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Author |
Shida Beigpour |
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Title |
Illumination and object reflectance modeling |
Type |
Book Whole |
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Year |
2013 |
Publication |
PhD Thesis, Universitat Autonoma de Barcelona-CVC |
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More realistic and accurate models of the scene illumination and object reflectance can greatly improve the quality of many computer vision and computer graphics tasks. Using such model, a more profound knowledge about the interaction of light with object surfaces can be established which proves crucial to a variety of computer vision applications. In the current work, we investigate the various existing approaches to illumination and reflectance modeling and form an analysis on their shortcomings in capturing the complexity of real-world scenes. Based on this analysis we propose improvements to different aspects of reflectance and illumination estimation in order to more realistically model the real-world scenes in the presence of complex lighting phenomena (i.e, multiple illuminants, interreflections and shadows). Moreover, we captured our own multi-illuminant dataset which consists of complex scenes and illumination conditions both outdoor and in laboratory conditions. In addition we investigate the use of synthetic data to facilitate the construction of datasets and improve the process of obtaining ground-truth information. |
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Address |
Barcelona |
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Thesis |
Ph.D. thesis |
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Publisher |
Ediciones Graficas Rey |
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Editor |
Joost Van de Weijer;Ernest Valveny |
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CIC |
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no |
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Call Number |
Admin @ si @ Bei2013 |
Serial |
2267 |
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Author |
Shida Beigpour |
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Title |
Physics-based Reflectance Estimation Applied to Recoloring |
Type |
Report |
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Year |
2009 |
Publication |
CVC Technical Report |
Abbreviated Journal |
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Volume |
137 |
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Computer Vision Center |
Thesis |
Master's thesis |
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Place of Publication |
Bellaterra, Barcelona |
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CIC |
Approved |
no |
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Call Number |
Admin @ si @ Bei2009 |
Serial |
2396 |
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Author |
Parichehr Behjati Ardakani |
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Title |
Towards Efficient and Robust Convolutional Neural Networks for Single Image Super-Resolution |
Type |
Book Whole |
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Year |
2022 |
Publication |
PhD Thesis, Universitat Autonoma de Barcelona-CVC |
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Single image super-resolution (SISR) is an important task in image processing which aims to enhance the resolution of imaging systems. Recently, SISR has witnessed great strides with the rapid development of deep learning. Recent advances in SISR are mostly devoted to designing deeper and wider networks to enhance their representation learning capacity. However, as the depth of networks increases, deep learning-based methods are faced with the challenge of computational complexity in practice. Moreover, most existing methods rarely leverage the intermediate features and also do not discriminate the computation of features by their frequencial components, thereby achieving relatively low performance. Aside from the aforementioned problems, another desired ability is to upsample images to arbitrary scales using a single model. Most current SISR methods train a dedicated model for each target resolution, losing generality and increasing memory requirements. In this thesis, we address the aforementioned issues and propose solutions to them: i) We present a novel frequency-based enhancement block which treats different frequencies in a heterogeneous way and also models inter-channel dependencies, which consequently enrich the output feature. Thus it helps the network generate more discriminative representations by explicitly recovering finer details. ii) We introduce OverNet which contains two main parts: a lightweight feature extractor that follows a novel recursive framework of skip and dense connections to reduce low-level feature degradation, and an overscaling module that generates an accurate SR image by internally constructing an overscaled intermediate representation of the output features. Then, to solve the problem of reconstruction at arbitrary scale factors, we introduce a novel multi-scale loss, that allows the simultaneous training of all scale factors using a single model. iii) We propose a directional variance attention network which leverages a novel attention mechanism to enhance features in different channels and spatial regions. Moreover, we introduce a novel procedure for using attention mechanisms together with residual blocks to facilitate the preservation of finer details. Finally, we demonstrate that our approaches achieve considerably better performance than previous state-of-the-art methods, in terms of both quantitative and visual quality. |
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Address |
April, 2022 |
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Thesis |
Ph.D. thesis |
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Editor |
