Anjan Dutta, Umapada Pal, & Josep Llados. (2016). Compact Correlated Features for Writer Independent Signature Verification. In 23rd International Conference on Pattern Recognition.
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.
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H. Martin Kjer, Jens Fagertun, Sergio Vera, Debora Gil, Miguel Angel Gonzalez Ballester, & Rasmus R. Paulsena. (2016). Free-form image registration of human cochlear uCT data using skeleton similarity as anatomical prior. PRL - Patter Recognition Letters, 76(1), 76–82.
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Albert Berenguel, Oriol Ramos Terrades, Josep Llados, & Cristina Cañero. (2016). Banknote counterfeit detection through background texture printing analysis. In 12th IAPR Workshop on Document Analysis Systems.
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.
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Marc Sunset Perez, Marc Comino Trinidad, Dimosthenis Karatzas, Antonio Chica Calaf, & Pere Pau Vazquez Alcocer. (2016). Development of general‐purpose projection‐based augmented reality systems. IADIs - IADIs international journal on computer science and information systems, 1–18.
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
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Lluis Gomez. (2016). Exploiting Similarity Hierarchies for Multi-script Scene Text Understanding (Dimosthenis Karatzas, Ed.). Ph.D. thesis, , .
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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Ivet Rafegas, & Maria Vanrell. (2016). Color spaces emerging from deep convolutional networks. In 24th Color and Imaging Conference (pp. 225–230).
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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Ivet Rafegas, & Maria Vanrell. (2016). Colour Visual Coding in trained Deep Neural Networks. In European Conference on Visual Perception.
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Alicia Fornes, Josep Llados, Oriol Ramos Terrades, & Marçal Rusiñol. (2016). La Visió per Computador com a Eina per a la Interpretació Automàtica de Fonts Documentals. Lligall, Revista Catalana d'Arxivística, 20–46.
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Arash Akbarinia, & C. Alejandro Parraga. (2016). Dynamically Adjusted Surround Contrast Enhances Boundary Detection, European Conference on Visual Perception. In European Conference on Visual Perception.
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C. Alejandro Parraga, & Arash Akbarinia. (2016). Colour Constancy as a Product of Dynamic Centre-Surround Adaptation. In 16th Annual meeting in Vision Sciences Society (Vol. 16).
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).
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Arnau Baro, Pau Riba, & Alicia Fornes. (2016). Towards the recognition of compound music notes in handwritten music scores. In 15th international conference on Frontiers in Handwriting Recognition.
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.
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Yaxing Wang, L. Zhang, & Joost Van de Weijer. (2016). Ensembles of generative adversarial networks. In 30th Annual Conference on Neural Information Processing Systems Worshops.
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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Guim Perarnau, Joost Van de Weijer, Bogdan Raducanu, & Jose Manuel Alvarez. (2016). Invertible conditional gans for image editing. In 30th Annual Conference on Neural Information Processing Systems Worshops.
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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Joana Maria Pujadas-Mora, Alicia Fornes, Josep Llados, & Anna Cabre. (2016). Bridging the gap between historical demography and computing: tools for computer-assisted transcription and the analysis of demographic sources. In K.Matthijs, S.Hin, H.Matsuo, & J.Kok (Eds.), The future of historical demography. Upside down and inside out (pp. 127–131). Acco Publishers.
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Oriol Vicente, Alicia Fornes, & Ramon Valdes. (2016). The Digital Humanities Network of the UABCie: a smart structure of research and social transference for the digital humanities. In Digital Humanities Centres: Experiences and Perspectives.
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