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Author |
Oualid M. Benkarim; Petia Radeva; Laura Igual |
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Title ![sorted by Title field, ascending order (up)](img/sort_asc.gif) |
Label Consistent Multiclass Discriminative Dictionary Learning for MRI Segmentation |
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Conference Article |
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2014 |
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8th Conference on Articulated Motion and Deformable Objects |
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8563 |
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138-147 |
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MRI segmentation; sparse representation; discriminative dic- tionary learning; multiclass classication |
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The automatic segmentation of multiple subcortical structures in brain Magnetic Resonance Images (MRI) still remains a challenging task. In this paper, we address this problem using sparse representation and discriminative dictionary learning, which have shown promising results in compression, image denoising and recently in MRI segmentation. Particularly, we use multiclass dictionaries learned from a set of brain atlases to simultaneously segment multiple subcortical structures.
We also impose dictionary atoms to be specialized in one given class using label consistent K-SVD, which can alleviate the bias produced by unbalanced libraries, present when dealing with small structures. The proposed method is compared with other state of the art approaches for the segmentation of the Basal Ganglia of 35 subjects of a public dataset.
The promising results of the segmentation method show the eciency of the multiclass discriminative dictionary learning algorithms in MRI segmentation problems. |
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Palma de Mallorca; July 2014 |
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Springer International Publishing |
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0302-9743 |
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978-3-319-08848-8 |
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AMDO |
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MILAB; OR |
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Admin @ si @ BRI2014 |
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2494 |
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Author |
Naila Murray; Eduard Vazquez |
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Title ![sorted by Title field, ascending order (up)](img/sort_asc.gif) |
Lacuna Restoration: How to choose a neutral colour? |
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Conference Article |
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2010 |
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Proceedings of The CREATE 2010 Conference |
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248–252 |
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Painting restoration which involves filling in material loss (called lacuna) is a complex process. Several standard techniques exist to tackle lacuna restoration,
and this article focuses on those techniques that employ a “neutral” colour to mask the defect. Restoration experts often disagree on the choice of such a colour and in fact, the concept of a neutral colour is controversial. We posit that a neutral colour is one that attracts relatively little visual attention for a specific lacuna. We conducted an eye tracking experiment to compare two common neutral
colour selection methods, specifically the most common local colour and the mean local colour. Results obtained demonstrate that the most common local colour triggers less visual attention in general. Notwithstanding, we have observed instances in which the most common colour triggers a significant amount of attention when subjects spent time resolving their confusion about whether or not a lacuna was part of the painting. |
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Gjovik, Norway |
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CREATE |
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CIC |
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Admin @ si @ MuV2010 |
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1297 |
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Author |
Arturo Fuentes; F. Javier Sanchez; Thomas Voncina; Jorge Bernal |
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Title ![sorted by Title field, ascending order (up)](img/sort_asc.gif) |
LAMV: Learning to Predict Where Spectators Look in Live Music Performances |
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Conference Article |
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2021 |
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16th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications |
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5 |
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500-507 |
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The advent of artificial intelligence has supposed an evolution on how different daily work tasks are performed. The analysis of cultural content has seen a huge boost by the development of computer-assisted methods that allows easy and transparent data access. In our case, we deal with the automation of the production of live shows, like music concerts, aiming to develop a system that can indicate the producer which camera to show based on what each of them is showing. In this context, we consider that is essential to understand where spectators look and what they are interested in so the computational method can learn from this information. The work that we present here shows the results of a first preliminary study in which we compare areas of interest defined by human beings and those indicated by an automatic system. Our system is based on the extraction of motion textures from dynamic Spatio-Temporal Volumes (STV) and then analyzing the patterns by means of texture analysis techniques. We validate our approach over several video sequences that have been labeled by 16 different experts. Our method is able to match those relevant areas identified by the experts, achieving recall scores higher than 80% when a distance of 80 pixels between method and ground truth is considered. Current performance shows promise when detecting abnormal peaks and movement trends. |
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Virtual; February 2021 |
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VISIGRAPP |
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MV; ISE; 600.119; |
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Admin @ si @ FSV2021 |
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3570 |
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Author |
David Lloret; C. Mariño; Joan Serrat; Antonio Lopez; Juan J. Villanueva |
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Title ![sorted by Title field, ascending order (up)](img/sort_asc.gif) |
Landmark-based registration of full SLO video sequences. |
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Miscellaneous |
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2001 |
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Proceedings of the IX Spanish Symposium on Pattern Recognition and Image Analysis,1:189–194. |
