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Author |
Marçal Rusiñol; David Aldavert; Ricardo Toledo; Josep Llados |
![goto web page url](img/www.gif)
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Title |
Browsing Heterogeneous Document Collections by a Segmentation-Free Word Spotting Method |
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Conference Article |
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Year |
2011 |
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11th International Conference on Document Analysis and Recognition |
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63-67 |
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Abstract ![sorted by Abstract field, ascending order (up)](img/sort_asc.gif) |
In this paper, we present a segmentation-free word spotting method that is able to deal with heterogeneous document image collections. We propose a patch-based framework where patches are represented by a bag-of-visual-words model powered by SIFT descriptors. A later refinement of the feature vectors is performed by applying the latent semantic indexing technique. The proposed method performs well on both handwritten and typewritten historical document images. We have also tested our method on documents written in non-Latin scripts. |
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Beijing, China |
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DAG;ADAS |
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no |
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Admin @ si @ RAT2011 |
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1788 |
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Author |
Thanh Ha Do; Oriol Ramos Terrades; Salvatore Tabbone |
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Title |
DSD: document sparse-based denoising algorithm |
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Journal Article |
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Year |
2019 |
Publication |
Pattern Analysis and Applications |
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PAA |
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22 |
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1 |
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177–186 |
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Document denoising; Sparse representations; Sparse dictionary learning; Document degradation models |
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Abstract ![sorted by Abstract field, ascending order (up)](img/sort_asc.gif) |
In this paper, we present a sparse-based denoising algorithm for scanned documents. This method can be applied to any kind of scanned documents with satisfactory results. Unlike other approaches, the proposed approach encodes noise documents through sparse representation and visual dictionary learning techniques without any prior noise model. Moreover, we propose a precision parameter estimator. Experiments on several datasets demonstrate the robustness of the proposed approach compared to the state-of-the-art methods on document denoising. |
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DAG; 600.097; 600.140; 600.121 |
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Admin @ si @ DRT2019 |
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3254 |
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Author |
David Aldavert; Marçal Rusiñol; Ricardo Toledo; Josep Llados |
![download PDF file pdf](img/file_PDF.gif)
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Title |
Integrating Visual and Textual Cues for Query-by-String Word Spotting |
Type |
Conference Article |
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Year |
2013 |
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12th International Conference on Document Analysis and Recognition |
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511 - 515 |
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Abstract ![sorted by Abstract field, ascending order (up)](img/sort_asc.gif) |
In this paper, we present a word spotting framework that follows the query-by-string paradigm where word images are represented both by textual and visual representations. The textual representation is formulated in terms of character $n$-grams while the visual one is based on the bag-of-visual-words scheme. These two representations are merged together and projected to a sub-vector space. This transform allows to, given a textual query, retrieve word instances that were only represented by the visual modality. Moreover, this statistical representation can be used together with state-of-the-art indexation structures in order to deal with large-scale scenarios. The proposed method is evaluated using a collection of historical documents outperforming state-of-the-art performances. |
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Washington; USA; August 2013 |
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1520-5363 |
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ICDAR |
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DAG; ADAS; 600.045; 600.055; 600.061 |
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no |
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Admin @ si @ ART2013 |
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2224 |
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Author |
Partha Pratim Roy; Umapada Pal; Josep Llados |
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Title |
Query Driven Word Retrieval in Graphical Documents |
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Conference Article |
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Year |
2010 |
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9th IAPR International Workshop on Document Analysis Systems |
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191–198 |
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Abstract ![sorted by Abstract field, ascending order (up)](img/sort_asc.gif) |
In this paper, we present an approach towards the retrieval of words from graphical document images. In graphical documents, due to presence of multi-oriented characters in non-structured layout, word indexing is a challenging task. The proposed approach uses recognition results of individual components to form character pairs with the neighboring components. An indexing scheme is designed to store the spatial description of components and to access them efficiently. Given a query text word (ascii/unicode format), the character pairs present in it are searched in the document. Next the retrieved character pairs are linked sequentially to form character string. Dynamic programming is applied to find different instances of query words. A string edit distance is used here to match the query word as the objective function. Recognition of multi-scale and multi-oriented character component is done using Support Vector Machine classifier. To consider multi-oriented character strings the features used in the SVM are invariant to character orientation. Experimental results show that the method is efficient to locate a query word from multi-oriented text in graphical documents. |
