Records |
Author |
Alicia Fornes; V.C.Kieu; M. Visani; N.Journet; Anjan Dutta |
Title |
The ICDAR/GREC 2013 Music Scores Competition: Staff Removal |
Type |
Book Chapter |
Year |
2014 |
Publication |
Graphics Recognition. Current Trends and Challenges |
Abbreviated Journal |
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Volume |
8746 |
Issue |
|
Pages |
207-220 |
Keywords |
Competition; Graphics recognition; Music scores; Writer identification; Staff removal |
Abstract |
The first competition on music scores that was organized at ICDAR and GREC in 2011 awoke the interest of researchers, who participated in both staff removal and writer identification tasks. In this second edition, we focus on the staff removal task and simulate a real case scenario concerning old and degraded music scores. For this purpose, we have generated a new set of semi-synthetic images using two degradation models that we previously introduced: local noise and 3D distortions. In this extended paper we provide an extended description of the dataset, degradation models, evaluation metrics, the participant’s methods and the obtained results that could not be presented at ICDAR and GREC proceedings due to page limitations. |
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Thesis |
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Publisher |
Springer Berlin Heidelberg |
Place of Publication |
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Editor |
B.Lamiroy; J.-M. Ogier |
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LNCS |
Series Volume |
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Series Issue |
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Edition |
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ISSN ![sorted by ISSN field, ascending order (up)](img/sort_asc.gif) |
0302-9743 |
ISBN |
978-3-662-44853-3 |
Medium |
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Area |
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Expedition |
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Conference |
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Notes |
DAG; 600.077; 600.061 |
Approved |
no |
Call Number |
Admin @ si @ FKV2014 |
Serial |
2581 |
Permanent link to this record |
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Author |
Marc Bolaños; Maite Garolera; Petia Radeva |
Title |
Object Discovery using CNN Features in Egocentric Videos |
Type |
Conference Article |
Year |
2015 |
Publication |
Pattern Recognition and Image Analysis, Proceedings of 7th Iberian Conference , ibPRIA 2015 |
Abbreviated Journal |
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Volume |
9117 |
Issue |
|
Pages |
67-74 |
Keywords |
Object discovery; Egocentric videos; Lifelogging; CNN |
Abstract |
Lifelogging devices based on photo/video are spreading faster everyday. This growth can represent great benefits to develop methods for extraction of meaningful information about the user wearing the device and his/her environment. In this paper, we propose a semi-supervised strategy for easily discovering objects relevant to the person wearing a first-person camera. The egocentric video sequence acquired by the camera, uses both the appearance extracted by means of a deep convolutional neural network and an object refill methodology that allow to discover objects even in case of small amount of object appearance in the collection of images. We validate our method on a sequence of 1000 egocentric daily images and obtain results with an F-measure of 0.5, 0.17 better than the state of the art approach. |
Address |
Santiago de Compostela; España; June 2015 |
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Abbreviated Series Title |
LNCS |
Series Volume |
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Series Issue |
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Edition |
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ISSN ![sorted by ISSN field, ascending order (up)](img/sort_asc.gif) |
0302-9743 |
ISBN |
978-3-319-19389-2 |
Medium |
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Area |
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Expedition |
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Conference |
IbPRIA |
Notes |
MILAB |
Approved |
no |
Call Number |
Admin @ si @ BGR2015 |
Serial |
2596 |
Permanent link to this record |
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Author |
Estefania Talavera; Mariella Dimiccoli; Marc Bolaños; Maedeh Aghaei; Petia Radeva |
Title |
R-clustering for egocentric video segmentation |
Type |
Conference Article |
Year |
2015 |
Publication |
Pattern Recognition and Image Analysis, Proceedings of 7th Iberian Conference , ibPRIA 2015 |
Abbreviated Journal |
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Volume |
9117 |
Issue |
|
Pages |
327-336 |
Keywords |
Temporal video segmentation; Egocentric videos; Clustering |
Abstract |
