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Author |
Sergio Escalera; Oriol Pujol; Petia Radeva |
![goto web page (via DOI) doi](img/doi.gif)
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Title |
Separability of Ternary Codes for Sparse Designs of Error-Correcting Output Codes |
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Journal Article |
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Year |
2009 |
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Pattern Recognition Letters |
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PRL |
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30 |
Issue |
3 |
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285–297 |
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Error Correcting Output Codes (ECOC) represent a successful framework to deal with multi-class categorization problems based on combining binary classifiers. In this paper, we present a new formulation of the ternary ECOC distance and the error-correcting capabilities in the ternary ECOC framework. Based on the new measure, we stress on how to design coding matrices preventing codification ambiguity and propose a new Sparse Random coding matrix with ternary distance maximization. The results on the UCI Repository and in a real speed traffic categorization problem show that when the coding design satisfies the new ternary measures, significant performance improvement is obtained independently of the decoding strategy applied. |
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MILAB;HuPBA |
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no |
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BCNPCL @ bcnpcl @ EPR2009a |
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1153 |
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Author |
Shida Beigpour; Marc Serra; Joost Van de Weijer; Robert Benavente; Maria Vanrell; Olivier Penacchio; Dimitris Samaras |
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Title |
Intrinsic Image Evaluation On Synthetic Complex Scenes |
Type |
Conference Article |
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Year |
2013 |
Publication |
20th IEEE International Conference on Image Processing |
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285 - 289 |
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Scene decomposition into its illuminant, shading, and reflectance intrinsic images is an essential step for scene understanding. Collecting intrinsic image groundtruth data is a laborious task. The assumptions on which the ground-truth
procedures are based limit their application to simple scenes with a single object taken in the absence of indirect lighting and interreflections. We investigate synthetic data for intrinsic image research since the extraction of ground truth is straightforward, and it allows for scenes in more realistic situations (e.g, multiple illuminants and interreflections). With this dataset we aim to motivate researchers to further explore intrinsic image decomposition in complex scenes. |
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Melbourne; Australia; September 2013 |
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ICIP |
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CIC; 600.048; 600.052; 600.051 |
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no |
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Admin @ si @ BSW2013 |
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2264 |
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Author |
Hassan Ahmed Sial; S. Sancho; Ramon Baldrich; Robert Benavente; Maria Vanrell |
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Title |
Color-based data augmentation for Reflectance Estimation |
Type |
Conference Article |
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Year |
2018 |
Publication |
26th Color Imaging Conference |
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284-289 |
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Deep convolutional architectures have shown to be successful frameworks to solve generic computer vision problems. The estimation of intrinsic reflectance from single image is not a solved problem yet. Encoder-Decoder architectures are a perfect approach for pixel-wise reflectance estimation, although it usually suffers from the lack of large datasets. Lack of data can be partially solved with data augmentation, however usual techniques focus on geometric changes which does not help for reflectance estimation. In this paper we propose a color-based data augmentation technique that extends the training data by increasing the variability of chromaticity. Rotation on the red-green blue-yellow plane of an opponent space enable to increase the training set in a coherent and sound way that improves network generalization capability for reflectance estimation. We perform some experiments on the Sintel dataset showing that our color-based augmentation increase performance and overcomes one of the state-of-the-art methods. |
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Vancouver; November 2018 |
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CIC |
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no |
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Admin @ si @ SSB2018a |
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3129 |
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Author |
Sergio Escalera |
![goto web page (via DOI) doi](img/doi.gif)
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Title |
Human Behavior Analysis From Depth Maps |
Type |
Conference Article |
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Year |
2012 |
Publication |
7th Conference on Articulated Motion and Deformable Objects |
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Volume |
7378 |
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282-292 |
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Pose Recovery (PR) and Human Behavior Analysis (HBA) have been a main focus of interest from the beginnings of Computer Vision and Machine Learning. PR and HBA were originally addressed by the analysis of still images and image sequences. More recent strategies consisted of Motion Capture technology (MOCAP), based on the synchronization of multiple cameras in controlled environments; and the analysis of depth maps from Time-of-Flight (ToF) technology, based on range image recording from distance sensor measurements. Recently, with the appearance of the multi-modal RGBD information provided by the low cost Kinect \textsfTM sensor (from RGB and Depth, respectively), classical methods for PR and HBA have been redefined, and new strategies have been proposed. In this paper, the recent contributions and future trends of multi-modal RGBD data analysis for PR and HBA are reviewed and discussed. |
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Mallorca |
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Springer Heidelberg |
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F.J. Perales; R.B. Fisher; T.B. Moeslund |
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0302-9743 |
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978-3-642-31566-4 |
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AMDO |
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Notes |
MILAB; HuPBA |
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no |
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Call Number |
Admin @ si @ Esc2012 |
Serial |
2040 |
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Author |
Sergio Escalera; Oriol Pujol; Petia Radeva |
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Title |
Traffic Sign Classification using Error Correcting Techniques |
Type |
Conference Article |
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Year |
2007 |
Publication |
2nd International Conference on Computer Vision Theory and Applications |
