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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 | |
| 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 | |
| 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 | |
| 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 |
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| 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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| 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 | Issue | Pages | 127-131 | ||
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| Publisher | Acco Publishers | Place of Publication | Editor | K.Matthijs; S.Hin; H.Matsuo; J.Kok | |
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| ISSN | ISBN | 978-94-6292-722-3 | Medium |
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| Notes | DAG; 600.097 | Approved | no | ||
| Call Number | Admin @ si @ PFL2016 | Serial | 2907 | ||
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| 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 | |
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| Address | Warsaw; Poland; December 2016 | ||||
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| Area | Expedition | Conference | DHLABS | ||
| Notes | DAG; 600.097 | Approved | no | ||
| Call Number | Admin @ si @ VFV2016 | Serial | 2908 | ||
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| 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 | |
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| 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. |
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| Address | Shenzhen; China; October 2016 | ||||
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| Area | Expedition | Conference | ICFHR | ||
| Notes | DAG; 600.097; 602.006 | Approved | no | ||
| Call Number | Admin @ si @ RFV2016 | Serial | 2909 | ||
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| Author | Iiris Lusi; Sergio Escalera; Gholamreza Anbarjafari | ||||
| Title | Human Head Pose Estimation on SASE database using Random Hough Regression Forests | Type | Conference Article | ||
| Year | 2016 | Publication | 23rd International Conference on Pattern Recognition Workshops | Abbreviated Journal | |
| Volume | 10165 | Issue | Pages | ||
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| Abstract | 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. | ||||
| Address | Cancun; Mexico; December 2016 | ||||
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| Series Editor | Series Title | Abbreviated Series Title | LNCS | ||
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| Area | Expedition | Conference | ICPRW | ||
| Notes | HuPBA; | Approved | no | ||
| Call Number | Admin @ si @ LEA2016b | Serial | 2910 | ||
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| Author | Xavier Baro; Sergio Escalera; Isabelle Guyon; Julio C. S. Jacques Junior; Lukasz Romaszko; Lisheng Sun; Sebastien Treguer; Evelyne Viegas | ||||
| Title | Coompetitions in machine learning: case studies | Type | Conference Article | ||
| Year | 2016 | Publication | 30th Annual Conference on Neural Information Processing Systems Worshops | Abbreviated Journal | |
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| Address | Barcelona; Spain; December 2016 | ||||
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| Area | Expedition | Conference | NIPSW | ||
| Notes | HuPBA | Approved | no | ||
| Call Number | Admin @ si @ BEG2016 | Serial | 2911 | ||
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| Author | Miguel Oliveira; Victor Santos; Angel Sappa; P. Dias; A. Moreira | ||||
| Title | Incremental texture mapping for autonomous driving | Type | Journal Article | ||
| Year | 2016 | Publication | Robotics and Autonomous Systems | Abbreviated Journal | RAS |
| Volume | 84 | Issue | Pages | 113-128 | |
| Keywords | Scene reconstruction; Autonomous driving; Texture mapping | ||||
| Abstract | Autonomous vehicles have a large number of on-board sensors, not only for providing coverage all around the vehicle, but also to ensure multi-modality in the observation of the scene. Because of this, it is not trivial to come up with a single, unique representation that feeds from the data given by all these sensors. We propose an algorithm which is capable of mapping texture collected from vision based sensors onto a geometric description of the scenario constructed from data provided by 3D sensors. The algorithm uses a constrained Delaunay triangulation to produce a mesh which is updated using a specially devised sequence of operations. These enforce a partial configuration of the mesh that avoids bad quality textures and ensures that there are no gaps in the texture. Results show that this algorithm is capable of producing fine quality textures. | ||||
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| Notes | ADAS; 600.086 | Approved | no | ||
| Call Number | Admin @ si @ OSS2016b | Serial | 2912 | ||
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| Author | Cristhian A. Aguilera-Carrasco; Angel Sappa; Cristhian Aguilera; Ricardo Toledo | ||||
| Title | Cross-Spectral Local Descriptors via Quadruplet Network | Type | Journal Article | ||
| Year | 2017 | Publication | Sensors | Abbreviated Journal | SENS |
| Volume | 17 | Issue | 4 | Pages | 873 |
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| Abstract | This paper presents a novel CNN-based architecture, referred to as Q-Net, to learn local feature descriptors that are useful for matching image patches from two different spectral bands. Given correctly matched and non-matching cross-spectral image pairs, a quadruplet network is trained to map input image patches to a common Euclidean space, regardless of the input spectral band. Our approach is inspired by the recent success of triplet networks in the visible spectrum, but adapted for cross-spectral scenarios, where, for each matching pair, there are always two possible non-matching patches: one for each spectrum. Experimental evaluations on a public cross-spectral VIS-NIR dataset shows that the proposed approach improves the state-of-the-art. Moreover, the proposed technique can also be used in mono-spectral settings, obtaining a similar performance to triplet network descriptors, but requiring less training data. | ||||
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| Notes | ADAS; 600.086; 600.118 | Approved | no | ||
| Call Number | Admin @ si @ ASA2017 | Serial | 2914 | ||
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| Author | Patricia Suarez; Angel Sappa; Boris X. Vintimilla | ||||
| Title | Cross-Spectral Image Patch Similarity using Convolutional Neural Network | Type | Conference Article | ||
| Year | 2017 | Publication | IEEE International Workshop of Electronics, Control, Measurement, Signals and their application to Mechatronics | Abbreviated Journal | |
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| Abstract | 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
classical approaches, showing improvements, and a state of the art CNN based approach. |
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| Address | San Sebastian; Spain; May 2017 | ||||
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| Area | Expedition | Conference | ECMSM | ||
| Notes | ADAS; 600.086; 600.118 | Approved | no | ||
| Call Number | Admin @ si @ SSV2017a | Serial | 2916 | ||
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