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Boston; USA |
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978-1-60558-773-8 |
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DAG |
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no |
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DAG @ dag @ RPL2010b |
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1433 |
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Author |
Marçal Rusiñol; Josep Llados |
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Title |
Efficient Logo Retrieval Through Hashing Shape Context Descriptors |
Type |
Conference Article |
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Year |
2010 |
Publication |
9th IAPR International Workshop on Document Analysis Systems |
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215–222 |
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Abstract ![sorted by Abstract field, ascending order (up)](img/sort_asc.gif) |
In this paper, we present an approach towards the retrieval of words from graphical document images. In graphical documents, due to presence of multi-oriented characters in non-structured layout, word indexing is a challenging task. The proposed approach uses recognition results of individual components to form character pairs with the neighboring components. An indexing scheme is designed to store the spatial description of components and to access them efficiently. Given a query text word (ascii/unicode format), the character pairs present in it are searched in the document. Next the retrieved character pairs are linked sequentially to form character string. Dynamic programming is applied to find different instances of query words. A string edit distance is used here to match the query word as the objective function. Recognition of multi-scale and multi-oriented character component is done using Support Vector Machine classifier. To consider multi-oriented character strings the features used in the SVM are invariant to character orientation. Experimental results show that the method is efficient to locate a query word from multi-oriented text in graphical documents. |
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Boston; USA |
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DAG |
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no |
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DAG @ dag @ RuL2010b |
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1434 |
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Author |
V. Poulain d'Andecy; Emmanuel Hartmann; Marçal Rusiñol |
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Title |
Field Extraction by hybrid incremental and a-priori structural templates |
Type |
Conference Article |
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Year |
2018 |
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13th IAPR International Workshop on Document Analysis Systems |
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251 - 256 |
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Layout Analysis; information extraction; incremental learning |
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Abstract ![sorted by Abstract field, ascending order (up)](img/sort_asc.gif) |
In this paper, we present an incremental framework for extracting information fields from administrative documents. First, we demonstrate some limits of the existing state-of-the-art methods such as the delay of the system efficiency. This is a concern in industrial context when we have only few samples of each document class. Based on this analysis, we propose a hybrid system combining incremental learning by means of itf-df statistics and a-priori generic
models. We report in the experimental section our results obtained with a dataset of real invoices. |
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Viena; Austria; April 2018 |
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DAS |
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DAG; 600.084; 600.129; 600.121 |
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no |
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Admin @ si @ PHR2018 |
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3106 |
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Author |
Miguel Reyes; Jordi Vitria; Petia Radeva; Sergio Escalera |
![download PDF file pdf](img/file_PDF.gif)
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Title |
Real-time Activity Monitoring of Inpatients |
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Conference Article |
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2010 |
Publication |
Medical Image Computing in Catalunya: Graduate Student Workshop |
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35–36 |
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In this paper, we present the development of an application capable of monitoring a set of patient vital signs in real time. The application has been designed to support the medical staff of a hospital. Preliminary results show the suitability
of the system to prevent the injury produced by the agitation of the patients. |
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Girona |
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MICCAT |
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OR;MILAB;HUPBA;MV |
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no |
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BCNPCL @ bcnpcl @ RVR2010 |
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1477 |
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Author |
Jun Wan; Yibing Zhao; Shuai Zhou; Isabelle Guyon; Sergio Escalera |
![download PDF file pdf](img/file_PDF.gif)
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Title |
ChaLearn Looking at People RGB-D Isolated and Continuous Datasets for Gesture Recognition |
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Conference Article |
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2016 |
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29th IEEE Conference on Computer Vision and Pattern Recognition Worshops |
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In this paper, we present two large video multi-modal datasets for RGB and RGB-D gesture recognition: the ChaLearn LAP RGB-D Isolated Gesture Dataset (IsoGD)and the Continuous Gesture Dataset (ConGD). Both datasets are derived from the ChaLearn Gesture Dataset
(CGD) that has a total of more than 50000 gestures for the “one-shot-learning” competition. To increase the potential of the old dataset, we designed new well curated datasets composed of 249 gesture labels, and including 47933 gestures manually labeled the begin and end frames in sequences.Using these datasets we will open two competitions
on the CodaLab platform so that researchers can test and compare their methods for “user independent” gesture recognition. The first challenge is designed for gesture spotting
and recognition in continuous sequences of gestures while the second one is designed for gesture classification from segmented data. The baseline method based on the bag of visual words model is also presented. |
