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Author ![sorted by Author field, ascending order (up)](img/sort_asc.gif) |
A. Dupuy; Joan Serrat; Jordi Vitria; J. Pladellorens |
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Analysis of gammagraphic images by mathematical morphology. |
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Conference Article |
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1991 |
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Pattern Recognition and image Analysis: IV Spanish Symposium of Pattern Recognition and image Analysis, World Scientific Pub. |
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ADAS;OR;MV |
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ADAS @ adas @ DSV1991 |
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262 |
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A. M. Here; B. C. Lopez; Debora Gil; J. J. Camarero; Jordi Martinez-Vilalta |
![download PDF file pdf](img/file_PDF.gif)
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Title |
A new software to analyse wood anatomical features in conifer species |
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2013 |
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International Symposium on Wood Structure in Plant Biology and Ecology |
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International Symposium on Wood Structure in Plant Biology and Ecology |
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Naples; Italy; March 2013 |
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WSE |
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IAM |
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no |
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Admin @ si @ HLG2013 |
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2303 |
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Author ![sorted by Author field, ascending order (up)](img/sort_asc.gif) |
A. Martinez; Jordi Vitria |
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Title |
From Visual Scanning to Object Recognition |
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1997 |
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(SNRFAI’97) 7th Spanish National Symposium on Pattern Recognition and Image Analysis. |
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Barcelona |
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OR;MV |
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BCNPCL @ bcnpcl @ MaV1997c |
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58 |
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A. Pujol; Felipe Lumbreras; Javier Varona; Juan J. Villanueva |
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Locating people in indoor scenes for real applications. |
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Conference Article |
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2000 |
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15 th International Conference on Pattern Recognition |
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4 |
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632-635 |
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Barcelona. |
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ADAS |
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ADAS @ adas @ PLV2000 |
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237 |
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A. Pujol; Jordi Vitria; Petia Radeva; Xavier Binefa; Robert Benavente; Ernest Valveny; Craig Von Land |
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Title |
Real time pharmaceutical product recognition using color and shape indexing. |
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Conference Article |
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1999 |
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Proceedings of the 2nd International Workshop on European Scientific and Industrial Collaboration (WESIC´99), Promotoring Advanced Technologies in Manufacturing. |
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Wales |
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OR;MILAB;DAG;CIC;MV |
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BCNPCL @ bcnpcl @ PVR1999 |
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24 |
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Author ![sorted by Author field, ascending order (up)](img/sort_asc.gif) |
A. Pujol; Juan J. Villanueva; H. Wechsler |
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Title |
Automatic View Based Caricaturing. |
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Conference Article |
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2000 |
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15 th International Conference on Pattern Recognition |
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1 |
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1072-1075 |
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Barcelona. |
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no |
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ISE @ ise @ PVW2000 |
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227 |
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Author ![sorted by Author field, ascending order (up)](img/sort_asc.gif) |
Abel Gonzalez-Garcia; Davide Modolo; Vittorio Ferrari |
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Title |
Objects as context for detecting their semantic parts |
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Conference Article |
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2018 |
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31st IEEE Conference on Computer Vision and Pattern Recognition |
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6907 - 6916 |
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Proposals; Semantics; Wheels; Automobiles; Context modeling; Task analysis; Object detection |
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We present a semantic part detection approach that effectively leverages object information. We use the object appearance and its class as indicators of what parts to expect. We also model the expected relative location of parts inside the objects based on their appearance. We achieve this with a new network module, called OffsetNet, that efficiently predicts a variable number of part locations within a given object. Our model incorporates all these cues to
detect parts in the context of their objects. This leads to considerably higher performance for the challenging task of part detection compared to using part appearance alone (+5 mAP on the PASCAL-Part dataset). We also compare
to other part detection methods on both PASCAL-Part and CUB200-2011 datasets. |
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Salt Lake City; USA; June 2018 |
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CVPR |
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LAMP; 600.109; 600.120 |
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no |
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Admin @ si @ GMF2018 |
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3229 |
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Author ![sorted by Author field, ascending order (up)](img/sort_asc.gif) |
Abel Gonzalez-Garcia; Joost Van de Weijer; Yoshua Bengio |
