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Author |
Javad Zolfaghari Bengar; Bogdan Raducanu; Joost Van de Weijer |
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Title |
When Deep Learners Change Their Mind: Learning Dynamics for Active Learning |
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Conference Article |
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Year |
2021 |
Publication |
19th International Conference on Computer Analysis of Images and Patterns |
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13052 |
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1 |
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403-413 |
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Active learning aims to select samples to be annotated that yield the largest performance improvement for the learning algorithm. Many methods approach this problem by measuring the informativeness of samples and do this based on the certainty of the network predictions for samples. However, it is well-known that neural networks are overly confident about their prediction and are therefore an untrustworthy source to assess sample informativeness. In this paper, we propose a new informativeness-based active learning method. Our measure is derived from the learning dynamics of a neural network. More precisely we track the label assignment of the unlabeled data pool during the training of the algorithm. We capture the learning dynamics with a metric called label-dispersion, which is low when the network consistently assigns the same label to the sample during the training of the network and high when the assigned label changes frequently. We show that label-dispersion is a promising predictor of the uncertainty of the network, and show on two benchmark datasets that an active learning algorithm based on label-dispersion obtains excellent results. |
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September 2021 |
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CAIP |
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LAMP; |
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no |
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Admin @ si @ ZRV2021 |
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3673 |
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Author |
Gemma Rotger; Francesc Moreno-Noguer; Felipe Lumbreras; Antonio Agudo |
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Title |
Single view facial hair 3D reconstruction |
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Conference Article |
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Year |
2019 |
Publication |
9th Iberian Conference on Pattern Recognition and Image Analysis |
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Volume |
11867 |
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423-436 |
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Keywords |
3D Vision; Shape Reconstruction; Facial Hair Modeling |
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Abstract |
n this work, we introduce a novel energy-based framework that addresses the challenging problem of 3D reconstruction of facial hair from a single RGB image. To this end, we identify hair pixels over the image via texture analysis and then determine individual hair fibers that are modeled by means of a parametric hair model based on 3D helixes. We propose to minimize an energy composed of several terms, in order to adapt the hair parameters that better fit the image detections. The final hairs respond to the resulting fibers after a post-processing step where we encourage further realism. The resulting approach generates realistic facial hair fibers from solely an RGB image without assuming any training data nor user interaction. We provide an experimental evaluation on real-world pictures where several facial hair styles and image conditions are observed, showing consistent results and establishing a comparison with respect to competing approaches. |
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Madrid; July 2019 |
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IbPRIA |
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MSIAU; 600.086; 600.130; 600.122 |
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Admin @ si @ |
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3707 |
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Author |
Muhammad Anwer Rao; Fahad Shahbaz Khan; Joost Van de Weijer; Jorma Laaksonen |
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Title |
Top-Down Deep Appearance Attention for Action Recognition |
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Conference Article |
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Year |
2017 |
Publication |
20th Scandinavian Conference on Image Analysis |
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10269 |
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297-309 |
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Action recognition; CNNs; Feature fusion |
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Recognizing human actions in videos is a challenging problem in computer vision. Recently, convolutional neural network based deep features have shown promising results for action recognition. In this paper, we investigate the problem of fusing deep appearance and motion cues for action recognition. We propose a video representation which combines deep appearance and motion based local convolutional features within the bag-of-deep-features framework. Firstly, dense deep appearance and motion based local convolutional features are extracted from spatial (RGB) and temporal (flow) networks, respectively. Both visual cues are processed in parallel by constructing separate visual vocabularies for appearance and motion. A category-specific appearance map is then learned to modulate the weights of the deep motion features. The proposed representation is discriminative and binds the deep local convolutional features to their spatial locations. Experiments are performed on two challenging datasets: JHMDB dataset with 21 action classes and ACT dataset with 43 categories. The results clearly demonstrate that our approach outperforms both standard approaches of early and late feature fusion. Further, our approach is only employing action labels and without exploiting body part information, but achieves competitive performance compared to the state-of-the-art deep features based approaches. |
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Tromso; June 2017 |
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SCIA |
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LAMP; 600.109; 600.068; 600.120 |
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Admin @ si @ RKW2017b |
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3039 |
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Author |
Veronica Romero; Alicia Fornes; Enrique Vidal; Joan Andreu Sanchez |
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Title |
Information Extraction in Handwritten Marriage Licenses Books Using the MGGI Methodology |
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Conference Article |
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Year |
2017 |
Publication |
8th Iberian Conference on Pattern Recognition and Image Analysis |
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Volume |
10255 |
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287-294 |
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Keywords |
Handwritten Text Recognition; Information extraction; Language modeling; MGGI; Categories-based language model |
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Abstract |
Historical records of daily activities provide intriguing insights into the life of our ancestors, useful for demographic and genealogical research. For example, marriage license books have been used for centuries by ecclesiastical and secular institutions to register marriages. These books follow a simple structure of the text in the records with a evolutionary vocabulary, mainly composed of proper names that change along the time. This distinct vocabulary makes automatic transcription and semantic information extraction difficult tasks. In previous works we studied the use of category-based language models and how a Grammatical Inference technique known as MGGI could improve the accuracy of these tasks. In this work we analyze the main causes of the semantic errors observed in previous results and apply a better implementation of the MGGI technique to solve these problems. Using the resulting language model, transcription and information extraction experiments have been carried out, and the results support our proposed approach. |
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Faro; Portugal; June 2017 |
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L.A. Alexandre; J.Salvador Sanchez; Joao M. F. Rodriguez |
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978-3-319-58837-7 |
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IbPRIA |
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DAG; 602.006; 600.097; 600.121 |
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no |
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Call Number |
Admin @ si @ RFV2017 |
Serial |
2952 |
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Author |
Juan Ramon Terven Salinas; Joaquin Salas; Bogdan Raducanu |
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Title |
Robust Head Gestures Recognition for Assistive Technology |
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Book Chapter |
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Year |
2014 |
Publication |
Pattern Recognition |
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Volume |
8495 |
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152-161 |
