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Lluis Gomez; Anguelos Nicolaou; Dimosthenis Karatzas |
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Improving patch‐based scene text script identification with ensembles of conjoined networks |
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Journal Article |
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2017 |
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Pattern Recognition |
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67 |
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85-96 |
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DAG; 600.084; 600.121; 600.129 |
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Admin @ si @ GNK2017 |
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2887 |
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Marc Sunset Perez; Marc Comino Trinidad; Dimosthenis Karatzas; Antonio Chica Calaf; Pere Pau Vazquez Alcocer |
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Title |
Development of general‐purpose projection‐based augmented reality systems |
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2016 |
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IADIs international journal on computer science and information systems |
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IADIs |
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11 |
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2 |
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1-18 |
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Despite the large amount of methods and applications of augmented reality, there is little homogenizatio n on the software platforms that support them. An exception may be the low level control software that is provided by some high profile vendors such as Qualcomm and Metaio. However, these provide fine grain modules for e.g. element tracking. We are more co ncerned on the application framework, that includes the control of the devices working together for the development of the AR experience. In this paper we describe the development of a software framework for AR setups. We concentrate on the modular design of the framework, but also on some hard problems such as the calibration stage, crucial for projection – based AR. The developed framework is suitable and has been tested in AR applications using camera – projector pairs, for both fixed and nomadic setups |
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DAG; 600.084 |
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Admin @ si @ SCK2016 |
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2890 |
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Alicia Fornes; Josep Llados; Oriol Ramos Terrades; Marçal Rusiñol |
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Title |
La Visió per Computador com a Eina per a la Interpretació Automàtica de Fonts Documentals |
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2016 |
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Lligall, Revista Catalana d'Arxivística |
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39 |
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20-46 |
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DAG; 600.097 |
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Admin @ si @ FLR2016 |
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2897 |
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Anjan Dutta; Josep Llados; Horst Bunke; Umapada Pal |
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Product graph-based higher order contextual similarities for inexact subgraph matching |
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2018 |
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Pattern Recognition |
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76 |
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596-611 |
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Many algorithms formulate graph matching as an optimization of an objective function of pairwise quantification of nodes and edges of two graphs to be matched. Pairwise measurements usually consider local attributes but disregard contextual information involved in graph structures. We address this issue by proposing contextual similarities between pairs of nodes. This is done by considering the tensor product graph (TPG) of two graphs to be matched, where each node is an ordered pair of nodes of the operand graphs. Contextual similarities between a pair of nodes are computed by accumulating weighted walks (normalized pairwise similarities) terminating at the corresponding paired node in TPG. Once the contextual similarities are obtained, we formulate subgraph matching as a node and edge selection problem in TPG. We use contextual similarities to construct an objective function and optimize it with a linear programming approach. Since random walk formulation through TPG takes into account higher order information, it is not a surprise that we obtain more reliable similarities and better discrimination among the nodes and edges. Experimental results shown on synthetic as well as real benchmarks illustrate that higher order contextual similarities increase discriminating power and allow one to find approximate solutions to the subgraph matching problem. |
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DAG; 602.167; 600.097; 600.121 |
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Admin @ si @ DLB2018 |
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3083 |
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Author |
Thanh Ha Do; Salvatore Tabbone; Oriol Ramos Terrades |
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Sparse representation over learned dictionary for symbol recognition |
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2016 |
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Signal Processing |
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SP |
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125 |
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36-47 |
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Symbol Recognition; Sparse Representation; Learned Dictionary; Shape Context; Interest Points |
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In this paper we propose an original sparse vector model for symbol retrieval task. More specically, we apply the K-SVD algorithm for learning a visual dictionary based on symbol descriptors locally computed around interest points. Results on benchmark datasets show that the obtained sparse representation is competitive related to state-of-the-art methods. Moreover, our sparse representation is invariant to rotation and scale transforms and also robust to degraded images and distorted symbols. Thereby, the learned visual dictionary is able to represent instances of unseen classes of symbols. |
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DAG; 600.061; 600.077 |
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Admin @ si @ DTR2016 |
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2946 |
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