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Title |
Image-to-image translation for cross-domain disentanglement |
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Conference Article |
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2018 |
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32nd Annual Conference on Neural Information Processing Systems |
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Montreal; Canada; December 2018 |
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NIPS |
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LAMP; 600.120 |
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no |
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Admin @ si @ GWB2018 |
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3155 |
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Author ![sorted by Author field, ascending order (up)](img/sort_asc.gif) |
Adam Fodor; Rachid R. Saboundji; Julio C. S. Jacques Junior; Sergio Escalera; David Gallardo Pujol; Andras Lorincz |
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Title |
Multimodal Sentiment and Personality Perception Under Speech: A Comparison of Transformer-based Architectures |
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Conference Article |
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2022 |
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Understanding Social Behavior in Dyadic and Small Group Interactions |
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173 |
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218-241 |
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Human-machine, human-robot interaction, and collaboration appear in diverse fields, from homecare to Cyber-Physical Systems. Technological development is fast, whereas real-time methods for social communication analysis that can measure small changes in sentiment and personality states, including visual, acoustic and language modalities are lagging, particularly when the goal is to build robust, appearance invariant, and fair methods. We study and compare methods capable of fusing modalities while satisfying real-time and invariant appearance conditions. We compare state-of-the-art transformer architectures in sentiment estimation and introduce them in the much less explored field of personality perception. We show that the architectures perform differently on automatic sentiment and personality perception, suggesting that each task may be better captured/modeled by a particular method. Our work calls attention to the attractive properties of the linear versions of the transformer architectures. In particular, we show that the best results are achieved by fusing the different architectures{’} preprocessing methods. However, they pose extreme conditions in computation power and energy consumption for real-time computations for quadratic transformers due to their memory requirements. In turn, linear transformers pave the way for quantifying small changes in sentiment estimation and personality perception for real-time social communications for machines and robots. |
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PMLR |
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HuPBA; no menciona |
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no |
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Admin @ si @ FSJ2022 |
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3769 |
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Author ![sorted by Author field, ascending order (up)](img/sort_asc.gif) |
Adarsh Tiwari; Sanket Biswas; Josep Llados |
![goto web page url](img/www.gif)
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Title |
Can Pre-trained Language Models Help in Understanding Handwritten Symbols? |
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Conference Article |
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2023 |
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17th International Conference on Document Analysis and Recognition |
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14193 |
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199–211 |
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The emergence of transformer models like BERT, GPT-2, GPT-3, RoBERTa, T5 for natural language understanding tasks has opened the floodgates towards solving a wide array of machine learning tasks in other modalities like images, audio, music, sketches and so on. These language models are domain-agnostic and as a result could be applied to 1-D sequences of any kind. However, the key challenge lies in bridging the modality gap so that they could generate strong features beneficial for out-of-domain tasks. This work focuses on leveraging the power of such pre-trained language models and discusses the challenges in predicting challenging handwritten symbols and alphabets. |
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San Jose; CA; USA; August 2023 |
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ICDAR |
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DAG |
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no |
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Admin @ si @ TBL2023 |
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3908 |
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Author ![sorted by Author field, ascending order (up)](img/sort_asc.gif) |
Adela Barbulescu; Wenjuan Gong; Jordi Gonzalez; Thomas B. Moeslund; Xavier Roca |
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Title |
3D Human Pose Estimation Using 2D Body Part Detectors |
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2012 |
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21st International Conference on Pattern Recognition |
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2484 - 2487 |
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Automatic 3D reconstruction of human poses from monocular images is a challenging and popular topic in the computer vision community, which provides a wide range of applications in multiple areas. Solutions for 3D pose estimation involve various learning approaches, such as support vector machines and Gaussian processes, but many encounter difficulties in cluttered scenarios and require additional input data, such as silhouettes, or controlled camera settings. We present a framework that is capable of estimating the 3D pose of a person from single images or monocular image sequences without requiring background information and which is robust to camera variations. The framework models the non-linearity present in human pose estimation as it benefits from flexible learning approaches, including a highly customizable 2D detector. Results on the HumanEva benchmark show how they perform and influence the quality of the 3D pose estimates. |
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Tsubuka, Japan |
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1051-4651 |
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978-1-4673-2216-4 |
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ISE |
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no |
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Admin @ si @ BGG2012 |
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2172 |
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Author ![sorted by Author field, ascending order (up)](img/sort_asc.gif) |
