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Lluis Pere de las Heras; David Fernandez; Alicia Fornes; Ernest Valveny; Gemma Sanchez; Josep Llados |
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Title |
Runlength Histogram Image Signature for Perceptual Retrieval of Architectural Floor Plans |
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Conference Article |
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2013 |
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10th IAPR International Workshop on Graphics Recognition |
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Bethlehem; PA; USA; August 2013 |
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GREC |
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DAG; 600.045; 600.061; 600.056 |
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Admin @ si @ HFF2013b |
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2695 |
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Author |
Lluis Pere de las Heras; Ernest Valveny; Gemma Sanchez |
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Title |
Unsupervised and Notation-Independent Wall Segmentation in Floor Plans Using a Combination of Statistical and Structural Strategies |
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2013 |
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10th IAPR International Workshop on Graphics Recognition |
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Bethlehem; PA; USA; August 2013 |
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GREC |
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DAG |
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Admin @ si @ HVS2013b |
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2696 |
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Juan Ignacio Toledo; Jordi Cucurull; Jordi Puiggali; Alicia Fornes; Josep Llados |
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Title |
Document Analysis Techniques for Automatic Electoral Document Processing: A Survey |
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2015 |
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E-Voting and Identity, Proceedings of 5th international conference, VoteID 2015 |
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139-141 |
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Document image analysis; Computer vision; Paper ballots; Paper based elections; Optical scan; Tally |
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In this paper, we will discuss the most common challenges in electoral document processing and study the different solutions from the document analysis community that can be applied in each case. We will cover Optical Mark Recognition techniques to detect voter selections in the Australian Ballot, handwritten number recognition for preferential elections and handwriting recognition for write-in areas. We will also propose some particular adjustments that can be made to those general techniques in the specific context of electoral documents. |
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Bern; Switzerland; September 2015 |
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VoteID |
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DAG; 600.061; 602.006; 600.077 |
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no |
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Admin @ si @ TCP2015 |
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2641 |
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Fernando Vilariño; Dimosthenis Karatzas |
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Title |
A Living Lab approach for Citizen Science in Libraries |
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2016 |
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1st International ECSA Conference |
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Berlin; Germany; May 2016 |
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ECSA |
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MV; DAG; 600.084; 600.097;SIAI |
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no |
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Admin @ si @ViK2016 |
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2804 |
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Author |
Arash Akbarinia; C. Alejandro Parraga; Marta Exposito; Bogdan Raducanu; Xavier Otazu |
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Can biological solutions help computers detect symmetry? |
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2017 |
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40th European Conference on Visual Perception |
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Berlin; Germany; August 2017 |
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ECVP |
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NEUROBIT |
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no |
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Admin @ si @ APE2017 |
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2995 |
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Author |
Ozan Caglayan; Walid Aransa; Yaxing Wang; Marc Masana; Mercedes Garcıa-Martinez; Fethi Bougares; Loic Barrault; Joost Van de Weijer |
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Title |
Does Multimodality Help Human and Machine for Translation and Image Captioning? |
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Conference Article |
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2016 |
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1st conference on machine translation |
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This paper presents the systems developed by LIUM and CVC for the WMT16 Multimodal Machine Translation challenge. We explored various comparative methods, namely phrase-based systems and attentional recurrent neural networks models trained using monomodal or multimodal data. We also performed a human evaluation in order to estimate theusefulness of multimodal data for human machine translation and image description generation. Our systems obtained the best results for both tasks according to the automatic evaluation metrics BLEU and METEOR. |
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Berlin; Germany; August 2016 |
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WMT |
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LAMP; 600.106 ; 600.068 |
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no |
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Admin @ si @ CAW2016 |
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2761 |
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Author |
Marta Ligero; Guillermo Torres; Carles Sanchez; Katerine Diaz; Raquel Perez; Debora Gil |
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Title |
Selection of Radiomics Features based on their Reproducibility |
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Conference Article |
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2019 |
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41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society |
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403-408 |
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Dimensionality reduction is key to alleviate machine learning artifacts in clinical applications with Small Sample Size (SSS) unbalanced datasets. Existing methods rely on either the probabilistic distribution of training data or the discriminant power of the reduced space, disregarding the impact of repeatability and uncertainty in features.In the present study is proposed the use of reproducibility of radiomics features to select features with high inter-class correlation coefficient (ICC). The reproducibility includes the variability introduced in the image acquisition, like medical scans acquisition parameters and convolution kernels, that affects intensity-based features and tumor annotations made by physicians, that influences morphological descriptors of the lesion.For the reproducibility of radiomics features three studies were conducted on cases collected at Vall Hebron Oncology Institute (VHIO) on responders to oncology treatment. The studies focused on the variability due to the convolution kernel, image acquisition parameters, and the inter-observer lesion identification. The features selected were those features with a ICC higher than 0.7 in the three studies.The selected features based on reproducibility were evaluated for lesion malignancy classification using a different database. Results show better performance compared to several state-of-the-art methods including Principal Component Analysis (PCA), Kernel Discriminant Analysis via QR decomposition (KDAQR), LASSO, and an own built Convolutional Neural Network. |