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281–285 |
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Barcelona (Spain) |
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VISAPP |
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MILAB;HuPBA |
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no |
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BCNPCL @ bcnpcl @ EPR2007a |
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909 |
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Author |
Xavier Baro; Jordi Vitria |
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Title |
Weighted Dissociated Diploes: An Extended Visual Feature Set |
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Book Chapter |
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Year |
2008 |
Publication |
Computer Vision Systems. 6th International Conference ICVS |
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5008 |
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281–290 |
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Santorini (Greece) |
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OR;HuPBA;MV |
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no |
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BCNPCL @ bcnpcl @ BaV2008b |
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977 |
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Author |
Oriol Vicente; Alicia Fornes; Ramon Valdes |
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Title |
La Xarxa d Humanitats Digitals de la UABCie: una estructura inteligente para la investigación y la transferencia en Humanidades |
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Conference Article |
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2017 |
Publication |
3rd Congreso Internacional de Humanidades Digitales Hispánicas. Sociedad Internacional |
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281-383 |
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978-84-697-5692-8 |
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HDH |
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DAG; 600.121 |
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no |
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Admin @ si @ VFV2017 |
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3060 |
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Author |
Marco Pedersoli; Jordi Gonzalez; Andrew Bagdanov; Juan J. Villanueva |
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Title |
Recursive Coarse-to-Fine Localization for fast Object Recognition |
Type |
Conference Article |
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Year |
2010 |
Publication |
11th European Conference on Computer Vision |
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6313 |
Issue |
II |
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280–293 |
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Cascading techniques are commonly used to speed-up the scan of an image for object detection. However, cascades of detectors are slow to train due to the high number of detectors and corresponding thresholds to learn. Furthermore, they do not use any prior knowledge about the scene structure to decide where to focus the search. To handle these problems, we propose a new way to scan an image, where we couple a recursive coarse-to-fine refinement together with spatial constraints of the object location. For doing that we split an image into a set of uniformly distributed neighborhood regions, and for each of these we apply a local greedy search over feature resolutions. The neighborhood is defined as a scanning region that only one object can occupy. Therefore the best hypothesis is obtained as the location with maximum score and no thresholds are needed. We present an implementation of our method using a pyramid of HOG features and we evaluate it on two standard databases, VOC2007 and INRIA dataset. Results show that the Recursive Coarse-to-Fine Localization (RCFL) achieves a 12x speed-up compared to standard sliding windows. Compared with a cascade of multiple resolutions approach our method has slightly better performance in speed and Average-Precision. Furthermore, in contrast to cascading approach, the speed-up is independent of image conditions, the number of detected objects and clutter. |
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Crete (Greece) |
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Springer Berlin Heidelberg |
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LNCS |
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0302-9743 |
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978-3-642-15566-6 |
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ECCV |
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ISE |
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no |
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DAG @ dag @ PGB2010 |
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1438 |
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Permanent link to this record |
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Author |
Carles Fernandez; Pau Baiget; Xavier Roca; Jordi Gonzalez |
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Title |
Natural Language Descriptions of Human Behavior from Video Sequences |
Type |
Conference Article |
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Year |
2007 |
Publication |
Advances in Artificial Intelligence, 30th Annual Conference on Artificial Intelligence |
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4667 |
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279–292 |
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KI |
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ISE |
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no |
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ISE @ ise @ FBR2007b |
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921 |
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Author |
Carme Julia; Angel Sappa; Felipe Lumbreras; Joan Serrat; Antonio Lopez |
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Title |
Rank Estimation in 3D Multibody Motion Segmentation |
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Journal Article |
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2008 |
Publication |
Electronic Letters |
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44 |
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4 |
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279-280 |
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A novel technique for rank estimation in 3D multibody motion segmentation is proposed. It is based on the study of the frequency spectra of moving rigid objects and does not use or assume a prior knowledge of the objects contained in the scene (i.e. number of objects and motion). The significance of rank estimation on multibody motion segmentation results is shown by using two motion segmentation algorithms over both synthetic and real data. |
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ADAS |
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no |
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ADAS @ adas @ JSL2008a |
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939 |
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Author |
Ivan Huerta; Ariel Amato; Jordi Gonzalez; Juan J. Villanueva |
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Title |
Fusing Edge Cues to Handle Colour Problems in Image Segmentation |
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Book Chapter |
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2008 |
Publication |
Articulated Motion and Deformable Objects, 5th International Conference |
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5098 |
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279–288 |
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Port d'Andratx (Mallorca) |
