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Author |
E. Serradell; Adriana Romero; R. Leta; Carlo Gatta; Francesc Moreno-Noguer |
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Title |
Simultaneous Correspondence and Non-Rigid 3D Reconstruction of the Coronary Tree from Single X-Ray Images |
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Conference Article |
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Year |
2011 |
Publication  |
13th IEEE International Conference on Computer Vision |
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850-857 |
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Barcelona |
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ICCV |
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MILAB |
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no |
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Call Number |
Admin @ si @ SRL2011 |
Serial |
1803 |
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Author |
Bhaskar Chakraborty; Michael Holte; Thomas B. Moeslund; Jordi Gonzalez; Xavier Roca |
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Title |
A Selective Spatio-Temporal Interest Point Detector for Human Action Recognition in Complex Scenes |
Type |
Conference Article |
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Year |
2011 |
Publication |
13th IEEE International Conference on Computer Vision |
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Pages |
1776-1783 |
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Recent progress in the field of human action recognition points towards the use of Spatio-Temporal Interest Points (STIPs) for local descriptor-based recognition strategies. In this paper we present a new approach for STIP detection by applying surround suppression combined with local and temporal constraints. Our method is significantly different from existing STIP detectors and improves the performance by detecting more repeatable, stable and distinctive STIPs for human actors, while suppressing unwanted background STIPs. For action representation we use a bag-of-visual words (BoV) model of local N-jet features to build a vocabulary of visual-words. To this end, we introduce a novel vocabulary building strategy by combining spatial pyramid and vocabulary compression techniques, resulting in improved performance and efficiency. Action class specific Support Vector Machine (SVM) classifiers are trained for categorization of human actions. A comprehensive set of experiments on existing benchmark datasets, and more challenging datasets of complex scenes, validate our approach and show state-of-the-art performance. |
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Barcelona |
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1550-5499 |
ISBN |
978-1-4577-1101-5 |
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ICCV |
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ISE |
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no |
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Admin @ si @ CHM2011 |
Serial |
1811 |
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Author |
Mohammad Rouhani; Angel Sappa |
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Title |
Correspondence Free Registration through a Point-to-Model Distance Minimization |
Type |
Conference Article |
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Year |
2011 |
Publication |
13th IEEE International Conference on Computer Vision |
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Pages |
2150-2157 |
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This paper presents a novel formulation, which derives in a smooth minimization problem, to tackle the rigid registration between a given point set and a model set. Unlike most of the existing works, which are based on minimizing a point-wise correspondence term, we propose to describe the model set by means of an implicit representation. It allows a new definition of the registration error, which works beyond the point level representation. Moreover, it could be used in a gradient-based optimization framework. The proposed approach consists of two stages. Firstly, a novel formulation is proposed that relates the registration parameters with the distance between the model and data set. Secondly, the registration parameters are obtained by means of the Levengberg-Marquardt algorithm. Experimental results and comparisons with state of the art show the validity of the proposed framework. |
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Barcelona |
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1550-5499 |
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978-1-4577-1101-5 |
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ICCV |
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Notes |
ADAS |
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no |
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Call Number |
Admin @ si @ RoS2011b; ADAS @ adas @ |
Serial |
1832 |
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Author |
David Aldavert; Arnau Ramisa; Ramon Lopez de Mantaras; Ricardo Toledo |
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Title |
Real-time Object Segmentation using a Bag of Features Approach |
Type |
Conference Article |
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Year |
2010 |
Publication |
13th International Conference of the Catalan Association for Artificial Intelligence |
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Volume |
220 |
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Pages |
321–329 |
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Keywords |
Object Segmentation; Bag Of Features; Feature Quantization; Densely sampled descriptors |
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Abstract |
In this paper, we propose an object segmentation framework, based on the popular bag of features (BoF), which can process several images per second while achieving a good segmentation accuracy assigning an object category to every pixel of the image. We propose an efficient color descriptor to complement the information obtained by a typical gradient-based local descriptor. Results show that color proves to be a useful cue to increase the segmentation accuracy, specially in large homogeneous regions. Then, we extend the Hierarchical K-Means codebook using the recently proposed Vector of Locally Aggregated Descriptors method. Finally, we show that the BoF method can be easily parallelized since it is applied locally, thus the time necessary to process an image is further reduced. The performance of the proposed method is evaluated in the standard PASCAL 2007 Segmentation Challenge object segmentation dataset. |