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Berlin; Alemanya; July 2019 |
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EMBC |
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IAM; 600.139; 600.145 |
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Admin @ si @ LTS2019 |
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3358 |
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Author |
Mohammad Ali Bagheri; Qigang Gao; Sergio Escalera |
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Title |
Three-Dimensional Design of Error Correcting Output Codes |
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2012 |
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8th International Conference on Machine Learning and Data Mining |
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29- |
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Berlin, Germany |
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HuPBA;MILAB |
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Admin @ si @ BGE2012a |
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2041 |
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Author |
Mikhail Mozerov |
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Title |
An Effective Stereo Matching Algorithm with Optimal Path Cost Aggregation |
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2006 |
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28th Annual Symposium of the German Association for Pattern Recognition, LNCS 4174: 617–626 |
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Berlin (Germany) |
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ISE |
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ISE @ ise @ Moz2006 |
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677 |
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Mikhail Mozerov; Ignasi Rius; Xavier Roca; Jordi Gonzalez |
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Title |
3D Human Motion Sequences Synchronization Using Dense Matching Algorithm |
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2006 |
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28th Annual Symposium of the German Association for Pattern Recognition, LNCS 4174: 485–494, ISBN 978–3–540–44412–1 |
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ISE @ ise @ MRR2006 |
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678 |
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Author |
Dani Rowe; I. Reid; Jordi Gonzalez; Juan J. Villanueva |
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Title |
Unconstrained Multiple-People Tracking |
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2006 |
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28th Annual Symposium of the German Association for Pattern Recognition, LNCS 4174: 505–514, ISBN 978–3–540–44412–1 |
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Berlin (Germany) |
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ISE @ ise @ RRG2006 |
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769 |
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Author |
Joan Serrat; Jordi Vitria; J. Pladellorens |
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Title |
Morphological Segmentation of Heart Scintigraphic image Sequences. |
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1991 |
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Computer Assisted Radiology. |
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ADAS;OR;MV |
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ADAS @ adas @ SVP1991 |
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263 |
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Josep Llados; Gemma Sanchez; K. Tombre |
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An Error-Correction Graph Grammar to Recognize Texture Symbols. |
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2002 |
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Graphics Recognition: Algorithms and Apllications, LNCS 2390: 128–138, Springer Verlag. |
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DAG @ dag @ LST2002 |
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281 |
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S. Garcia; Dani Rowe; Jordi Gonzalez; Juan J. Villanueva |
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Articulated Object Modelling Using Neural Gas Networks |
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2005 |
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5th IASTED International Conference on Visualization, Imaging and Image Processing (VIIP’2005) |
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Benidorm (Spain) |
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ISE @ ise @ GRG2005 |
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David Vazquez; Antonio Lopez |
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Title |
Intrusion Classification in Intelligent Video Surveillance Systems |
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2008 |
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Estudis d'Enginyeria Superior en Informática |
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UAB |
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Human detection; Car detection; Intrusion detection |
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An intelligent video surveillance system (IVS) is a camera-based installation able to process in real-time the images coming from the cameras. The aim is to automatically warn about different events of interest at the moment they happen. Daview system of Davantis is a com mercial example of IVS system. The problems addressed by any IVS system, and so Daview, are so challenging that none IVS system is perfect, thus, they need continuous improvement. Accordingly, this project aims to study different approaches in order to outperform current Daview performance, in particular, we bet for improving its classification core. We present an in deep study of the state of the art on IVS systems, as well as on how Daview works. Based on that knowledge, we propose four possibilities for improving Daview classification capabilities: improve existent classifiers; improve existing classifiers combination; create new classifiers and create new classifier-based architectures. Our main contribution has been the incorporation of state-of-the-art feature selection and machine learning techniques for the classification tasks, a viewpoint not fully addressed in current Daview system. After a comprehensive quantitative evaluation we will see how one of our proposals clearly outperforms the overall performance of current Daview system. In particular the classification core that we finally propose consists in an AdaBoost One-Against-All architecture that uses appearance and motion features that were already present in current Daview system |
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Bellaterra, Spain |
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PFC |
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ADAS |
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no |
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ADAS @ adas @ VL2008a |
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1670 |
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