In this paper, we present a new method for egocentric video temporal segmentation based on integrating a statistical mean change detector and agglomerative clustering(AC) within an energy-minimization framework. Given the tendency of most AC methods to oversegment video sequences when clustering their frames, we combine the clustering with a concept drift detection technique (ADWIN) that has rigorous guarantee of performances. ADWIN serves as a statistical upper bound for the clustering-based video segmentation. We integrate both techniques in an energy-minimization framework that serves to disambiguate the decision of both techniques and to complete the segmentation taking into account the temporal continuity of video frames descriptors. We present experiments over egocentric sets of more than 13.000 images acquired with different wearable cameras, showing that our method outperforms state-of-the-art clustering methods. |
Address |
Santiago de Compostela; España; June 2015 |
Corporate Author |
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Thesis |
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Publisher |
Springer International Publishing |
Place of Publication |
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Abbreviated Series Title |
LNCS |
Series Volume |
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Series Issue |
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Edition |
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ISSN ![sorted by ISSN field, ascending order (up)](img/sort_asc.gif) |
0302-9743 |
ISBN |
978-3-319-19389-2 |
Medium |
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Area |
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Expedition |
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Conference |
IbPRIA |
Notes |
MILAB |
Approved |
no |
Call Number |
Admin @ si @ TDB2015 |
Serial |
2597 |
Permanent link to this record |
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Author |
Pau Riba; Josep Llados; Alicia Fornes; Anjan Dutta |
Title |
Large-scale Graph Indexing using Binary Embeddings of Node Contexts |
Type |
Conference Article |
Year |
2015 |
Publication |
10th IAPR-TC15 Workshop on Graph-based Representations in Pattern Recognition |
Abbreviated Journal |
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Volume |
9069 |
Issue |
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Pages |
208-217 |
Keywords |
Graph matching; Graph indexing; Application in document analysis; Word spotting; Binary embedding |
Abstract |
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. |
Address |
Beijing; China; May 2015 |
Corporate Author |
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Thesis |
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Publisher |
Springer International Publishing |
Place of Publication |
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Editor |
C.-L.Liu; B.Luo; W.G.Kropatsch; J.Cheng |
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Original Title |
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Abbreviated Series Title |
LNCS |
Series Volume |
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Series Issue |
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Edition |
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ISSN ![sorted by ISSN field, ascending order (up)](img/sort_asc.gif) |
0302-9743 |
ISBN |
978-3-319-18223-0 |
Medium |
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Area |
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Expedition |
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Conference |
GbRPR |
Notes |
DAG; 600.061; 602.006; 600.077 |
Approved |
no |
Call Number |
Admin @ si @ RLF2015a |
Serial |
2618 |
Permanent link to this record |
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Author |
Onur Ferhat; Arcadi Llanza; Fernando Vilariño |
Title |
A Feature-Based Gaze Estimation Algorithm for Natural Light Scenarios |
Type |
Conference Article |
Year |
2015 |
Publication |
Pattern Recognition and Image Analysis, Proceedings of 7th Iberian Conference , ibPRIA 2015 |
Abbreviated Journal |
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Volume |
9117 |
Issue |
|
Pages |
569-576 |
Keywords |
Eye tracking; Gaze estimation; Natural light; Webcam |
Abstract |
We present an eye tracking system that works with regular webcams. We base our work on open source CVC Eye Tracker [7] and we propose a number of improvements and a novel gaze estimation method. The new method uses features extracted from iris segmentation and it does not fall into the traditional categorization of appearance–based/model–based methods. Our experiments show that our approach reduces the gaze estimation errors by 34 % in the horizontal direction and by 12 % in the vertical direction compared to the baseline system. |
Address |
Santiago de Compostela; June 2015 |
Corporate Author |
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Thesis |
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Publisher |
Springer International Publishing |
Place of Publication |
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Editor |
