serial | 2891 | ||
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| Author | Jordi Roca | ||||
| Title | Constancy and inconstancy in categorical colour perception | Type | Book Whole | ||
| Year | 2012 | Publication | PhD Thesis, Universitat Autonoma de Barcelona-CVC | Abbreviated Journal |
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| Abstract | To recognise objects is perhaps the most important task an autonomous system, either biological or artificial needs to perform. In the context of human vision, this is partly achieved by recognizing the colour of surfaces despite changes in the wavelength distribution of the illumination, a property called colour constancy. Correct surface colour recognition may be adequately accomplished by colour category matching without the need to match colours precisely, therefore categorical colour constancy is likely to play an important role for object identification to be successful. The main aim of this work is to study the relationship between colour constancy and categorical colour perception. Previous studies of colour constancy have shown the influence of factors such the spatio-chromatic properties of the background, individual observer's performance, semantics, etc. However there is very little systematic study of these influences. To this end, we developed a new approach to colour constancy which includes both individual observers' categorical perception, the categorical structure of the background, and their interrelations resulting in a more comprehensive characterization of the phenomenon. In our study, we first developed a new method to analyse the categorical structure of 3D colour space, which allowed us to characterize individual categorical colour perception as well as quantify inter-individual variations in terms of shape and centroid location of 3D categorical regions. Second, we developed a new colour constancy paradigm, termed chromatic setting, which allows measuring the precise location of nine categorically-relevant points in colour space under immersive illumination. Additionally, we derived from these measurements a new colour constancy index which takes into account the magnitude and orientation of the chromatic shift, memory effects and the interrelations among colours and a model of colour naming tuned to each observer/adaptation state. Our results lead to the following conclusions: (1) There exists large inter-individual variations in the categorical structure of colour space, and thus colour naming ability varies significantly but this is not well predicted by low-level chromatic discrimination ability; (2) Analysis of the average colour naming space suggested the need for an additional three basic colour terms (turquoise, lilac and lime) for optimal colour communication; (3) Chromatic setting improved the precision of more complex linear colour constancy models and suggested that mechanisms other than cone gain might be best suited to explain colour constancy; (4) The categorical structure of colour space is broadly stable under illuminant changes for categorically balanced backgrounds; (5) Categorical inconstancy exists for categorically unbalanced backgrounds thus indicating that categorical information perceived in the initial stages of adaptation may constrain further categorical perception. | ||||
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| Corporate Author | Thesis | Ph.D. thesis | |||
| Publisher | Place of Publication | Editor | Maria Vanrell;C. Alejandro Parraga | ||
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| Notes | CIC | Approved | no | ||
| Call Number | Admin @ si @ Roc2012 | Serial | 2893 | ||
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| 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 |
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| Volume | Issue | Pages | 225-230 | ||
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| 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. |
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| Address | San Diego; USA; November 2016 | ||||
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| Area | Expedition | Conference | CIC | ||
| Notes | CIC | Approved | no | ||
| Call Number | Admin @ si @ RaV2016a | Serial | 2894 | ||
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| 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 |
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| Address | Barcelona; Spain; August 2016 | ||||
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| Area | Expedition | Conference | ECVP | ||
| Notes | CIC | Approved | no | ||
| Call Number | Admin @ si @ RaV2016b | Serial | 2895 | ||
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| Author | Marçal Rusiñol; Josep Llados | ||||
| Title | Flowchart Recognition in Patent Information Retrieval | Type | Book Chapter | ||
| Year | 2017 | Publication | Current Challenges in Patent Information Retrieval | Abbreviated Journal |
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| Volume | 37 | Issue | Pages | 351-368 | |
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| Publisher | Springer Berlin Heidelberg | Place of Publication | Editor | M. Lupu; K. Mayer; N. Kando; A.J. Trippe | |
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| Notes | DAG; 600.097; 600.121 | Approved | no | ||
| Call Number | Admin @ si @ RuL2017 | Serial | 2896 | ||
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| Author | Alicia Fornes; Josep Llados; Oriol Ramos Terrades; Marçal Rusiñol | ||||
| Title | La Visió per Computador com a Eina per a la Interpretació Automàtica de Fonts Documentals | Type | Journal | ||
| Year | 2016 | Publication | Lligall, Revista Catalana d'Arxivística | Abbreviated Journal |
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| Volume | 39 | Issue | Pages | 20-46 | |
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| Notes | DAG; 600.097 | Approved | no | ||
| Call Number | Admin @ si @ FLR2016 | Serial | 2897 | ||
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| Author | Victor Vaquero; German Ros; Francesc Moreno-Noguer; Antonio Lopez; Alberto Sanfeliu | ||||
| Title | Joint coarse-and-fine reasoning for deep optical flow | Type | Conference Article | ||
| Year | 2017 | Publication | 24th International Conference on Image Processing | Abbreviated Journal |
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| Volume | Issue | Pages | 2558-2562 | ||
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| Abstract | We propose a novel representation for dense pixel-wise estimation tasks using CNNs that boosts accuracy and reduces training time, by explicitly exploiting joint coarse-and-fine reasoning. The coarse reasoning is performed over a discrete classification space to obtain a general rough solution, while the fine details of the solution are obtained over a continuous regression space. In our approach both components are jointly estimated, which proved to be beneficial for improving estimation accuracy. Additionally, we propose a new network architecture, which combines coarse and fine components by treating the fine estimation as a refinement built on top of the coarse solution, and therefore adding details to the general prediction. We apply our approach to the challenging problem of optical flow estimation and empirically validate it against state-of-the-art CNN-based solutions trained from scratch and tested on large optical flow datasets. | ||||