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IOS Press Amsterdam, |
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In R.Alquezar, A.Moreno, J.Aguilar. |
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9781607506423 |
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CCIA |
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Notes |
ADAS |
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no |
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Call Number |
Admin @ si @ ARL2010b |
Serial |
1417 |
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Author |
Eloi Puertas; Sergio Escalera; Oriol Pujol |
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Title |
Classifying Objects at Different Sizes with Multi-Scale Stacked Sequential Learning |
Type |
Conference Article |
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Year |
2010 |
Publication |
13th International Conference of the Catalan Association for Artificial Intelligence |
Abbreviated Journal |
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Volume |
220 |
Issue |
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Pages |
193–200 |
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Abstract |
Sequential learning is that discipline of machine learning that deals with dependent data. In this paper, we use the Multi-scale Stacked Sequential Learning approach (MSSL) to solve the task of pixel-wise classification based on contextual information. The main contribution of this work is a shifting technique applied during the testing phase that makes possible, thanks to template images, to classify objects at different sizes. The results show that the proposed method robustly classifies such objects capturing their spatial relationships. |
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R. Alquezar, A. Moreno, J. Aguilar |
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978-1-60750-642-3 |
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CCIA |
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Notes |
HUPBA;MILAB |
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no |
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Call Number |
BCNPCL @ bcnpcl @ PEP2010 |
Serial |
1448 |
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Author |
Mehdi Mirza-Mohammadi; Sergio Escalera; Petia Radeva |
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Title |
Contextual-Guided Bag-of-Visual-Words Model for Multi-class Object Categorization |
Type |
Conference Article |
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Year |
2009 |
Publication |
13th International Conference on Computer Analysis of Images and Patterns |
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Volume |
5702 |
Issue |
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Pages |
748–756 |
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Abstract |
Bag-of-words model (BOW) is inspired by the text classification problem, where a document is represented by an unsorted set of contained words. Analogously, in the object categorization problem, an image is represented by an unsorted set of discrete visual words (BOVW). In these models, relations among visual words are performed after dictionary construction. However, close object regions can have far descriptions in the feature space, being grouped as different visual words. In this paper, we present a method for considering geometrical information of visual words in the dictionary construction step. Object interest regions are obtained by means of the Harris-Affine detector and then described using the SIFT descriptor. Afterward, a contextual-space and a feature-space are defined, and a merging process is used to fuse feature words based on their proximity in the contextual-space. Moreover, we use the Error Correcting Output Codes framework to learn the new dictionary in order to perform multi-class classification. Results show significant classification improvements when spatial information is taken into account in the dictionary construction step. |
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Springer Berlin Heidelberg |
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LNCS |
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ISSN |
0302-9743 |
ISBN |
978-3-642-03766-5 |
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CAIP |
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Notes |
HuPBA; MILAB |
Approved |
no |
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Call Number |
BCNPCL @ bcnpcl @ MEP2009 |
Serial |
1185 |
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Author |
Miquel Ferrer; Ernest Valveny; F. Serratosa; I. Bardaji; Horst Bunke |
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Title |
Graph-based k-means clustering: A comparison of the set versus the generalized median graph |
Type |
Conference Article |
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Year |
2009 |
Publication |
13th International Conference on Computer Analysis of Images and Patterns |
Abbreviated Journal |
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Volume |
5702 |
Issue |
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Pages |
342–350 |
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Abstract |
In this paper we propose the application of the generalized median graph in a graph-based k-means clustering algorithm. In the graph-based k-means algorithm, the centers of the clusters have been traditionally represented using the set median graph. We propose an approximate method for the generalized median graph computation that allows to use it to represent the centers of the clusters. Experiments on three databases show that using the generalized median graph as the clusters representative yields better results than the set median graph. |
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Münster, Germany |
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Springer Berlin Heidelberg |
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LNCS |
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0302-9743 |
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978-3-642-03766-5 |
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CAIP |
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Notes |
DAG |
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Call Number |
DAG @ dag @ FVS2009d |
Serial |
1219 |
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Author |
Debora Gil; Aura Hernandez-Sabate; Mireia Burnat; Steven Jansen; Jordi Martinez-Vilalta |