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Language |
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Summary Language |
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Series Editor |
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Abbreviated Series Title |
LNCS |
Series Volume |
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Series Issue |
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Edition |
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ISSN ![sorted by ISSN field, ascending order (up)](img/sort_asc.gif) |
0302-9743 |
ISBN |
978-3-319-19389-2 |
Medium |
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Area |
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Expedition |
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Conference |
IbPRIA |
Notes |
MV;SIAI |
Approved |
no |
Call Number |
Admin @ si @ FLV2015a |
Serial |
2646 |
Permanent link to this record |
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Author |
Dennis G.Romero; Anselmo Frizera; Angel Sappa; Boris X. Vintimilla; Teodiano F.Bastos |
Title |
A predictive model for human activity recognition by observing actions and context |
Type |
Conference Article |
Year |
2015 |
Publication |
Advanced Concepts for Intelligent Vision Systems, Proceedings of 16th International Conference, ACIVS 2015 |
Abbreviated Journal |
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Volume |
9386 |
Issue |
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Pages |
323-333 |
Keywords |
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Abstract |
This paper presents a novel model to estimate human activities — a human activity is defined by a set of human actions. The proposed approach is based on the usage of Recurrent Neural Networks (RNN) and Bayesian inference through the continuous monitoring of human actions and its surrounding environment. In the current work human activities are inferred considering not only visual analysis but also additional resources; external sources of information, such as context information, are incorporated to contribute to the activity estimation. The novelty of the proposed approach lies in the way the information is encoded, so that it can be later associated according to a predefined semantic structure. Hence, a pattern representing a given activity can be defined by a set of actions, plus contextual information or other kind of information that could be relevant to describe the activity. Experimental results with real data are provided showing the validity of the proposed approach. |
Address |
Catania; Italy; October 2015 |
Corporate Author |
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Thesis |
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Publisher |
Springer International Publishing |
Place of Publication |
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Editor |
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Original Title |
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Series Editor |
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Series Title |
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Abbreviated Series Title |
LNCS |
Series Volume |
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Series Issue |
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Edition |
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ISSN ![sorted by ISSN field, ascending order (up)](img/sort_asc.gif) |
0302-9743 |
ISBN |
978-3-319-25902-4 |
Medium |
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Area |
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Expedition |
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Conference |
ACIVS |
Notes |
ADAS; 600.076 |
Approved |
no |
Call Number |
Admin @ si @ RFS2015 |
Serial |
2661 |
Permanent link to this record |
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Author |
Fahad Shahbaz Khan; Muhammad Anwer Rao; Joost Van de Weijer; Michael Felsberg; J.Laaksonen |
Title |
Deep semantic pyramids for human attributes and action recognition |
Type |
Conference Article |
Year |
2015 |
Publication |
Image Analysis, Proceedings of 19th Scandinavian Conference , SCIA 2015 |
Abbreviated Journal |
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Volume |
9127 |
Issue |
|
Pages |
341-353 |
Keywords |
Action recognition; Human attributes; Semantic pyramids |
Abstract |
Describing persons and their actions is a challenging problem due to variations in pose, scale and viewpoint in real-world images. Recently, semantic pyramids approach [1] for pose normalization has shown to provide excellent results for gender and action recognition. The performance of semantic pyramids approach relies on robust image description and is therefore limited due to the use of shallow local features. In the context of object recognition [2] and object detection [3], convolutional neural networks (CNNs) or deep features have shown to improve the performance over the conventional shallow features.
