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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 | ||
Area | Expedition | Conference | |||
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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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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Author | Angel Valencia; Roger Idrovo; Angel Sappa; Douglas Plaza; Daniel Ochoa | ||||
Title | A 3D Vision Based Approach for Optimal Grasp of Vacuum Grippers | 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 | In general, robot grasping approaches are based on the usage of multi-finger grippers. However, when large size objects need to be manipulated vacuum grippers are preferred, instead of finger based grippers. This paper aims to estimate the best picking place for a two suction cups vacuum gripper,
when planar objects with an unknown size and geometry are considered. The approach is based on the estimation of geometric properties of object’s shape from a partial cloud of points (a single 3D view), in such a way that combine with considerations of a theoretical model to generate an optimal contact point that minimizes the vacuum force needed to guarantee a grasp. Experimental results in real scenarios are presented to show the validity of the proposed 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 @ VIS2017 | Serial | 2917 | ||
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Author | Cristhian Aguilera; Xavier Soria; Angel Sappa; Ricardo Toledo | ||||
Title | RGBN Multispectral Images: a Novel Color Restoration Approach | Type | Conference Article | ||
Year | 2017 | Publication | 15th International Conference on Practical Applications of Agents and Multi-Agent System | Abbreviated Journal | |
Volume | Issue | Pages | |||
Keywords | Multispectral Imaging; Free Sensor Model; Neural Network | ||||
Abstract | This paper describes a color restoration technique used to remove NIR information from single sensor cameras where color and near-infrared images are simultaneously acquired|referred to in the literature as RGBN images. The proposed approach is based on a neural network architecture that learns the NIR information contained in the RGBN images. The proposed approach is evaluated on real images obtained by using a pair of RGBN cameras. Additionally, qualitative comparisons with a nave color correction technique based on mean square
error minimization are provided. |
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Address | Porto; Portugal; June 2017 | ||||
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Area | Expedition | Conference | PAAMS | ||
Notes | ADAS; MSIAU; 600.118; 600.122 | Approved | no | ||
Call Number | Admin @ si @ ASS2017 | Serial | 2918 | ||
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Author | Patricia Suarez; Angel Sappa; Boris X. Vintimilla | ||||
Title | Learning to Colorize Infrared Images | Type | Conference Article | ||
Year | 2017 | Publication | 15th International Conference on Practical Applications of Agents and Multi-Agent System | Abbreviated Journal | |
Volume | Issue | Pages | |||
Keywords | CNN in multispectral imaging; Image colorization | ||||
Abstract | This paper focuses on near infrared (NIR) image colorization by using a Generative Adversarial Network (GAN) architecture model. The proposed architecture consists of two stages. Firstly, it learns to colorize the given input, resulting in a RGB image. Then, in the second stage, a discriminative model is used to estimate the probability that the generated image came from the training dataset, rather than the image automatically generated. The proposed model starts the learning process from scratch, because our set of images is very dierent from the dataset used in existing pre-trained models, so transfer learning strategies cannot be used. Infrared image colorization is an important problem when human perception need to be considered, e.g, in remote sensing applications. Experimental results with a large set of real images are provided showing the validity of the proposed approach. | ||||
Address | Porto; Portugal; June 2017 | ||||
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Area | Expedition | Conference | PAAMS | ||
Notes | ADAS; MSIAU; 600.086; 600.122; 600.118 | Approved | no | ||
Call Number | Admin @ si @ | Serial | 2919 | ||
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Author | Patricia Suarez; Angel Sappa; Boris X. Vintimilla | ||||
Title | Infrared Image Colorization based on a Triplet DCGAN Architecture | Type | Conference Article | ||
Year | 2017 | Publication | IEEE Conference on Computer Vision and Pattern Recognition Workshops | Abbreviated Journal | |