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This paper presents a system capable of recognizing six head gestures: nodding, shaking, turning right, turning left, looking up, and looking down. The main difference of our system compared to other methods is that the Hidden Markov Models presented in this paper, are fully connected and consider all possible states in any given order, providing the following advantages to the system: (1) allows unconstrained movement of the head and (2) it can be easily integrated into a wearable device (e.g. glasses, neck-hung devices), in which case it can robustly recognize gestures in the presence of ego-motion. Experimental results show that this approach outperforms common methods that use restricted HMMs for each gesture. |
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Springer International Publishing |
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0302-9743 |
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978-3-319-07490-0 |
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LAMP; |
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Admin @ si @ TSR2014b |
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2505 |
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Author |
Javier Varona; Juan J. Villanueva |
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Title |
NeuroFilters: Neural Networks for image Processing. |
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Conference Article |
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Year |
1997 |
Publication |
Proceedings Volume 3101, New Image Processing Techniques and Applications: Algorithms, Methods, and Components II |
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3101 |
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Munich |
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ISE @ ise @ VaV1997a |
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207 |
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Author |
Agnes Borras; Francesc Tous; Josep Llados; Maria Vanrell |
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Title |
High-Level Clothes Description Based on Colour-Texture and Structural Features |
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Conference Article |
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2003 |
Publication |
1rst. Iberian Conference on Pattern Recognition and Image Analysis IbPRIA 2003 |
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2652 |
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108-116 |
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Abstract |
ecture Notes in Computer Science 2652 108–116 |
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Palma de Mallorca |
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IbPRIA |
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DAG;CIC |
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no |
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CAT @ cat @ BTL2003b |
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369 |
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Author |
David Guillamet; B. Moghaddam; Jordi Vitria |
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Title |
Modeling High-Order Dependencies in Local Appearance Models |
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Conference Article |
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2003 |
Publication |
1rst. Iberian Conference on Pattern Recognition and Image Analysis IbPRIA 2003 |
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2652 |
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308-316 |
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Springer-Verlag |
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OR;MV |
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BCNPCL @ bcnpcl @ GMV2003a |
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376 |
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David Guillamet; Jordi Vitria |
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Title |
An Experimental Evaluation of K-nn for Linear Transforms of Positive Data |
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Conference Article |
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2003 |
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1rst. Iberian Conference on Pattern Recognition and Image Analysis IbPRIA 2003 |
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2652 |
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317-325 |
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Springer-Verlag |
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OR;MV |
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BCNPCL @ bcnpcl @ GuV2003a |
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379 |
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Author |
David Lloret; Joan Serrat; Antonio Lopez; Juan J. Villanueva |
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Title |
Ultrasound to MR Volume Registration for Brain Sinking Measurement |
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Conference Article |
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2003 |
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1rst. Iberian Conference on Pattern Recognition and Image Analysis IbPRIA 2003 |
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2652 |
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420-427 |
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Springer-Verlag |
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ADAS |
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no |
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ADAS @ adas @ LSL2003a |
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384 |
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Author |
David Masip; Jordi Vitria |
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Title |
An Experimental Comparision of Dimensionality Reduction for Face Verification Methods |
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Conference Article |
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2003 |
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1rst. Iberian Conference on Pattern Recognition and Image Analysis IbPRIA 2003 |
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2652 |
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530-537 |
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Springer-Verlag |
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OR;MV |
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BCNPCL @ bcnpcl @ MaV2003a |
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386 |
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Daniel Ponsa; Xavier Roca |
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Title |
Multiple Model Approach to Deformable Shape Tracking |
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Conference Article |
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2003 |
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1rst. Iberian Conference on Pattern Recognition and Image Analysis IbPRIA 2003 |
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2652 |
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782-792 |
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Springer-Verlag |
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ADAS;ISE |
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ADAS @ adas @ PoR2003 |
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397 |
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Author |
Anna Salvatella; Maria Vanrell; Ramon Baldrich |
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Title |
Subtexture Components for Texture Description |
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Conference Article |
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2003 |
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1rst. Iberian Conference on Pattern Recognition and Image Analysis IbPRIA 2003 |
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2652 |
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884-892 |
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Springer-Verlag |
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CAT @ cat @ SVR2003 |
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421 |
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Author |
A.F. Sole; S. Ngan; G. Sapiro; X. Hu; Antonio Lopez |
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Anisotropic 2-D and 3-D Averaging of fMRI Signals |
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2001 |
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IEEE Transactions on Medical Imaging |
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2020 |
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2 |
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86-93 |
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ADAS |
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ADAS @ adas @ SNS2001 |
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165 |
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Author |
Josep Llados; Gemma Sanchez; Enric Marti |
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A String-Based Method to Recognize Symbols and Structural Textures in Architectural Plans. |
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1997 |
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Graphics Recognition Algorithms and Systems. GREC 1997. |
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1389 |
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91-103 |
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Series Volume |
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Series Issue |
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Edition |
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ISSN |
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ISBN |
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Medium |
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Area |
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Expedition |
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Conference |
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Notes |
DAG |
Approved |
no |
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Call Number |
DAG @ dag @ LSM1997 |
Serial |
44 |
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