We propose deep semantic pyramids for human attributes and action recognition. The method works by constructing spatial pyramids based on CNNs of different part locations. These pyramids are then combined to obtain a single semantic representation. We validate our approach on the Berkeley and 27 Human Attributes datasets for attributes classification. For action recognition, we perform experiments on two challenging datasets: Willow and PASCAL VOC 2010. The proposed deep semantic pyramids provide a significant gain of 17.2%, 13.9%, 24.3% and 22.6% compared to the standard shallow semantic pyramids on Berkeley, 27 Human Attributes, Willow and PASCAL VOC 2010 datasets respectively. Our results also show that deep semantic pyramids outperform conventional CNNs based on the full bounding box of the person. Finally, we compare our approach with state-of-the-art methods and show a gain in performance compared to best methods in literature. |
Address |
Denmark; Copenhagen; June 2015 |
Corporate Author |
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Thesis |
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Publisher |
Springer International Publishing |
Place of Publication |
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Editor |
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Summary Language |
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Original Title |
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Series Editor |
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Series Title |
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Abbreviated Series Title |
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Series Volume |
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Series Issue |
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Edition |
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ISSN ![sorted by ISSN field, ascending order (up)](img/sort_asc.gif) |
0302-9743 |
ISBN |
978-3-319-19664-0 |
Medium |
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Area |
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Expedition |
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Conference |
SCIA |
Notes |
LAMP; 600.068; 600.079;ADAS |
Approved |
no |
Call Number |
Admin @ si @ KRW2015b |
Serial |
2672 |
Permanent link to this record |
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Author |
Klaus Broelemann; Anjan Dutta; Xiaoyi Jiang; Josep Llados |
Title |
Hierarchical Plausibility-Graphs for Symbol Spotting in Graphical Documents |
Type |
Book Chapter |
Year |
2014 |
Publication |
Graphics Recognition. Current Trends and Challenges |
Abbreviated Journal |
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Volume |
8746 |
Issue |
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Pages |
25-37 |
Keywords |
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Abstract |
Graph representation of graphical documents often suffers from noise such as spurious nodes and edges, and their discontinuity. In general these errors occur during the low-level image processing viz. binarization, skeletonization, vectorization etc. Hierarchical graph representation is a nice and efficient way to solve this kind of problem by hierarchically merging node-node and node-edge depending on the distance. But the creation of hierarchical graph representing the graphical information often uses hard thresholds on the distance to create the hierarchical nodes (next state) of the lower nodes (or states) of a graph. As a result, the representation often loses useful information. This paper introduces plausibilities to the nodes of hierarchical graph as a function of distance and proposes a modified algorithm for matching subgraphs of the hierarchical graphs. The plausibility-annotated nodes help to improve the performance of the matching algorithm on two hierarchical structures. To show the potential of this approach, we conduct an experiment with the SESYD dataset. |
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Corporate Author |
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Thesis |
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Publisher |
Springer Berlin Heidelberg |
Place of Publication |
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Editor |
Bart Lamiroy; Jean-Marc Ogier |
Language |
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Summary Language |
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Original Title |
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Series Editor |
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Series Title |
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Abbreviated Series Title |
LNCS |
Series Volume |
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Series Issue |
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Edition |
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ISSN ![sorted by ISSN field, ascending order (up)](img/sort_asc.gif) |
0302-9743 |
ISBN |
978-3-662-44853-3 |
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Area |
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Expedition |
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Conference |
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Notes |
DAG; 600.045; 600.056; 600.061; 600.077 |
Approved |
no |
Call Number |
Admin @ si @ BDJ2014 |
Serial |
2699 |
Permanent link to this record |
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Author |
Marçal Rusiñol; Dimosthenis Karatzas; Josep Llados |
Title |
Spotting Graphical Symbols in Camera-Acquired Documents in Real Time |
Type |
Book Chapter |
Year |
2014 |
Publication |
Graphics Recognition. Current Trends and Challenges |
Abbreviated Journal |
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Volume |
8746 |
Issue |
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Pages |
3-10 |
Keywords |
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Abstract |