Jordi Gonzalez;Xavier Roca;Pau Rodriguez |
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ISBN |
978-84-124793-1-7 |
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Notes |
ISE |
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no |
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Call Number |
Admin @ si @ Beh2022 |
Serial |
3713 |
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Author |
Xavier Baro; Sergio Escalera; Isabelle Guyon; Julio C. S. Jacques Junior; Lukasz Romaszko; Lisheng Sun; Sebastien Treguer; Evelyne Viegas |
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Title |
Coompetitions in machine learning: case studies |
Type |
Conference Article |
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Year |
2016 |
Publication |
30th Annual Conference on Neural Information Processing Systems Worshops |
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Barcelona; Spain; December 2016 |
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NIPSW |
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Notes |
HuPBA |
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no |
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Call Number |
Admin @ si @ BEG2016 |
Serial |
2911 |
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Author |
Miguel Angel Bautista; Sergio Escalera; Xavier Baro; Oriol Pujol; Jordi Vitria; Petia Radeva |
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Title |
On the Design of Low Redundancy Error-Correcting Output Codes |
Type |
Book Chapter |
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Year |
2011 |
Publication |
Ensembles in Machine Learning Applications |
Abbreviated Journal |
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Volume |
373 |
Issue |
2 |
Pages |
21-38 |
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Abstract |
The classification of large number of object categories is a challenging trend in the Pattern Recognition field. In the literature, this is often addressed using an ensemble of classifiers . In this scope, the Error-Correcting Output Codes framework has demonstrated to be a powerful tool for combining classifiers. However, most of the state-of-the-art ECOC approaches use a linear or exponential number of classifiers, making the discrimination of a large number of classes unfeasible. In this paper, we explore and propose a compact design of ECOC in terms of the number of classifiers. Evolutionary computation is used for tuning the parameters of the classifiers and looking for the best compact ECOC code configuration. The results over several public UCI data sets and different multi-class Computer Vision problems show that the proposed methodology obtains comparable (even better) results than the state-of-the-art ECOC methodologies with far less number of dichotomizers. |
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Publisher |
Springer Berlin Heidelberg |
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ISSN |
1860-949X |
ISBN |
978-3-642-22909-1 |
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Notes |
MILAB; OR;HuPBA;MV |
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no |
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Call Number |
Admin @ si @ BEB2011b |
Serial |
1886 |
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Permanent link to this record |
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Author |
Miguel Angel Bautista; Sergio Escalera; Xavier Baro; Petia Radeva; Jordi Vitria; Oriol Pujol |
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Title |
Minimal Design of Error-Correcting Output Codes |
Type |
Journal Article |
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Year |
2011 |
Publication |
Pattern Recognition Letters |
Abbreviated Journal |
PRL |
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Volume |
33 |
Issue |
6 |
Pages |
693-702 |
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Keywords |
Multi-class classification; Error-correcting output codes; Ensemble of classifiers |
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Abstract |
IF JCR CCIA 1.303 2009 54/103
The classification of large number of object categories is a challenging trend in the pattern recognition field. In literature, this is often addressed using an ensemble of classifiers. In this scope, the Error-correcting output codes framework has demonstrated to be a powerful tool for combining classifiers. However, most state-of-the-art ECOC approaches use a linear or exponential number of classifiers, making the discrimination of a large number of classes unfeasible. In this paper, we explore and propose a minimal design of ECOC in terms of the number of classifiers. Evolutionary computation is used for tuning the parameters of the classifiers and looking for the best minimal ECOC code configuration. The results over several public UCI datasets and different multi-class computer vision problems show that the proposed methodology obtains comparable (even better) results than state-of-the-art ECOC methodologies with far less number of dichotomizers. |
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Publisher |
Elsevier |
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ISSN |
0167-8655 |
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Notes |
MILAB; OR;HuPBA;MV |
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no |
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Call Number |
Admin @ si @ BEB2011a |
Serial |
1800 |
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Permanent link to this record |
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Author |
Miguel Angel Bautista; Sergio Escalera; Xavier Baro; Oriol Pujol; Jordi Vitria; Petia Radeva |
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Title |
Compact Evolutive Design of Error-Correcting Output Codes. Supervised and Unsupervised Ensemble Methods and Applications |
Type |
Conference Article |