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Las Vegas; USA; July 2016 |
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CVPRW |
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HuPBA;MILAB; |
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Admin @ si @ WZZ2016 |
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2771 |
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Author |
Sergio Escalera; Alicia Fornes; Oriol Pujol; Josep Llados; Petia Radeva |
![goto web page (via DOI) doi](img/doi.gif)
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Title |
Circular Blurred Shape Model for Multiclass Symbol Recognition |
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Journal Article |
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2011 |
Publication |
IEEE Transactions on Systems, Man and Cybernetics (Part B) (IEEE) |
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TSMCB |
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41 |
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2 |
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497-506 |
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Abstract ![sorted by Abstract field, ascending order (up)](img/sort_asc.gif) |
In this paper, we propose a circular blurred shape model descriptor to deal with the problem of symbol detection and classification as a particular case of object recognition. The feature extraction is performed by capturing the spatial arrangement of significant object characteristics in a correlogram structure. The shape information from objects is shared among correlogram regions, where a prior blurring degree defines the level of distortion allowed in the symbol, making the descriptor tolerant to irregular deformations. Moreover, the descriptor is rotation invariant by definition. We validate the effectiveness of the proposed descriptor in both the multiclass symbol recognition and symbol detection domains. In order to perform the symbol detection, the descriptors are learned using a cascade of classifiers. In the case of multiclass categorization, the new feature space is learned using a set of binary classifiers which are embedded in an error-correcting output code design. The results over four symbol data sets show the significant improvements of the proposed descriptor compared to the state-of-the-art descriptors. In particular, the results are even more significant in those cases where the symbols suffer from elastic deformations. |
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1083-4419 |
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MILAB; DAG;HuPBA |
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Admin @ si @ EFP2011 |
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1784 |
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Author |
Saiping Zhang; Luis Herranz; Marta Mrak; Marc Gorriz Blanch; Shuai Wan; Fuzheng Yang |
![download PDF file pdf](img/file_PDF.gif)
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Title |
DCNGAN: A Deformable Convolution-Based GAN with QP Adaptation for Perceptual Quality Enhancement of Compressed Video |
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Conference Article |
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2022 |
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47th International Conference on Acoustics, Speech, and Signal Processing |
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In this paper, we propose a deformable convolution-based generative adversarial network (DCNGAN) for perceptual quality enhancement of compressed videos. DCNGAN is also adaptive to the quantization parameters (QPs). Compared with optical flows, deformable convolutions are more effective and efficient to align frames. Deformable convolutions can operate on multiple frames, thus leveraging more temporal information, which is beneficial for enhancing the perceptual quality of compressed videos. Instead of aligning frames in a pairwise manner, the deformable convolution can process multiple frames simultaneously, which leads to lower computational complexity. Experimental results demonstrate that the proposed DCNGAN outperforms other state-of-the-art compressed video quality enhancement algorithms. |
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Virtual; May 2022 |
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ICASSP |
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MACO; 600.161; 601.379 |
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no |
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Admin @ si @ ZHM2022a |
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3765 |
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Author |
Guillem Martinez; Maya Aghaei; Martin Dijkstra; Bhalaji Nagarajan; Femke Jaarsma; Jaap van de Loosdrecht; Petia Radeva; Klaas Dijkstra |
![download PDF file pdf](img/file_PDF.gif)
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Title |
Hyper-Spectral Imaging for Overlapping Plastic Flakes Segmentation |
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Conference Article |
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2022 |
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47th International Conference on Acoustics, Speech, and Signal Processing |
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Hyper-spectral imaging; plastic sorting; multi-label segmentation; bitfield encoding |
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Abstract ![sorted by Abstract field, ascending order (up)](img/sort_asc.gif) |
In this paper, we propose a deformable convolution-based generative adversarial network (DCNGAN) for perceptual quality enhancement of compressed videos. DCNGAN is also adaptive to the quantization parameters (QPs). Compared with optical flows, deformable convolutions are more effective and efficient to align frames. Deformable convolutions can operate on multiple frames, thus leveraging more temporal information, which is beneficial for enhancing the perceptual quality of compressed videos. Instead of aligning frames in a pairwise manner, the deformable convolution can process multiple frames simultaneously, which leads to lower computational complexity. Experimental results demonstrate that the proposed DCNGAN outperforms other state-of-the-art compressed video quality enhancement algorithms. |
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Singapore; May 2022 |
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ICASSP |
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MILAB; no proj |
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no |
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Admin @ si @ MAD2022 |
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3767 |
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Author |
Thanh Ha Do; Salvatore Tabbone; Oriol Ramos Terrades |
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Title |
New Approach for Symbol Recognition Combining Shape Context of Interest Points with Sparse Representation |
Type |
Conference Article |
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2013 |