In this paper we present a system devoted to spot graphical symbols in camera-acquired document images. The system is based on the extraction and further matching of ORB compact local features computed over interest key-points. Then, the FLANN indexing framework based on approximate nearest neighbor search allows to efficiently match local descriptors between the captured scene and the graphical models. Finally, the RANSAC algorithm is used in order to compute the homography between the spotted symbol and its appearance in the document image. The proposed approach is efficient and is able to work in real time. |
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Corporate Author |
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Thesis |
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Publisher |
Springer Berlin Heidelberg |
Place of Publication |
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Editor |
Bart Lamiroy; Jean-Marc Ogier |
Language |
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Summary Language |
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Original Title |
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Series Editor |
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Series Title |
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Abbreviated Series Title |
LNCS |
Series Volume |
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Series Issue |
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Edition |
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ISSN ![sorted by ISSN field, ascending order (up)](img/sort_asc.gif) |
0302-9743 |
ISBN |
978-3-662-44853-3 |
Medium |
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Area |
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Expedition |
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Conference |
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Notes |
DAG; 600.045; 600.055; 600.061; 600.077 |
Approved |
no |
Call Number |
Admin @ si @ RKL2014 |
Serial |
2700 |
Permanent link to this record |
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Author |
Marçal Rusiñol; V. Poulain d'Andecy; Dimosthenis Karatzas; Josep Llados |
Title |
Classification of Administrative Document Images by Logo Identification |
Type |
Book Chapter |
Year |
2014 |
Publication |
Graphics Recognition. Current Trends and Challenges |
Abbreviated Journal |
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Volume |
8746 |
Issue |
|
Pages |
49-58 |
Keywords |
Administrative Document Classification; Logo Recognition; Logo Spotting |
Abstract |
This paper is focused on the categorization of administrative document images (such as invoices) based on the recognition of the supplier’s graphical logo. Two different methods are proposed, the first one uses a bag-of-visual-words model whereas the second one tries to locate logo images described by the blurred shape model descriptor within documents by a sliding-window technique. Preliminar results are reported with a dataset of real administrative documents. |
Address |
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Corporate Author |
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Thesis |
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Publisher |
Springer Berlin Heidelberg |
Place of Publication |
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Editor |
Bart Lamiroy; Jean-Marc Ogier |
Language |
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Summary Language |
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Original Title |
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Series Editor |
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Series Title |
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Series Volume |
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Series Issue |
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Edition |
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ISSN ![sorted by ISSN field, ascending order (up)](img/sort_asc.gif) |
0302-9743 |
ISBN |
978-3-662-44853-3 |
Medium |
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Area |
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Expedition |
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Conference |
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Notes |
DAG; 600.056; 600.045; 605.203; 600.077 |
Approved |
no |
Call Number |
Admin @ si @ RPK2014 |
Serial |
2701 |
Permanent link to this record |
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Author |
Suman Ghosh; Ernest Valveny |
Title |
A Sliding Window Framework for Word Spotting Based on Word Attributes |
Type |
Conference Article |
Year |
2015 |
Publication |
Pattern Recognition and Image Analysis, Proceedings of 7th Iberian Conference , ibPRIA 2015 |
Abbreviated Journal |
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Volume |
9117 |
Issue |
|
Pages |
652-661 |
Keywords |
Word spotting; Sliding window; Word attributes |
Abstract |
In this paper we propose a segmentation-free approach to word spotting. Word images are first encoded into feature vectors using Fisher Vector. Then, these feature vectors are used together with pyramidal histogram of characters labels (PHOC) to learn SVM-based attribute models. Documents are represented by these PHOC based word attributes. To efficiently compute the word attributes over a sliding window, we propose to use an integral image representation of the document using a simplified version of the attribute model. Finally we re-rank the top word candidates using the more discriminative full version of the word attributes. We show state-of-the-art results for segmentation-free query-by-example word spotting in single-writer and multi-writer standard datasets. |
Address |
Santiago de Compostela; June 2015 |
Corporate Author |
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Thesis |
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Publisher |
Springer International Publishing |