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ADAS |
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ADAS @ adas @ LMS2001 |
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116 |
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Author |
Naveen Onkarappa; Angel Sappa |
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Title ![sorted by Title field, ascending order (up)](img/sort_asc.gif) |
Laplacian Derivative based Regularization for Optical Flow Estimation in Driving Scenario |
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Conference Article |
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Year |
2013 |
Publication |
15th International Conference on Computer Analysis of Images and Patterns |
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Volume |
8048 |
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Pages |
483-490 |
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Keywords |
Optical flow; regularization; Driver Assistance Systems; Performance Evaluation |
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Abstract |
Existing state of the art optical flow approaches, which are evaluated on standard datasets such as Middlebury, not necessarily have a similar performance when evaluated on driving scenarios. This drop on performance is due to several challenges arising on real scenarios during driving. Towards this direction, in this paper, we propose a modification to the regularization term in a variational optical flow formulation, that notably improves the results, specially in driving scenarios. The proposed modification consists on using the Laplacian derivatives of flow components in the regularization term instead of gradients of flow components. We show the improvements in results on a standard real image sequences dataset (KITTI). |
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York; UK; August 2013 |
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Springer Berlin Heidelberg |
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0302-9743 |
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978-3-642-40245-6 |
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CAIP |
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ADAS; 600.055; 601.215 |
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Admin @ si @ OnS2013b |
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2244 |
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Author |
Antoni Gurgui; Debora Gil; Enric Marti |
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Title ![sorted by Title field, ascending order (up)](img/sort_asc.gif) |
Laplacian Unitary Domain for Texture Morphing |
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Conference Article |
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Year |
2015 |
Publication |
Proceedings of the 10th International Conference on Computer Vision Theory and Applications VISIGRAPP2015 |
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1 |
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693-699 |
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Keywords |
Facial; metamorphosis;LaplacianMorphing |
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Deformation of expressive textures is the gateway to realistic computer synthesis of expressions. By their good mathematical properties and flexible formulation on irregular meshes, most texture mappings rely on solutions to the Laplacian in the cartesian space. In the context of facial expression morphing, this approximation can be seen from the opposite point of view by neglecting the metric. In this paper, we use the properties of the Laplacian in manifolds to present a novel approach to warping expressive facial images in order to generate a morphing between them. |
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Munich; Germany; February 2015 |
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SciTePress |
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978-989-758-089-5 |
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VISAPP |
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IAM; 600.075 |
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no |
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Admin @ si @ GGM2015 |
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2614 |
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Author |
Bhaskar Chakraborty; Jordi Gonzalez; Xavier Roca |
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Title ![sorted by Title field, ascending order (up)](img/sort_asc.gif) |
Large scale continuous visual event recognition using max-margin Hough transformation framework |
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Journal Article |
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2013 |
Publication |
Computer Vision and Image Understanding |
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CVIU |
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117 |
Issue |
10 |
Pages |
1356–1368 |
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In this paper we propose a novel method for continuous visual event recognition (CVER) on a large scale video dataset using max-margin Hough transformation framework. Due to high scalability, diverse real environmental state and wide scene variability direct application of action recognition/detection methods such as spatio-temporal interest point (STIP)-local feature based technique, on the whole dataset is practically infeasible. To address this problem, we apply a motion region extraction technique which is based on motion segmentation and region clustering to identify possible candidate “event of interest” as a preprocessing step. On these candidate regions a STIP detector is applied and local motion features are computed. For activity representation we use generalized Hough transform framework where each feature point casts a weighted vote for possible activity class centre. A max-margin frame work is applied to learn the feature codebook weight. For activity detection, peaks in the Hough voting space are taken into account and initial event hypothesis is generated using the spatio-temporal information of the participating STIPs. For event recognition a verification Support Vector Machine is used. An extensive evaluation on benchmark large scale video surveillance dataset (VIRAT) and as well on a small scale benchmark dataset (MSR) shows that the proposed method is applicable on a wide range of continuous visual event recognition applications having extremely challenging conditions. |
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1077-3142 |
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ISE |
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Admin @ si @ CGR2013 |
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2413 |
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Author |
Albert Gordo; Florent Perronnin; Ernest Valveny |
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Title ![sorted by Title field, ascending order (up)](img/sort_asc.gif) |
Large-scale document image retrieval and classification with runlength histograms and binary embeddings |
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Journal Article |
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2013 |
Publication |
Pattern Recognition |
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PR |
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46 |
Issue |
7 |