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Title |
Structure-Preserving Smoothing of Biomedical Images |
Type |
Conference Article |
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Year |
2009 |
Publication |
13th International Conference on Computer Analysis of Images and Patterns |
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5702 |
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427-434 |
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Keywords |
non-linear smoothing; differential geometry; anatomical structures segmentation; cardiac magnetic resonance; computerized tomography. |
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Abstract |
Smoothing of biomedical images should preserve gray-level transitions between adjacent tissues, while restoring contours consistent with anatomical structures. Anisotropic diffusion operators are based on image appearance discontinuities (either local or contextual) and might fail at weak inter-tissue transitions. Meanwhile, the output of block-wise and morphological operations is prone to present a block structure due to the shape and size of the considered pixel neighborhood. In this contribution, we use differential geometry concepts to define a diffusion operator that restricts to image consistent level-sets. In this manner, the final state is a non-uniform intensity image presenting homogeneous inter-tissue transitions along anatomical structures, while smoothing intra-structure texture. Experiments on different types of medical images (magnetic resonance, computerized tomography) illustrate its benefit on a further process (such as segmentation) of images. |
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Address |
Münster, Germany |
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Springer Berlin Heidelberg |
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0302-9743 |
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978-3-642-03766-5 |
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CAIP |
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Notes |
IAM |
Approved |
no |
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Call Number |
IAM @ iam @ GHB2009 |
Serial |
1527 |
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Author |
Nuria Cirera; Alicia Fornes; Josep Llados |
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Title |
Hidden Markov model topology optimization for handwriting recognition |
Type |
Conference Article |
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Year |
2015 |
Publication |
13th International Conference on Document Analysis and Recognition ICDAR2015 |
Abbreviated Journal |
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Pages |
626-630 |
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Abstract |
In this paper we present a method to optimize the topology of linear left-to-right hidden Markov models. These models are very popular for sequential signals modeling on tasks such as handwriting recognition. Many topology definition methods select the number of states for a character model based
on character length. This can be a drawback when characters are shorter than the minimum allowed by the model, since they can not be properly trained nor recognized. The proposed method optimizes the number of states per model by automatically including convenient skip-state transitions and therefore it avoids the aforementioned problem.We discuss and compare our method with other character length-based methods such the Fixed, Bakis and Quantile methods. Our proposal performs well on off-line handwriting recognition task. |
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Address |
Nancy; France; August 2015 |
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ICDAR |
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Notes |
DAG; 600.061; 602.006; 600.077 |
Approved |
no |
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Call Number |
Admin @ si @ CFL2015 |
Serial |
2639 |
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Author |
Pau Riba; Josep Llados; Alicia Fornes |
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Title |
Handwritten Word Spotting by Inexact Matching of Grapheme Graphs |
Type |
Conference Article |
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Year |
2015 |
Publication |
13th International Conference on Document Analysis and Recognition ICDAR2015 |
Abbreviated Journal |
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Pages |
781 - 785 |
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Abstract |
This paper presents a graph-based word spotting for handwritten documents. Contrary to most word spotting techniques, which use statistical representations, we propose a structural representation suitable to be robust to the inherent deformations of handwriting. Attributed graphs are constructed using a part-based approach. Graphemes extracted from shape convexities are used as stable units of handwriting, and are associated to graph nodes. Then, spatial relations between them determine graph edges. Spotting is defined in terms of an error-tolerant graph matching using bipartite-graph matching algorithm. To make the method usable in large datasets, a graph indexing approach that makes use of binary embeddings is used as preprocessing. Historical documents are used as experimental framework. The approach is comparable to statistical ones in terms of time and memory requirements, especially when dealing with large document collections. |
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ICDAR |
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Notes |
DAG; 600.077; 600.061; 602.006 |
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no |
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Call Number |
Admin @ si @ RLF2015b |
Serial |
2642 |
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Author |
Jean-Christophe Burie; J. Chazalon; M.Coustaty; S.Eskenazi; Muhammad Muzzamil Luqman; M.Mehri; N.Nayef; Jean-Marc Ogier; S.Prum; Marçal Rusiñol |
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Title |
ICDAR2015 Competition on Smartphone Document Capture and OCR (SmartDoc) |
Type |
Conference Article |
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Year |
2015 |
Publication |
13th International Conference on Document Analysis and Recognition ICDAR2015 |
Abbreviated Journal |
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Pages |
1161 - 1165 |
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Abstract |
Smartphones are enabling new ways of capture,
hence arises the need for seamless and reliable acquisition and