Place of Publication |
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Editor |
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Summary Language |
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Original Title |
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Series Editor |
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Series Title |
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Abbreviated Series Title |
LNCS |
Series Volume |
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Series Issue |
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Edition |
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ISSN ![sorted by ISSN field, ascending order (up)](img/sort_asc.gif) |
0302-9743 |
ISBN |
978-3-319-19389-2 |
Medium |
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Area |
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Expedition |
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Conference |
IbPRIA |
Notes |
DAG; 600.077 |
Approved |
no |
Call Number |
Admin @ si @ GhV2015b |
Serial |
2716 |
Permanent link to this record |
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Author |
Hanne Kause; Aura Hernandez-Sabate; Patricia Marquez; Andrea Fuster; Luc Florack; Hans van Assen; Debora Gil |
Title |
Confidence Measures for Assessing the HARP Algorithm in Tagged Magnetic Resonance Imaging |
Type |
Book Chapter |
Year |
2015 |
Publication |
Statistical Atlases and Computational Models of the Heart. Revised selected papers of Imaging and Modelling Challenges 6th International Workshop, STACOM 2015, Held in Conjunction with MICCAI 2015 |
Abbreviated Journal |
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Volume |
9534 |
Issue |
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Pages |
69-79 |
Keywords |
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Abstract |
Cardiac deformation and changes therein have been linked to pathologies. Both can be extracted in detail from tagged Magnetic Resonance Imaging (tMRI) using harmonic phase (HARP) images. Although point tracking algorithms have shown to have high accuracies on HARP images, these vary with position. Detecting and discarding areas with unreliable results is crucial for use in clinical support systems. This paper assesses the capability of two confidence measures (CMs), based on energy and image structure, for detecting locations with reduced accuracy in motion tracking results. These CMs were tested on a database of simulated tMRI images containing the most common artifacts that may affect tracking accuracy. CM performance is assessed based on its capability for HARP tracking error bounding and compared in terms of significant differences detected using a multi comparison analysis of variance that takes into account the most influential factors on HARP tracking performance. Results showed that the CM based on image structure was better suited to detect unreliable optical flow vectors. In addition, it was shown that CMs can be used to detect optical flow vectors with large errors in order to improve the optical flow obtained with the HARP tracking algorithm. |
Address |
Munich; Germany; January 2015 |
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Publisher |
Springer International Publishing |
Place of Publication |
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Series Title |
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LNCS |
Series Volume |
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Edition |
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ISSN ![sorted by ISSN field, ascending order (up)](img/sort_asc.gif) |
0302-9743 |
ISBN |
978-3-319-28711-9 |
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Area |
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Expedition |
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Conference |
STACOM |
Notes |
ADAS; IAM; 600.075; 600.076; 600.060; 601.145 |
Approved |
no |
Call Number |
Admin @ si @ KHM2015 |
Serial |
2734 |
Permanent link to this record |
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Author |
Aleksandr Setkov; Fabio Martinez Carillo; Michele Gouiffes; Christian Jacquemin; Maria Vanrell; Ramon Baldrich |
Title |
DAcImPro: A Novel Database of Acquired Image Projections and Its Application to Object Recognition |
Type |
Conference Article |
Year |
2015 |
Publication |
Advances in Visual Computing. Proceedings of 11th International Symposium, ISVC 2015 Part II |
Abbreviated Journal |
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Volume |
9475 |
Issue |
|
Pages |
463-473 |
Keywords |
Projector-camera systems; Feature descriptors; Object recognition |
Abstract |
Projector-camera systems are designed to improve the projection quality by comparing original images with their captured projections, which is usually complicated due to high photometric and geometric variations. Many research works address this problem using their own test data which makes it extremely difficult to compare different proposals. This paper has two main contributions. Firstly, we introduce a new database of acquired image projections (DAcImPro) that, covering photometric and geometric conditions and providing data for ground-truth computation, can serve to evaluate different algorithms in projector-camera systems. Secondly, a new object recognition scenario from acquired projections is presented, which could be of a great interest in such domains, as home video projections and public presentations. We show that the task is more challenging than the classical recognition problem and thus requires additional pre-processing, such as color compensation or projection area selection. |
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Corporate Author |
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Publisher |
Springer International Publishing |
Place of Publication |
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Summary Language |