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12th International Conference on Document Analysis and Recognition |
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265-269 |
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Abstract ![sorted by Abstract field, ascending order (up)](img/sort_asc.gif) |
In this paper, we propose a new approach for symbol description. Our method is built based on the combination of shape context of interest points descriptor and sparse representation. More specifically, we first learn a dictionary describing shape context of interest point descriptors. Then, based on information retrieval techniques, we build a vector model for each symbol based on its sparse representation in a visual vocabulary whose visual words are columns in the learneddictionary. The retrieval task is performed by ranking symbols based on similarity between vector models. Evaluation of our method, using benchmark datasets, demonstrates the validity of our approach and shows that it outperforms related state-of-theart methods. |
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Washington; USA; August 2013 |
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ICDAR |
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DAG |
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no |
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Admin @ si @ DTR2013b |
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2331 |
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Author |
Thanh Ha Do; Salvatore Tabbone; Oriol Ramos Terrades |
![download PDF file pdf](img/file_PDF.gif)
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Title |
Text/graphic separation using a sparse representation with multi-learned dictionaries |
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Conference Article |
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2012 |
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21st International Conference on Pattern Recognition |
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Graphics Recognition; Layout Analysis; Document Understandin |
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In this paper, we propose a new approach to extract text regions from graphical documents. In our method, we first empirically construct two sequences of learned dictionaries for the text and graphical parts respectively. Then, we compute the sparse representations of all different sizes and non-overlapped document patches in these learned dictionaries. Based on these representations, each patch can be classified into the text or graphic category by comparing its reconstruction errors. Same-sized patches in one category are then merged together to define the corresponding text or graphic layers which are combined to createfinal text/graphic layer. Finally, in a post-processing step, text regions are further filtered out by using some learned thresholds. |
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Tsukuba |
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ICPR |
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DAG |
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no |
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Admin @ si @ DTR2012a |
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2135 |
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Author |
Alejandro Cartas; Petia Radeva; Mariella Dimiccoli |
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Title |
Modeling long-term interactions to enhance action recognition |
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Conference Article |
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2021 |
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25th International Conference on Pattern Recognition |
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10351-10358 |
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In this paper, we propose a new approach to under-stand actions in egocentric videos that exploits the semantics of object interactions at both frame and temporal levels. At the frame level, we use a region-based approach that takes as input a primary region roughly corresponding to the user hands and a set of secondary regions potentially corresponding to the interacting objects and calculates the action score through a CNN formulation. This information is then fed to a Hierarchical LongShort-Term Memory Network (HLSTM) that captures temporal dependencies between actions within and across shots. Ablation studies thoroughly validate the proposed approach, showing in particular that both levels of the HLSTM architecture contribute to performance improvement. Furthermore, quantitative comparisons show that the proposed approach outperforms the state-of-the-art in terms of action recognition on standard benchmarks,without relying on motion information |
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January 2021 |
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MILAB; |
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no |
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Admin @ si @ CRD2021 |
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3626 |
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Author |
Youssef El Rhabi; Simon Loic; Brun Luc; Josep Llados; Felipe Lumbreras |
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Title |
Information Theoretic Rotationwise Robust Binary Descriptor Learning |
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2016 |
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Joint IAPR International Workshops on Statistical Techniques in Pattern Recognition (SPR) and Structural and Syntactic Pattern Recognition (SSPR) |
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368-378 |
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In this paper, we propose a new data-driven approach for binary descriptor selection. In order to draw a clear analysis of common designs, we present a general information-theoretic selection paradigm. It encompasses several standard binary descriptor construction schemes, including a recent state-of-the-art one named BOLD. We pursue the same endeavor to increase the stability of the produced descriptors with respect to rotations. To achieve this goal, we have designed a novel offline selection criterion which is better adapted to the online matching procedure. The effectiveness of our approach is demonstrated on two standard datasets, where our descriptor is compared to BOLD and to several classical descriptors. In particular, it emerges that our approach can reproduce equivalent if not better performance as BOLD while relying on twice shorter descriptors. Such an improvement can be influential for real-time applications. |
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Mérida; Mexico; November 2016 |
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S+SSPR |
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DAG; ADAS; 600.097; 600.086 |
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no |
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Admin @ si @ RLL2016 |
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2871 |
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