Pages |
1898-1905 |
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visual document descriptor; compression; large-scale; retrieval; classification |
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We present a new document image descriptor based on multi-scale runlength
histograms. This descriptor does not rely on layout analysis and can be
computed efficiently. We show how this descriptor can achieve state-of-theart
results on two very different public datasets in classification and retrieval
tasks. Moreover, we show how we can compress and binarize these descriptors
to make them suitable for large-scale applications. We can achieve state-ofthe-
art results in classification using binary descriptors of as few as 16 to 64
bits. |
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Elsevier |
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0031-3203 |
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DAG; 600.042; 600.045; 605.203 |
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Admin @ si @ GPV2013 |
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2306 |
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Author |
Pau Riba; Josep Llados; Alicia Fornes; Anjan Dutta |
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Title ![sorted by Title field, ascending order (up)](img/sort_asc.gif) |
Large-scale Graph Indexing using Binary Embeddings of Node Contexts |
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Conference Article |
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2015 |
Publication |
10th IAPR-TC15 Workshop on Graph-based Representations in Pattern Recognition |
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9069 |
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208-217 |
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Graph matching; Graph indexing; Application in document analysis; Word spotting; Binary embedding |
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Graph-based representations are experiencing a growing usage in visual recognition and retrieval due to their representational power in front of classical appearance-based representations in terms of feature vectors. Retrieving a query graph from a large dataset of graphs has the drawback of the high computational complexity required to compare the query and the target graphs. The most important property for a large-scale retrieval is the search time complexity to be sub-linear in the number of database examples. In this paper we propose a fast indexation formalism for graph retrieval. A binary embedding is defined as hashing keys for graph nodes. Given a database of labeled graphs, graph nodes are complemented with vectors of attributes representing their local context. Hence, each attribute counts the length of a walk of order k originated in a vertex with label l. Each attribute vector is converted to a binary code applying a binary-valued hash function. Therefore, graph retrieval is formulated in terms of finding target graphs in the database whose nodes have a small Hamming distance from the query nodes, easily computed with bitwise logical operators. As an application example, we validate the performance of the proposed methods in a handwritten word spotting scenario in images of historical documents. |
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Beijing; China; May 2015 |
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Springer International Publishing |
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C.-L.Liu; B.Luo; W.G.Kropatsch; J.Cheng |
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0302-9743 |
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978-3-319-18223-0 |
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GbRPR |
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DAG; 600.061; 602.006; 600.077 |
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no |
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Admin @ si @ RLF2015a |
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2618 |
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Author |
Pau Riba; Josep Llados; Alicia Fornes; Anjan Dutta |
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Title ![sorted by Title field, ascending order (up)](img/sort_asc.gif) |
Large-scale graph indexing using binary embeddings of node contexts for information spotting in document image databases |
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Journal Article |
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2017 |
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Pattern Recognition Letters |
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PRL |
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87 |
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203-211 |
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Graph-based representations are experiencing a growing usage in visual recognition and retrieval due to their representational power in front of classical appearance-based representations. However, retrieving a query graph from a large dataset of graphs implies a high computational complexity. The most important property for a large-scale retrieval is the search time complexity to be sub-linear in the number of database examples. With this aim, in this paper we propose a graph indexation formalism applied to visual retrieval. A binary embedding is defined as hashing keys for graph nodes. Given a database of labeled graphs, graph nodes are complemented with vectors of attributes representing their local context. Then, each attribute vector is converted to a binary code applying a binary-valued hash function. Therefore, graph retrieval is formulated in terms of finding target graphs in the database whose nodes have a small Hamming distance from the query nodes, easily computed with bitwise logical operators. As an application example, we validate the performance of the proposed methods in different real scenarios such as handwritten word spotting in images of historical documents or symbol spotting in architectural floor plans. |
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DAG; 600.097; 602.006; 603.053; 600.121 |
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RLF2017b |
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2873 |
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Author |
S. Tanimoto; N. Bruining; David Rotger; Petia Radeva; J. Ligthart; R.T. van Domburg; P. W. Serryus |
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Title ![sorted by Title field, ascending order (up)](img/sort_asc.gif) |
Late Stent Recoil of the Bioabsorbable Everolimus Eluting Coronary Stent and its Relationship with Stent Struts Distribution and Plaque Morphology |
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2008 |
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Journal of the American College of Cardiology, vol. 52(20):1616–1620 |
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Bridgewater, NJ 08807(USA) |
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MILAB |
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BCNPCL @ bcnpcl @ TBR2008 |
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953 |
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Author |
G.Estape; Enric Marti |
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Title ![sorted by Title field, ascending order (up)](img/sort_asc.gif) |