digitization of documents, in order to convert them to editable,
searchable and a more human-readable format. Current stateof-the-art
works lack databases and baseline benchmarks for
digitizing mobile captured documents. We have organized a
competition for mobile document capture and OCR in order to
address this issue. The competition is structured into two independent
challenges: smartphone document capture, and smartphone
OCR. This report describes the datasets for both challenges
along with their ground truth, details the performance evaluation
protocols which we used, and presents the final results of the
participating methods. In total, we received 13 submissions: 8
for challenge-I, and 5 for challenge-2. |
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Address |
Nancy; France; August 2015 |
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ICDAR |
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Notes |
DAG; 600.077; 601.223; 600.084 |
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no |
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Call Number |
Admin @ si @ BCC2015 |
Serial |
2681 |
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Author |
Marçal Rusiñol; David Aldavert; Ricardo Toledo; Josep Llados |
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Title |
Towards Query-by-Speech Handwritten Keyword Spotting |
Type |
Conference Article |
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Year |
2015 |
Publication |
13th International Conference on Document Analysis and Recognition ICDAR2015 |
Abbreviated Journal |
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501-505 |
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Abstract |
In this paper, we present a new querying paradigm for handwritten keyword spotting. We propose to represent handwritten word images both by visual and audio representations, enabling a query-by-speech keyword spotting system. The two representations are merged together and projected to a common sub-space in the training phase. This transform allows to, given a spoken query, retrieve word instances that were only represented by the visual modality. In addition, the same method can be used backwards at no additional cost to produce a handwritten text-tospeech system. We present our first results on this new querying mechanism using synthetic voices over the George Washington
dataset. |
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Address |
Nancy; France; August 2015 |
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ICDAR |
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DAG; 600.084; 600.061; 601.223; 600.077;ADAS |
Approved |
no |
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Call Number |
Admin @ si @ RAT2015b |
Serial |
2682 |
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Author |
Hongxing Gao; Marçal Rusiñol; Dimosthenis Karatzas; Josep Llados; R.Jain; D.Doermann |
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Title |
Novel Line Verification for Multiple Instance Focused Retrieval in Document Collections |
Type |
Conference Article |
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Year |
2015 |
Publication |
13th International Conference on Document Analysis and Recognition ICDAR2015 |
Abbreviated Journal |
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481-485 |
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Nancy; France; August 2015 |
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DAG; 600.077; 601.223; 600.084; 600.061 |
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Admin @ si @ GRK2015 |
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2683 |
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Author |
Marçal Rusiñol; J. Chazalon; Jean-Marc Ogier; Josep Llados |
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A Comparative Study of Local Detectors and Descriptors for Mobile Document Classification |
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2015 |
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13th International Conference on Document Analysis and Recognition ICDAR2015 |
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596-600 |
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In this paper we conduct a comparative study of local key-point detectors and local descriptors for the specific task of mobile document classification. A classification architecture based on direct matching of local descriptors is used as baseline for the comparative study. A set of four different key-point
detectors and four different local descriptors are tested in all the possible combinations. The experiments are conducted in a database consisting of 30 model documents acquired on 6 different backgrounds, totaling more than 36.000 test images. |
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Nancy; France; August 2015 |
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DAG; 600.084; 600.61; 601.223; 600.077 |
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Admin @ si @ RCO2015 |
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2684 |
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J. Chazalon; Marçal Rusiñol; Jean-Marc Ogier; Josep Llados |
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A Semi-Automatic Groundtruthing Tool for Mobile-Captured Document Segmentation |
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2015 |
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13th International Conference on Document Analysis and Recognition ICDAR2015 |
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621-625 |
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This paper presents a novel way to generate groundtruth data for the evaluation of mobile document capture systems, focusing on the first stage of the image processing pipeline involved: document object detection and segmentation in lowquality preview frames. We introduce and describe a simple, robust and fast technique based on color markers which enables a semi-automated annotation of page corners. We also detail a technique for marker removal. Methods and tools presented in the paper were successfully used to annotate, in few hours, 24889
frames in 150 video files for the smartDOC competition at ICDAR 2015 |
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Nancy; France; August 2015 |
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DAG; 600.084; 600.061; 601.223; 600.077 |
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Admin @ si @ CRO2015b |
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2685 |
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