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Series Editor |
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Series Title |
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Abbreviated Series Title |
LNCS |
Series Volume |
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Series Issue |
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Edition |
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ISSN ![sorted by ISSN field, ascending order (up)](img/sort_asc.gif) |
0302-9743 |
ISBN |
978-3-319-27862-9 |
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Area |
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Expedition |
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Conference |
ISVC |
Notes |
CIC |
Approved |
no |
Call Number |
Admin @ si @ SMG2015 |
Serial |
2736 |
Permanent link to this record |
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Author |
L. Calvet; A. Ferrer; M. Gomes; A. Juan; David Masip |
Title |
Combining Statistical Learning with Metaheuristics for the Multi-Depot Vehicle Routing Problem with Market Segmentation |
Type |
Journal Article |
Year |
2016 |
Publication |
Computers & Industrial Engineering |
Abbreviated Journal |
CIE |
Volume |
94 |
Issue |
|
Pages |
93-104 |
Keywords |
Multi-Depot Vehicle Routing Problem; market segmentation applications; hybrid algorithms; statistical learning |
Abstract |
In real-life logistics and distribution activities it is usual to face situations in which the distribution of goods has to be made from multiple warehouses or depots to the nal customers. This problem is known as the Multi-Depot Vehicle Routing Problem (MDVRP), and it typically includes two sequential and correlated stages: (a) the assignment map of customers to depots, and (b) the corresponding design of the distribution routes. Most of the existing work in the literature has focused on minimizing distance-based distribution costs while satisfying a number of capacity constraints. However, no attention has been given so far to potential variations in demands due to the tness of the customerdepot mapping in the case of heterogeneous depots. In this paper, we consider this realistic version of the problem in which the depots are heterogeneous in terms of their commercial oer and customers show dierent willingness to consume depending on how well the assigned depot ts their preferences. Thus, we assume that dierent customer-depot assignment maps will lead to dierent customer-expenditure levels. As a consequence, market-segmentation strategiesneed to be considered in order to increase sales and total income while accounting for the distribution costs. To solve this extension of the MDVRP, we propose a hybrid approach that combines statistical learning techniques with a metaheuristic framework. First, a set of predictive models is generated from historical data. These statistical models allow estimating the demand of any customer depending on the assigned depot. Then, the estimated expenditure of each customer is included as part of an enriched objective function as a way to better guide the stochastic local search inside the metaheuristic framework. A set of computational experiments contribute to illustrate our approach and how the extended MDVRP considered here diers in terms of the proposed solutions from the traditional one. |
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PERGAMON-ELSEVIER SCIENCE LTD |
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0360-8352 |
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Admin @ si @ CFG2016 |
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2749 |
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Author |
Aura Hernandez-Sabate; Debora Gil; Jaume Garcia; Enric Marti |
Title |
Image-based Cardiac Phase Retrieval in Intravascular Ultrasound Sequences |
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Journal Article |
Year |
2011 |
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IEEE Transactions on Ultrasonics, Ferroelectrics and Frequency Control |
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T-UFFC |
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58 |
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1 |
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60-72 |
Keywords |
3-D exploring; ECG; band-pass filter; cardiac motion; cardiac phase retrieval; coronary arteries; electrocardiogram signal; image intensity local mean evolution; image-based cardiac phase retrieval; in vivo pullbacks acquisition; intravascular ultrasound sequences; longitudinal motion; signal extrema; time 36 ms; band-pass filters; biomedical ultrasonics; cardiovascular system; electrocardiography; image motion analysis; image retrieval; image sequences; medical image processing; ultrasonic imaging |
Abstract |
Longitudinal motion during in vivo pullbacks acquisition of intravascular ultrasound (IVUS) sequences is a major artifact for 3-D exploring of coronary arteries. Most current techniques are based on the electrocardiogram (ECG) signal to obtain a gated pullback without longitudinal motion by using specific hardware or the ECG signal itself. We present an image-based approach for cardiac phase retrieval from coronary IVUS sequences without an ECG signal. A signal reflecting cardiac motion is computed by exploring the image intensity local mean evolution. The signal is filtered by a band-pass filter centered at the main cardiac frequency. Phase is retrieved by computing signal extrema. The average frame processing time using our setup is 36 ms. Comparison to manually sampled sequences encourages a deeper study comparing them to ECG signals. |
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0885-3010 |
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IAM;ADAS |
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IAM @ iam @ HGG2011 |
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1546 |
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