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Joana Maria Pujadas-Mora; Alicia Fornes; Josep Llados; Anna Cabre |
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Bridging the gap between historical demography and computing: tools for computer-assisted transcription and the analysis of demographic sources |
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Book Chapter |
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2016 |
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The future of historical demography. Upside down and inside out |
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127-131 |
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Acco Publishers |
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K.Matthijs; S.Hin; H.Matsuo; J.Kok |
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978-94-6292-722-3 |
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DAG; 600.097 |
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Admin @ si @ PFL2016 |
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2907 |
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Oriol Vicente; Alicia Fornes; Ramon Valdes |
![download PDF file pdf](img/file_PDF.gif)
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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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DHLABS |
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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 |
![download PDF file pdf](img/file_PDF.gif)
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Using the MGGI Methodology for Category-based Language Modeling in Handwritten Marriage Licenses Books |
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Conference Article |
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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 |
![goto web page (via DOI) doi](img/doi.gif)
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Title |
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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ICPRW |
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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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Conference Article |
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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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NIPSW |
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HuPBA |
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Admin @ si @ BEG2016 |
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2911 |
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Author |
Patricia Suarez; Angel Sappa; Boris X. Vintimilla |
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Title |
Cross-Spectral Image Patch Similarity using Convolutional Neural Network |
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Conference Article |
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2017 |
Publication |
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
classical approaches, showing improvements, and a state of the art CNN based approach. |
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San Sebastian; Spain; May 2017 |
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ECMSM |
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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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Angel Valencia; Roger Idrovo; Angel Sappa; Douglas Plaza; Daniel Ochoa |
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Title |
A 3D Vision Based Approach for Optimal Grasp of Vacuum Grippers |
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Conference Article |
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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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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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San Sebastian; Spain; May 2017 |
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ADAS; 600.086; 600.118 |
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Admin @ si @ VIS2017 |
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2917 |
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Author |
Cristhian Aguilera; Xavier Soria; Angel Sappa; Ricardo Toledo |
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Title |
RGBN Multispectral Images: a Novel Color Restoration Approach |
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2017 |
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15th International Conference on Practical Applications of Agents and Multi-Agent System |
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Multispectral Imaging; Free Sensor Model; Neural Network |
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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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Porto; Portugal; June 2017 |
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ADAS; MSIAU; 600.118; 600.122 |
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Admin @ si @ ASS2017 |
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2918 |
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Patricia Suarez; Angel Sappa; Boris X. Vintimilla |
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Title |
Learning to Colorize Infrared Images |
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Conference Article |
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2017 |
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15th International Conference on Practical Applications of Agents and Multi-Agent System |
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CNN in multispectral imaging; Image colorization |
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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. |
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Porto; Portugal; June 2017 |
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ADAS; MSIAU; 600.086; 600.122; 600.118 |
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Admin @ si @ |
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2919 |
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Patricia Suarez; Angel Sappa; Boris X. Vintimilla |
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Title |
Infrared Image Colorization based on a Triplet DCGAN Architecture |
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2017 |
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IEEE Conference on Computer Vision and Pattern Recognition Workshops |
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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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Honolulu; Hawaii; USA; July 2017 |
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CVPRW |
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ADAS; 600.086; 600.118 |
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Admin @ si @ SSV2017b |
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2920 |
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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 |
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Title |
Design of an Explainable Machine Learning Challenge for Video Interviews |
