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Author |
Razieh Rastgoo; Kourosh Kiani; Sergio Escalera |
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Title |
Sign Language Recognition: A Deep Survey |
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Journal Article |
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Year |
2021 |
Publication |
Expert Systems With Applications |
Abbreviated Journal |
ESWA |
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164 |
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Pages |
113794 |
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Abstract |
Sign language, as a different form of the communication language, is important to large groups of people in society. There are different signs in each sign language with variability in hand shape, motion profile, and position of the hand, face, and body parts contributing to each sign. So, visual sign language recognition is a complex research area in computer vision. Many models have been proposed by different researchers with significant improvement by deep learning approaches in recent years. In this survey, we review the vision-based proposed models of sign language recognition using deep learning approaches from the last five years. While the overall trend of the proposed models indicates a significant improvement in recognition accuracy in sign language recognition, there are some challenges yet that need to be solved. We present a taxonomy to categorize the proposed models for isolated and continuous sign language recognition, discussing applications, datasets, hybrid models, complexity, and future lines of research in the field. |
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HUPBA; no proj |
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no |
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Admin @ si @ RKE2021a |
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3521 |
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Author |
David Rotger; Petia Radeva; E Fernandez-Nofrerias; J. Mauri |
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Title |
Blood Detection In IVUS Longitudinal Cuts Using AdaBoost With a Novel Feature Stability Criterion |
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Conference Article |
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2007 |
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Artificial Intelligence Research and Development. Proceedings of the 10th International Conference of the ACIA |
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163 |
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197–204 |
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978-1-58603-798-7 |
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CCIA’07 |
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MILAB |
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no |
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BCNPCL @ bcnpcl @ RRF2007a |
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831 |
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Author |
Alex Goldhoorn; Arnau Ramisa; Ramon Lopez de Mantaras; Ricardo Toledo |
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Title |
Using the Average Landmark Vector Method for Robot Homing |
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Conference Article |
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2007 |
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Artificial Intelligence Research and Development, Proceedings of the 10th International Conference of the ACIA |
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163 |
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331–338 |
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978–1–58603–798–7 |
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CCIA’07 |
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RV;ADAS |
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Admin @ si @ GRL2007 |
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899 |
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Author |
Jon Almazan |
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Title |
Deforming the Blurred Shape Model for Shape Description and Recognition |
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2010 |
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CVC Technical Report |
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163 |
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no |
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Call Number |
Admin @ si @ Alm2010 |
Serial |
1354 |
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Author |
Monica Piñol |
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Title |
Adaptative Vocabulary Tree for Image Classification using Reinforcement Learning |
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Report |
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2010 |
Publication |
CVC Technical Report |
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162 |
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Bellaterra (Barcelona) |
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Computer Vision Center |
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Master's thesis |
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ADAS |
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no |
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Admin @ si @ Piñ2010 |
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1936 |
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Author |
David Fernandez |
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Title |
Handwritten Word Spotting in Old Manuscript Images using Shape Descriptors |
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Report |
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2010 |
Publication |
CVC Technical Report |
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161 |
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DAG |
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no |
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Admin @ si @ Fer2010b |
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1353 |
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Author |
Ekain Artola |
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Title |
Human Attention Map Prediction Combining Visual Features |
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Report |
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2010 |
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CVC Technical Report |
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160 |
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Bachelor's thesis |
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Admin @ si @ Art2010 |
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1352 |
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Author |
Mohamed Ali Souibgui; Alicia Fornes; Yousri Kessentini; Beata Megyesi |
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Title |
Few shots are all you need: A progressive learning approach for low resource handwritten text recognition |
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Journal Article |
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Year |
2022 |
Publication |
Pattern Recognition Letters |
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PRL |
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160 |
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43-49 |
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Abstract |
Handwritten text recognition in low resource scenarios, such as manuscripts with rare alphabets, is a challenging problem. In this paper, we propose a few-shot learning-based handwriting recognition approach that significantly reduces the human annotation process, by requiring only a few images of each alphabet symbols. The method consists of detecting all the symbols of a given alphabet in a textline image and decoding the obtained similarity scores to the final sequence of transcribed symbols. Our model is first pretrained on synthetic line images generated from an alphabet, which could differ from the alphabet of the target domain. A second training step is then applied to reduce the gap between the source and the target data. Since this retraining would require annotation of thousands of handwritten symbols together with their bounding boxes, we propose to avoid such human effort through an unsupervised progressive learning approach that automatically assigns pseudo-labels to the unlabeled data. The evaluation on different datasets shows that our model can lead to competitive results with a significant reduction in human effort. The code will be publicly available in the following repository: https://github.com/dali92002/HTRbyMatching |