Adria Molina; Lluis Gomez; Oriol Ramos Terrades; Josep Llados |
![download PDF file pdf](img/file_PDF.gif)
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Title |
A Generic Image Retrieval Method for Date Estimation of Historical Document Collections |
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Conference Article |
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2022 |
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Document Analysis Systems.15th IAPR International Workshop, (DAS2022) |
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13237 |
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583–597 |
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Date estimation; Document retrieval; Image retrieval; Ranking loss; Smooth-nDCG |
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Date estimation of historical document images is a challenging problem, with several contributions in the literature that lack of the ability to generalize from one dataset to others. This paper presents a robust date estimation system based in a retrieval approach that generalizes well in front of heterogeneous collections. We use a ranking loss function named smooth-nDCG to train a Convolutional Neural Network that learns an ordination of documents for each problem. One of the main usages of the presented approach is as a tool for historical contextual retrieval. It means that scholars could perform comparative analysis of historical images from big datasets in terms of the period where they were produced. We provide experimental evaluation on different types of documents from real datasets of manuscript and newspaper images. |
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La Rochelle, France; May 22–25, 2022 |
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DAS |
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DAG; 600.140; 600.121 |
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no |
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Admin @ si @ MGR2022 |
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3694 |
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Author ![sorted by Author field, ascending order (up)](img/sort_asc.gif) |
Adria Molina; Pau Riba; Lluis Gomez; Oriol Ramos Terrades; Josep Llados |
![download PDF file pdf](img/file_PDF.gif)
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Title |
Date Estimation in the Wild of Scanned Historical Photos: An Image Retrieval Approach |
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Conference Article |
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2021 |
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16th International Conference on Document Analysis and Recognition |
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12822 |
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306-320 |
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This paper presents a novel method for date estimation of historical photographs from archival sources. The main contribution is to formulate the date estimation as a retrieval task, where given a query, the retrieved images are ranked in terms of the estimated date similarity. The closer are their embedded representations the closer are their dates. Contrary to the traditional models that design a neural network that learns a classifier or a regressor, we propose a learning objective based on the nDCG ranking metric. We have experimentally evaluated the performance of the method in two different tasks: date estimation and date-sensitive image retrieval, using the DEW public database, overcoming the baseline methods. |
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Lausanne; Suissa; September 2021 |
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DAG; 600.121; 600.140; 110.312 |
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Admin @ si @ MRG2021b |
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3571 |
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Author ![sorted by Author field, ascending order (up)](img/sort_asc.gif) |
Adria Rico; Alicia Fornes |
![download PDF file pdf](img/file_PDF.gif)
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Title |
Camera-based Optical Music Recognition using a Convolutional Neural Network |
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2017 |
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12th IAPR International Workshop on Graphics Recognition |
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27-28 |
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optical music recognition; document analysis; convolutional neural network; deep learning |
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Optical Music Recognition (OMR) consists in recognizing images of music scores. Contrary to expectation, the current OMR systems usually fail when recognizing images of scores captured by digital cameras and smartphones. In this work, we propose a camera-based OMR system based on Convolutional Neural Networks, showing promising preliminary results |
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Author ![sorted by Author field, ascending order (up)](img/sort_asc.gif) |
Adria Ruiz; Joost Van de Weijer; Xavier Binefa |
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Regularized Multi-Concept MIL for weakly-supervised facial behavior categorization |
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2014 |
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25th British Machine Vision Conference |
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We address the problem of estimating high-level semantic labels for videos of recorded people by means of analysing their facial expressions. This problem, to which we refer as facial behavior categorization, is a weakly-supervised learning problem where we do not have access to frame-by-frame facial gesture annotations but only weak-labels at the video level are available. Therefore, the goal is to learn a set of discriminative expressions and how they determine the video weak-labels. Facial behavior categorization can be posed as a Multi-Instance-Learning (MIL) problem and we propose a novel MIL method called Regularized Multi-Concept MIL to solve it. In contrast to previous approaches applied in facial behavior analysis, RMC-MIL follows a Multi-Concept assumption which allows different facial expressions (concepts) to contribute differently to the video-label. Moreover, to handle with the high-dimensional nature of facial-descriptors, RMC-MIL uses a discriminative approach to model the concepts and structured sparsity regularization to discard non-informative features. RMC-MIL is posed as a convex-constrained optimization problem where all the parameters are jointly learned using the Projected-Quasi-Newton method. In our experiments, we use two public data-sets to show the advantages of the Regularized Multi-Concept approach and its improvement compared to existing MIL methods. RMC-MIL outperforms state-of-the-art results in the UNBC data-set for pain detection. |
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Nottingham; UK; September 2014 |
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LAMP; CIC; 600.074; 600.079 |
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Admin @ si @ RWB2014 |
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2508 |
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