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2017 |
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International Joint Conference on Neural Networks |
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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. |
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Anchorage; Alaska; USA; May 2017 |
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IJCNN |
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HUPBA; no proj |
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Admin @ si @ EGE2017 |
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2922 |
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Julio C. S. Jacques Junior; Xavier Baro; Sergio Escalera |
![download PDF file pdf](img/file_PDF.gif)
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Title |
Exploiting feature representations through similarity learning and ranking aggregation for person re-identification |
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Conference Article |
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2017 |
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12th IEEE International Conference on Automatic Face and Gesture Recognition |
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Person re-identification has received special attentionby the human analysis community in the last few years.To address the challenges in this field, many researchers haveproposed different strategies, which basically exploit eithercross-view invariant features or cross-view robust metrics. Inthis work we propose to combine different feature representationsthrough ranking aggregation. Spatial information, whichpotentially benefits the person matching, is represented usinga 2D body model, from which color and texture informationare extracted and combined. We also consider contextualinformation (background and foreground data), automaticallyextracted via Deep Decompositional Network, and the usage ofConvolutional Neural Network (CNN) features. To describe thematching between images we use the polynomial feature map,also taking into account local and global information. Finally,the Stuart ranking aggregation method is employed to combinecomplementary ranking lists obtained from different featurerepresentations. Experimental results demonstrated that weimprove the state-of-the-art on VIPeR and PRID450s datasets,achieving 58.77% and 71.56% on top-1 rank recognitionrate, respectively, as well as obtaining competitive results onCUHK01 dataset. |
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Washington; DC; USA; May 2017 |
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HUPBA; 602.143 |
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Admin @ si @ JBE2017 |
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2923 |
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Iiris Lusi; Julio C. S. Jacques Junior; Jelena Gorbova; Xavier Baro; Sergio Escalera; Hasan Demirel; Juri Allik; Cagri Ozcinar; Gholamreza Anbarjafari |
![goto web page (via DOI) doi](img/doi.gif)
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Joint Challenge on Dominant and Complementary Emotion Recognition Using Micro Emotion Features and Head-Pose Estimation: Databases |
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2017 |
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12th IEEE International Conference on Automatic Face and Gesture Recognition |
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In this work two databases for the Joint Challenge on Dominant and Complementary Emotion Recognition Using Micro Emotion Features and Head-Pose Estimation1 are introduced. Head pose estimation paired with and detailed emotion recognition have become very important in relation to human-computer interaction. The 3D head pose database, SASE, is a 3D database acquired with Microsoft Kinect 2 camera, including RGB and depth information of different head poses which is composed by a total of 30000 frames with annotated markers, including 32 male and 18 female subjects. For the dominant and complementary emotion database, iCVMEFED, includes 31250 images with different emotions of 115 subjects whose gender distribution is almost uniform. For each subject there are 5 samples. The emotions are composed by 7 basic emotions plus neutral, being defined as complementary and dominant pairs. The emotion associated to the images were labeled with the support of psychologists. |
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Washington; DC; USA; May 2017 |
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HUPBA; no menciona |
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Admin @ si @ LJG2017 |
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2924 |
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Chirster Loob; Pejman Rasti; Iiris Lusi; Julio C. S. Jacques Junior; Xavier Baro; Sergio Escalera; Tomasz Sapinski; Dorota Kaminska; Gholamreza Anbarjafari |
![download PDF file pdf](img/file_PDF.gif)
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Dominant and Complementary Multi-Emotional Facial Expression Recognition Using C-Support Vector Classification |
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2017 |
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12th IEEE International Conference on Automatic Face and Gesture Recognition |
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We are proposing a new facial expression recognition model which introduces 30+ detailed facial expressions recognisable by any artificial intelligence interacting with a human. Throughout this research, we introduce two categories for the emotions, namely, dominant emotions and complementary emotions. In this research paper the complementary emotion is recognised by using the eye region if the dominant emotion is angry, fearful or sad, and if the dominant emotion is disgust or happiness the complementary emotion is mainly conveyed by the mouth. In order to verify the tagged dominant and complementary emotions, randomly chosen people voted for the recognised multi-emotional facial expressions. The average results of voting are showing that 73.88% of the voters agree on the correctness of the recognised multi-emotional facial expressions. |
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Washington; DC; USA; May 2017 |
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Admin @ si @ LRL2017 |
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2925 |
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Pau Rodriguez; Jordi Gonzalez; Jordi Cucurull; Josep M. Gonfaus; Xavier Roca |
![download PDF file pdf](img/file_PDF.gif)
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Regularizing CNNs with Locally Constrained Decorrelations |
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2017 |
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5th International Conference on Learning Representations |
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Toulon; France; April 2017 |
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ICLR |
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ISE; 602.143; 600.119; 600.098 |
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Admin @ si @ RGC2017 |
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2927 |
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