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Abstract | This paper proposes a novel approach for colorizing near infrared (NIR) images using Deep Convolutional Generative Adversarial Network (GAN) architectures. The proposed approach is based on the usage of a triplet model for learning each color channel independently, in a more homogeneous way. It allows a fast convergence during the training, obtaining a greater similarity between the given NIR image and the corresponding ground truth. The proposed approach has been evaluated with a large data set of NIR images and compared with a recent approach, which is also based on a GAN architecture but in this case all the
color channels are obtained at the same time. |
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Address | Honolulu; Hawaii; USA; July 2017 | ||||
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Area | Expedition | Conference | CVPRW | ||
Notes | ADAS; 600.086; 600.118 | Approved | no | ||
Call Number | Admin @ si @ SSV2017b | Serial | 2920 | ||
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Author | Mikhail Mozerov; Joost Van de Weijer | ||||
Title | Improved Recursive Geodesic Distance Computation for Edge Preserving Filter | Type | Journal Article | ||
Year | 2017 | Publication | IEEE Transactions on Image Processing | Abbreviated Journal | TIP |
Volume | 26 | Issue | 8 | Pages | 3696 - 3706 |
Keywords | Geodesic distance filter; color image filtering; image enhancement | ||||
Abstract | All known recursive filters based on the geodesic distance affinity are realized by two 1D recursions applied in two orthogonal directions of the image plane. The 2D extension of the filter is not valid and has theoretically drawbacks, which lead to known artifacts. In this paper, a maximum influence propagation method is proposed to approximate the 2D extension for the
geodesic distance-based recursive filter. The method allows to partially overcome the drawbacks of the 1D recursion approach. We show that our improved recursion better approximates the true geodesic distance filter, and the application of this improved filter for image denoising outperforms the existing recursive implementation of the geodesic distance. As an application, we consider a geodesic distance-based filter for image denoising. Experimental evaluation of our denoising method demonstrates comparable and for several test images better results, than stateof-the-art approaches, while our algorithm is considerably fasterwith computational complexity O(8P). |
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Notes | LAMP; ISE; 600.120; 600.098; 600.119 | Approved | no | ||
Call Number | Admin @ si @ Moz2017 | Serial | 2921 | ||
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Author | Hugo Jair Escalante; Isabelle Guyon; Sergio Escalera; Julio C. S. Jacques Junior; Xavier Baro; Evelyne Viegas; Yagmur Gucluturk; Umut Guclu; Marcel A. J. van Gerven; Rob van Lier; Meysam Madadi; Stephane Ayache | ||||
Title | Design of an Explainable Machine Learning Challenge for Video Interviews | Type | Conference Article | ||
Year | 2017 | Publication | International Joint Conference on Neural Networks | Abbreviated Journal | |
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Abstract | This paper reviews and discusses research advances on “explainable machine learning” in computer vision. We focus on a particular area of the “Looking at People” (LAP) thematic domain: first impressions and personality analysis. Our aim is to make the computational intelligence and computer vision communities aware of the importance of developing explanatory mechanisms for computer-assisted decision making applications, such as automating recruitment. Judgments based on personality traits are being made routinely by human resource departments to evaluate the candidates' capacity of social insertion and their potential of career growth. However, inferring personality traits and, in general, the process by which we humans form a first impression of people, is highly subjective and may be biased. Previous studies have demonstrated that learning machines can learn to mimic human decisions. In this paper, we go one step further and formulate the problem of explaining the decisions of the models as a means of identifying what visual aspects are important, understanding how they relate to decisions suggested, and possibly gaining insight into undesirable negative biases. We design a new challenge on explainability of learning machines for first impressions analysis. We describe the setting, scenario, evaluation metrics and preliminary outcomes of the competition. To the best of our knowledge this is the first effort in terms of challenges for explainability in computer vision. In addition our challenge design comprises several other quantitative and qualitative elements of novelty, including a “coopetition” setting, which combines competition and collaboration. | ||||
Address | Anchorage; Alaska; USA; May 2017 | ||||
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Area | Expedition | Conference | IJCNN | ||
Notes | HUPBA; no proj | Approved | no | ||
Call Number | Admin @ si @ EGE2017 | Serial | 2922 | ||
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