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AMDO |
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ISE |
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no |
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ISE @ ise @ HAG2008 |
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973 |
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Author |
Raul Gomez; Lluis Gomez; Jaume Gibert; Dimosthenis Karatzas |
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Title |
Self-Supervised Learning from Web Data for Multimodal Retrieval |
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Book Chapter |
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2019 |
Publication |
Multi-Modal Scene Understanding Book |
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279-306 |
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self-supervised learning; webly supervised learning; text embeddings; multimodal retrieval; multimodal embedding |
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Self-Supervised learning from multimodal image and text data allows deep neural networks to learn powerful features with no need of human annotated data. Web and Social Media platforms provide a virtually unlimited amount of this multimodal data. In this work we propose to exploit this free available data to learn a multimodal image and text embedding, aiming to leverage the semantic knowledge learnt in the text domain and transfer it to a visual model for semantic image retrieval. We demonstrate that the proposed pipeline can learn from images with associated text without supervision and analyze the semantic structure of the learnt joint image and text embeddingspace. Weperformathoroughanalysisandperformancecomparisonoffivedifferentstateof the art text embeddings in three different benchmarks. We show that the embeddings learnt with Web and Social Media data have competitive performances over supervised methods in the text basedimageretrievaltask,andweclearlyoutperformstateoftheartintheMIRFlickrdatasetwhen training in the target data. Further, we demonstrate how semantic multimodal image retrieval can be performed using the learnt embeddings, going beyond classical instance-level retrieval problems. Finally, we present a new dataset, InstaCities1M, composed by Instagram images and their associated texts that can be used for fair comparison of image-text embeddings. |
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DAG; 600.129; 601.338; 601.310 |
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no |
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Admin @ si @ GGG2019 |
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3266 |
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Author |
Ernest Valveny; Salvatore Tabbone; Oriol Ramos Terrades |
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Title |
Performance Characterization of Shape Descriptors for Symbol Representation |
Type |
Book Chapter |
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Year |
2008 |
Publication |
Graphics Recognition: Recent Advances and New Opportunities |
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5046 |
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278–287 |
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W. Liu, J. Llados, J.M. Ogier |
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DAG |
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DAG @ dag @ VTR2008 |
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985 |
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Author |
Marc Serra; Olivier Penacchio; Robert Benavente; Maria Vanrell |
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Title |
Names and Shades of Color for Intrinsic Image Estimation |
Type |
Conference Article |
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2012 |
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25th IEEE Conference on Computer Vision and Pattern Recognition |
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278-285 |
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Abstract |
In the last years, intrinsic image decomposition has gained attention. Most of the state-of-the-art methods are based on the assumption that reflectance changes come along with strong image edges. Recently, user intervention in the recovery problem has proved to be a remarkable source of improvement. In this paper, we propose a novel approach that aims to overcome the shortcomings of pure edge-based methods by introducing strong surface descriptors, such as the color-name descriptor which introduces high-level considerations resembling top-down intervention. We also use a second surface descriptor, termed color-shade, which allows us to include physical considerations derived from the image formation model capturing gradual color surface variations. Both color cues are combined by means of a Markov Random Field. The method is quantitatively tested on the MIT ground truth dataset using different error metrics, achieving state-of-the-art performance. |
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Address |
Providence, Rhode Island |
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Publisher |
IEEE Xplore |
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Edition |
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ISSN |
1063-6919 |
ISBN |
978-1-4673-1226-4 |
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CVPR |
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no |
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Call Number |
Admin @ si @ SPB2012 |
Serial |
2026 |
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Author |
Stepan Simsa; Michal Uricar; Milan Sulc; Yash Patel; Ahmed Hamdi; Matej Kocian; Matyas Skalicky; Jiri Matas; Antoine Doucet; Mickael Coustaty; Dimosthenis Karatzas |
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Title |
Overview of DocILE 2023: Document Information Localization and Extraction |
Type |
Conference Article |
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Year |
2023 |
Publication |
International Conference of the Cross-Language Evaluation Forum for European Languages |
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Volume |
14163 |
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Pages ![sorted by First Page field, descending order (down)](img/sort_desc.gif) |
276–293 |
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Keywords |
Information Extraction; Computer Vision; Natural Language Processing; Optical Character Recognition; Document Understanding |
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Abstract |
This paper provides an overview of the DocILE 2023 Competition, its tasks, participant submissions, the competition results and possible future research directions. This first edition of the competition focused on two Information Extraction tasks, Key Information Localization and Extraction (KILE) and Line Item Recognition (LIR). Both of these tasks require detection of pre-defined categories of information in business documents. The second task additionally requires correctly grouping the information into tuples, capturing the structure laid out in the document. The competition used the recently published DocILE dataset and benchmark that stays open to new submissions. The diversity of the participant solutions indicates the potential of the dataset as the submissions included pure Computer Vision, pure Natural Language Processing, as well as multi-modal solutions and utilized all of the parts of the dataset, including the annotated, synthetic and unlabeled subsets. |
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Thessaloniki; Greece; September 2023 |
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CLEF |
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Call Number |
Admin @ si @ SUS2023a |
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3924 |
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