L’ús d’aplicacions de visualització 3D com a eina d’aprenenetatge en activitats formatives dirigides i autònomes: el cas del programa Bluestar |
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Miscellaneous |
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2008 |
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V Jornades d’Innovació Docent UAB |
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IAM |
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IAM @ iam @ ESM2008 |
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1495 |
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Author |
Xavier Soria; Gonzalo Pomboza-Junez; Angel Sappa |
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Title ![sorted by Title field, ascending order (up)](img/sort_asc.gif) |
LDC: Lightweight Dense CNN for Edge Detection |
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Journal Article |
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2022 |
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IEEE Access |
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ACCESS |
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10 |
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68281-68290 |
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This paper presents a Lightweight Dense Convolutional (LDC) neural network for edge detection. The proposed model is an adaptation of two state-of-the-art approaches, but it requires less than 4% of parameters in comparison with these approaches. The proposed architecture generates thin edge maps and reaches the highest score (i.e., ODS) when compared with lightweight models (models with less than 1 million parameters), and reaches a similar performance when compare with heavy architectures (models with about 35 million parameters). Both quantitative and qualitative results and comparisons with state-of-the-art models, using different edge detection datasets, are provided. The proposed LDC does not use pre-trained weights and requires straightforward hyper-parameter settings. The source code is released at https://github.com/xavysp/LDC |
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27 June 2022 |
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IEEE |
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MSIAU; MACO; 600.160; 600.167 |
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Admin @ si @ SPS2022 |
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3751 |
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Author |
Jiaolong Xu; David Vazquez; Antonio Lopez; Javier Marin; Daniel Ponsa |
![download PDF file pdf](img/file_PDF.gif)
![find book details (via ISBN) isbn](img/isbn.gif)
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Title ![sorted by Title field, ascending order (up)](img/sort_asc.gif) |
Learning a Multiview Part-based Model in Virtual World for Pedestrian Detection |
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Conference Article |
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2013 |
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IEEE Intelligent Vehicles Symposium |
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467 - 472 |
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Pedestrian Detection; Virtual World; Part based |
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State-of-the-art deformable part-based models based on latent SVM have shown excellent results on human detection. In this paper, we propose to train a multiview deformable part-based model with automatically generated part examples from virtual-world data. The method is efficient as: (i) the part detectors are trained with precisely extracted virtual examples, thus no latent learning is needed, (ii) the multiview pedestrian detector enhances the performance of the pedestrian root model, (iii) a top-down approach is used for part detection which reduces the searching space. We evaluate our model on Daimler and Karlsruhe Pedestrian Benchmarks with publicly available Caltech pedestrian detection evaluation framework and the result outperforms the state-of-the-art latent SVM V4.0, on both average miss rate and speed (our detector is ten times faster). |
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Gold Coast; Australia; June 2013 |
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IEEE |
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1931-0587 |
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978-1-4673-2754-1 |
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IV |
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ADAS; 600.054; 600.057 |
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XVL2013; ADAS @ adas @ xvl2013a |
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2214 |
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Author |
Jiaolong Xu; David Vazquez; Antonio Lopez; Javier Marin; Daniel Ponsa |
![download PDF file pdf](img/file_PDF.gif)
![find book details (via ISBN) isbn](img/isbn.gif)
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Title ![sorted by Title field, ascending order (up)](img/sort_asc.gif) |
Learning a Part-based Pedestrian Detector in Virtual World |
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Journal Article |
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2014 |
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IEEE Transactions on Intelligent Transportation Systems |
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TITS |
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15 |
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5 |
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2121-2131 |
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Domain Adaptation; Pedestrian Detection; Virtual Worlds |
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Detecting pedestrians with on-board vision systems is of paramount interest for assisting drivers to prevent vehicle-to-pedestrian accidents. The core of a pedestrian detector is its classification module, which aims at deciding if a given image window contains a pedestrian. Given the difficulty of this task, many classifiers have been proposed during the last fifteen years. Among them, the so-called (deformable) part-based classifiers including multi-view modeling are usually top ranked in accuracy. Training such classifiers is not trivial since a proper aspect clustering and spatial part alignment of the pedestrian training samples are crucial for obtaining an accurate classifier. In this paper, first we perform automatic aspect clustering and part alignment by using virtual-world pedestrians, i.e., human annotations are not required. Second, we use a mixture-of-parts approach that allows part sharing among different aspects. Third, these proposals are integrated in a learning framework which also allows to incorporate real-world training data to perform domain adaptation between virtual- and real-world cameras. Overall, the obtained results on four popular on-board datasets show that our proposal clearly outperforms the state-of-the-art deformable part-based detector known as latent SVM. |
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1931-0587 |
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978-1-4673-2754-1 |
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ADAS; 600.076 |
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ADAS @ adas @ XVL2014 |
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2433 |
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