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Elsevier |
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DAG; 600.121; 600.162; 602.230 |
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Admin @ si @ SFK2022 |
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3736 |
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Author |
Anjan Dutta |
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Title |
Symbol Spotting in Graphical Documents by Serialized Subgraph Matching |
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Report |
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2010 |
Publication |
CVC Technical Report |
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159 |
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Master's thesis |
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DAG |
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no |
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Admin @ si @ Dut2010 |
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1351 |
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Author |
Lluis Pere de las Heras |
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Title |
Syntactic Model for Semantic Document Analysis |
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Report |
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2010 |
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CVC Technical Report |
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158 |
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Admin @ si @ Per2010 |
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1350 |
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Author |
Mohammad Ali Bagheri; Qigang Gao; Sergio Escalera; Huamin Ren; Thomas B. Moeslund; Elham Etemad |
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Title |
Locality Regularized Group Sparse Coding for Action Recognition |
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Journal Article |
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2017 |
Publication |
Computer Vision and Image Understanding |
Abbreviated Journal |
CVIU |
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158 |
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106-114 |
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Bag of words; Feature encoding; Locality constrained coding; Group sparse coding; Alternating direction method of multipliers; Action recognition |
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Bag of visual words (BoVW) models are widely utilized in image/ video representation and recognition. The cornerstone of these models is the encoding stage, in which local features are decomposed over a codebook in order to obtain a representation of features. In this paper, we propose a new encoding algorithm by jointly encoding the set of local descriptors of each sample and considering the locality structure of descriptors. The proposed method takes advantages of locality coding such as its stability and robustness to noise in descriptors, as well as the strengths of the group coding strategy by taking into account the potential relation among descriptors of a sample. To efficiently implement our proposed method, we consider the Alternating Direction Method of Multipliers (ADMM) framework, which results in quadratic complexity in the problem size. The method is employed for a challenging classification problem: action recognition by depth cameras. Experimental results demonstrate the outperformance of our methodology compared to the state-of-the-art on the considered datasets. |
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HuPBA; no proj |
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Admin @ si @ BGE2017 |
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3014 |
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Author |
Patricia Marquez |
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Title |
Conditions Ensuring Accuracy of Local Optical Flow Schemes |
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Report |
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2010 |
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CVC Tehcnical Report |
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157 |
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Accurate computation of optical flow is a key-point in many image processing fields. Detection of anomalous and unpredicted agents (such as pedestrians, bikers or cars) in urban scenes or pathology discrimination in medical imaging sequences, to mention just a two. The above kinds sequences present two main difficulties for standard optical flow techniques. On one hand, variability in acquisition conditions (illuminance, medical imaging modality, ...) force an alterantive representation for images fulfilling the britghtness constancy constrain. On the hand, current variational schemes produce oversmoothed fields unable to properly model discontinuous behaviours such as collisions or functionless pathological areas. This master project explores the abilities and limitations of local and global optical flow approaches. The master student will put especial emphasis in the theoretical grounds behind in order to design a variational framework combining the theoretical advantages of the considered techniques. In particular an optical flow based on Gabor phase tracking (developed in the group for medical imaging) will be generalized to urban scenes. |
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Bellaterra 08193, Barcelona, Spain |
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IAM; |
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IAM @ iam @ Mar2010 |
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1582 |
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Author |
Zhanwu Xiong |
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A Pompd Model for Active Camera Control |
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2010 |
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CVC Technical Report |
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156 |
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Admin @ si @ Xio2010 |
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1356 |
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Debora Gil; Jose Maria-Carazo; Roberto Marabini |
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On the nature of 2D crystal unbending |
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2006 |
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Journal of Structural Biology |
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156 |
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3 |
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546-555 |
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Electron microscopy |
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Crystal unbending, the process that aims to recover a perfect crystal from experimental data, is one of the more important steps in electron crystallography image processing. The unbending process involves three steps: estimation of the unit cell displacements from their ideal positions, extension of the deformation field to the whole image and transformation of the image in order to recover an ideal crystal. In this work, we present a systematic analysis of the second step oriented to address two issues. First, whether the unit cells remain undistorted and only the distance between them should be changed (rigid case) or should be modified with the same deformation suffered by the whole crystal (elastic case). Second, the performance of different extension algorithms (interpolation versus approximation) is explored. Our experiments show that there is no difference between elastic and rigid cases or among the extension algorithms. This implies that the deformation fields are constant over large areas. Furthermore, our results indicate that the main source of error is the transformation of the crystal image. |
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1047-8477 |
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IAM; |
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IAM @ iam @ GCM2006 |
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1519 |
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Nataliya Shapovalova |
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On Importance of Interaction and Context |
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2010 |
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CVC Technical Report |
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155 |
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Admin @ si @ Sha2010 |
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1355 |
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