| Address | Beijing; China; September 2017 | ||||
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| Area | Expedition | Conference | ICIP | ||
| Notes | ADAS; 600.118 | Approved | no | ||
| Call Number | Admin @ si @ VRM2017 | Serial | 2898 | ||
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| 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 |
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| Address | Barcelona; Spain; August 2016 | ||||
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| Area | Expedition | Conference | ECVP | ||
| Notes | NEUROBIT | Approved | no | ||
| Call Number | Admin @ si @ AkP2016b | Serial | 2900 | ||
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| Author | C. Alejandro Parraga; Arash Akbarinia | ||||
| Title | Colour Constancy as a Product of Dynamic Centre-Surround Adaptation | Type | Conference Article | ||
| Year | 2016 | Publication | 16th Annual meeting in Vision Sciences Society | Abbreviated Journal |
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| Volume | 16 | Issue | 12 | Pages | |
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| Abstract | Colour constancy refers to the human visual system's ability to preserve the perceived colour of objects despite changes in the illumination. Its exact mechanisms are unknown, although a number of systems ranging from retinal to cortical and memory are thought to play important roles. The strength of the perceptual shift necessary to preserve these colours is usually estimated by the vectorial distances from an ideal match (or canonical illuminant). In this work we explore how much of the colour constancy phenomenon could be explained by well-known physiological properties of V1 and V2 neurons whose receptive fields (RF) vary according to the contrast and orientation of surround stimuli. Indeed, it has been shown that both RF size and the normalization occurring between centre and surround in cortical neurons depend on the local properties of surrounding stimuli. Our stating point is the construction of a computational model which includes this dynamical centre-surround adaptation by means of two overlapping asymmetric Gaussian kernels whose variances are adjusted to the contrast of surrounding pixels to represent the changes in RF size of cortical neurons and the weights of their respective contributions are altered according to differences in centre-surround contrast and orientation. The final output of the model is obtained after convolving an image with this dynamical operator and an estimation of the illuminant is obtained by considering the contrast of the far surround. We tested our algorithm on naturalistic stimuli from several benchmark datasets. Our results show that although our model does not require any training, its performance against the state-of-the-art is highly competitive, even outperforming learning-based algorithms in some cases. Indeed, these results are very encouraging if we consider that they were obtained with the same parameters for all datasets (i.e. just like the human visual system operates). | ||||
| Address | Florida; USA; May 2016 | ||||
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| Area | Expedition | Conference | VSS | ||
| Notes | NEUROBIT | Approved | no | ||
| Call Number | Admin @ si @ PaA2016b | Serial | 2901 | ||
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| Author | Marco Bellantonio; Mohammad A. Haque; Pau Rodriguez; Kamal Nasrollahi; Taisi Telve; Sergio Escalera; Jordi Gonzalez; Thomas B. Moeslund; Pejman Rasti; Golamreza Anbarjafari | ||||
| Title | Spatio-Temporal Pain Recognition in CNN-based Super-Resolved Facial Images | Type | Conference Article | ||
| Year | 2016 | Publication | 23rd International Conference on Pattern Recognition | Abbreviated Journal |
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| Volume | 10165 | Issue | Pages | ||
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| Abstract | Automatic pain detection is a long expected solution to a prevalent medical problem of pain management. This is more relevant when the subject of pain is young children or patients with limited ability to communicate about their pain experience. Computer vision-based analysis of facial pain expression provides a way of efficient pain detection. When deep machine learning methods came into the scene, automatic pain detection exhibited even better performance. In this paper, we figured out three important factors to exploit in automatic pain detection: spatial information available regarding to pain in each of the facial video frames, temporal axis information regarding to pain expression pattern in a subject video sequence, and variation of face resolution. We employed a combination of convolutional neural network and recurrent neural network to setup a deep hybrid pain detection framework that is able to exploit both spatial and temporal pain information from facial video. In order to analyze the effect of different facial resolutions, we introduce a super-resolution algorithm to generate facial video frames with different resolution setups. We investigated the performance on the publicly available UNBC-McMaster Shoulder Pain database. As a contribution, the paper provides novel and important information regarding to the performance of a hybrid deep learning framework for pain detection in facial images of different resolution. | ||||
| Address | Cancun; Mexico; December 2016 | ||||
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| Series Editor | Series Title | Abbreviated Series Title | LNCS | ||
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| Area | Expedition | Conference | ICPR | ||
| Notes | HuPBA; ISE; 600.098; 600.119 | Approved | no | ||
| Call Number | Admin @ si @ BHR2016 | Serial | 2902 | ||
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| 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 |
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| 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 | ||||
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| ISSN | 2167-6445 | ISBN | Medium | ||
| Area | Expedition | Conference | ICFHR | ||
| Notes | DAG; 600.097 | Approved | no | ||
| Call Number | Admin @ si @ BRF2016 | Serial | 2903 | ||
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| 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 |
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| 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. |
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| Address | Barcelona; Spain; December 2016 | ||||
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| Area | Expedition | Conference | NIPSW | ||
| Notes | LAMP; 600.068 | Approved | no | ||
| Call Number | Admin @ si @ WZW2016 | Serial | 2905 | ||
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| 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 |
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| 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. |
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| Address | Barcelona; Spain; December 2016 | ||||
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| Area | Expedition | Conference | NIPSW | ||
| Notes | LAMP; ADAS; 600.068 | Approved | no | ||
| Call Number | Admin @ si @ PWR2016 | Serial | 2906 | ||
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