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Albert Berenguel; Oriol Ramos Terrades; Josep Llados; Cristina Cañero |
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Title |
Banknote counterfeit detection through background texture printing analysis |
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2016 |
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12th IAPR Workshop on Document Analysis Systems |
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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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Rumania; May 2016 |
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DAS |
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DAG; 600.061; 601.269; 600.097 |
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Admin @ si @ BRL2016 |
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2950 |
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Lluis Gomez; Y. Patel; Marçal Rusiñol; C.V. Jawahar; Dimosthenis Karatzas |
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Title |
Self‐supervised learning of visual features through embedding images into text topic spaces |
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Conference Article |
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2017 |
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30th IEEE Conference on Computer Vision and Pattern Recognition |
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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. |
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Honolulu; Hawaii; July 2017 |
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CVPR |
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DAG; 600.084; 600.121 |
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Admin @ si @ GPR2017 |
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2889 |
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Ivet Rafegas; Maria Vanrell |
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Title |
Color spaces emerging from deep convolutional networks |
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2016 |
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24th Color and Imaging Conference |
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225-230 |
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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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San Diego; USA; November 2016 |
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CIC |
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CIC |
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Admin @ si @ RaV2016a |
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2894 |
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Author |
Victor Vaquero; German Ros; Francesc Moreno-Noguer; Antonio Lopez; Alberto Sanfeliu |
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Title |
Joint coarse-and-fine reasoning for deep optical flow |
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Conference Article |
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2017 |
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24th International Conference on Image Processing |
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2558-2562 |
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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. |
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Beijing; China; September 2017 |
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ICIP |
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ADAS; 600.118 |
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Admin @ si @ VRM2017 |
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2898 |
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Author |
Arash Akbarinia; C. Alejandro Parraga |
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Dynamically Adjusted Surround Contrast Enhances Boundary Detection, European Conference on Visual Perception |
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Conference Article |
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2016 |
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European Conference on Visual Perception |
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Barcelona; Spain; August 2016 |
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ECVP |
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NEUROBIT |
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Admin @ si @ AkP2016b |
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2900 |
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C. Alejandro Parraga; Arash Akbarinia |
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Title |
Colour Constancy as a Product of Dynamic Centre-Surround Adaptation |
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Conference Article |
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2016 |
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16th Annual meeting in Vision Sciences Society |
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16 |
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12 |
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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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Florida; USA; May 2016 |
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VSS |
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NEUROBIT |
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Admin @ si @ PaA2016b |
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2901 |
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Marco Bellantonio; Mohammad A. Haque; Pau Rodriguez; Kamal Nasrollahi; Taisi Telve; Sergio Escalera; Jordi Gonzalez; Thomas B. Moeslund; Pejman Rasti; Golamreza Anbarjafari |
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Title |
Spatio-Temporal Pain Recognition in CNN-based Super-Resolved Facial Images |
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Conference Article |
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2016 |
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23rd International Conference on Pattern Recognition |
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10165 |
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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. |
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Cancun; Mexico; December 2016 |
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LNCS |
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ICPR |
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HuPBA; ISE; 600.098; 600.119 |
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Admin @ si @ BHR2016 |
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2902 |
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Arnau Baro; Pau Riba; Alicia Fornes |
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Towards the recognition of compound music notes in handwritten music scores |
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2016 |
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15th international conference on Frontiers in Handwriting Recognition |
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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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Shenzhen; China; October 2016 |
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2167-6445 |
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ICFHR |
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DAG; 600.097 |
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Admin @ si @ BRF2016 |
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2903 |
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Yaxing Wang; L. Zhang; Joost Van de Weijer |
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Ensembles of generative adversarial networks |
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2016 |
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30th Annual Conference on Neural Information Processing Systems Worshops |
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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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Barcelona; Spain; December 2016 |
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LAMP; 600.068 |
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Admin @ si @ WZW2016 |
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2905 |
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Author |
Guim Perarnau; Joost Van de Weijer; Bogdan Raducanu; Jose Manuel Alvarez |
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Invertible conditional gans for image editing |
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Conference Article |
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2016 |
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30th Annual Conference on Neural Information Processing Systems Worshops |
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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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Barcelona; Spain; December 2016 |
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LAMP; ADAS; 600.068 |
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Admin @ si @ PWR2016 |
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2906 |
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Oriol Vicente; Alicia Fornes; Ramon Valdes |
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The Digital Humanities Network of the UABCie: a smart structure of research and social transference for the digital humanities |
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2016 |
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Digital Humanities Centres: Experiences and Perspectives |
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Warsaw; Poland; December 2016 |
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DAG; 600.097 |
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Admin @ si @ VFV2016 |
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2908 |
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Veronica Romero; Alicia Fornes; Enrique Vidal; Joan Andreu Sanchez |
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Using the MGGI Methodology for Category-based Language Modeling in Handwritten Marriage Licenses Books |
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2016 |
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15th international conference on Frontiers in Handwriting Recognition |
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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. |
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Shenzhen; China; October 2016 |
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ICFHR |
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DAG; 600.097; 602.006 |
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Admin @ si @ RFV2016 |
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2909 |
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Iiris Lusi; Sergio Escalera; Gholamreza Anbarjafari |
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Human Head Pose Estimation on SASE database using Random Hough Regression Forests |
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2016 |
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23rd International Conference on Pattern Recognition Workshops |
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10165 |
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In recent years head pose estimation has become an important task in face analysis scenarios. Given the availability of high resolution 3D sensors, the design of a high resolution head pose database would be beneficial for the community. In this paper, Random Hough Forests are used to estimate 3D head pose and location on a new 3D head database, SASE, which represents the baseline performance on the new data for an upcoming international head pose estimation competition. The data in SASE is acquired with a Microsoft Kinect 2 camera, including the RGB and depth information of 50 subjects with a large sample of head poses, allowing us to test methods for real-life scenarios. We briefly review the database while showing baseline head pose estimation results based on Random Hough Forests. |
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Cancun; Mexico; December 2016 |
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HuPBA; |
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Admin @ si @ LEA2016b |
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2910 |
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Xavier Baro; Sergio Escalera; Isabelle Guyon; Julio C. S. Jacques Junior; Lukasz Romaszko; Lisheng Sun; Sebastien Treguer; Evelyne Viegas |
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Coompetitions in machine learning: case studies |
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2016 |
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30th Annual Conference on Neural Information Processing Systems Worshops |
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Barcelona; Spain; December 2016 |
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Patricia Suarez; Angel Sappa; Boris X. Vintimilla |
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Cross-Spectral Image Patch Similarity using Convolutional Neural Network |
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2017 |
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IEEE International Workshop of Electronics, Control, Measurement, Signals and their application to Mechatronics |
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The ability to compare image regions (patches) has been the basis of many approaches to core computer vision problems, including object, texture and scene categorization. Hence, developing representations for image patches have been of interest in several works. The current work focuses on learning similarity between cross-spectral image patches with a 2 channel convolutional neural network (CNN) model. The proposed approach is an adaptation of a previous work, trying to obtain similar results than the state of the art but with a lowcost hardware. Hence, obtained results are compared with both
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San Sebastian; Spain; May 2017 |
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ADAS; 600.086; 600.118 |
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Admin @ si @ SSV2017a |
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2916 |
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