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Year |
2010 |
Publication |
European Conference on Machine Learning |
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Volume |
I |
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Pages |
119-128 |
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ECML |
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Notes |
MILAB; OR;HUPBA;MV |
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no |
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Call Number |
Admin @ si @ BEB2010 |
Serial |
1775 |
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Author |
Sonia Baeza; R.Domingo; M.Salcedo; G.Moragas; J.Deportos; I.Garcia Olive; Carles Sanchez; Debora Gil; Antoni Rosell |
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Title |
Artificial Intelligence to Optimize Pulmonary Embolism Diagnosis During Covid-19 Pandemic by Perfusion SPECT/CT, a Pilot Study |
Type |
Journal Article |
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Year |
2021 |
Publication |
American Journal of Respiratory and Critical Care Medicine |
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Notes |
IAM; 600.145 |
Approved |
no |
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Call Number |
Admin @ si @ BDS2021 |
Serial |
3591 |
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Author |
Marc Bolaños; Mariella Dimiccoli; Petia Radeva |
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Title |
Towards Storytelling from Visual Lifelogging: An Overview |
Type |
Journal Article |
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Year |
2017 |
Publication |
IEEE Transactions on Human-Machine Systems |
Abbreviated Journal |
THMS |
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Volume |
47 |
Issue |
1 |
Pages |
77 - 90 |
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Abstract |
Visual lifelogging consists of acquiring images that capture the daily experiences of the user by wearing a camera over a long period of time. The pictures taken offer considerable potential for knowledge mining concerning how people live their lives, hence, they open up new opportunities for many potential applications in fields including healthcare, security, leisure and
the quantified self. However, automatically building a story from a huge collection of unstructured egocentric data presents major challenges. This paper provides a thorough review of advances made so far in egocentric data analysis, and in view of the current state of the art, indicates new lines of research to move us towards storytelling from visual lifelogging. |
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Notes |
MILAB; 601.235 |
Approved |
no |
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Call Number |
Admin @ si @ BDR2017 |
Serial |
2712 |
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Author |
Thierry Brouard; A. Delaplace; Muhammad Muzzamil Luqman; H. Cardot; Jean-Yves Ramel |
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Title |
Design of Evolutionary Methods Applied to the Learning of Bayesian Nerwork Structures |
Type |
Book Chapter |
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Year |
2010 |
Publication |
Bayesian Network |
Abbreviated Journal |
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Volume |
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Pages |
13-37 |
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Publisher |
Sciyo |
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Editor |
Ahmed Rebai |
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ISBN |
978-953-307-124-4 |
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no |
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Call Number |
Admin @ si @ BDL2010 |
Serial |
1461 |
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Author |
Klaus Broelemann; Anjan Dutta; Xiaoyi Jiang; Josep Llados |
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Title |
Hierarchical Plausibility-Graphs for Symbol Spotting in Graphical Documents |
Type |
Book Chapter |
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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 |
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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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Publisher |
Springer Berlin Heidelberg |
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Editor |
Bart Lamiroy; Jean-Marc Ogier |
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LNCS |
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ISSN |
0302-9743 |
ISBN |
978-3-662-44853-3 |
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Notes |
DAG; 600.045; 600.056; 600.061; 600.077 |
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no |
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Call Number |
Admin @ si @ BDJ2014 |
Serial |
2699 |
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Author |
Klaus Broelemann; Anjan Dutta; Xiaoyi Jiang; Josep Llados |
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Title |
Plausibility-Graphs for Symbol Spotting in Graphical Documents |
Type |
Conference Article |
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Year |
2013 |
Publication |
10th IAPR International Workshop on Graphics Recognition |
Abbreviated Journal |
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Abstract |
Graph representation of graphical documents often suffers from noise viz. spurious nodes and spurios edges of graph and their discontinuity etc. 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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Bethlehem; PA; USA; August 2013 |
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GREC |
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Notes |
DAG; 600.045; 600.056; 600.061; 601.152 |
Approved |
no |
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Call Number |
Admin @ si @ BDJ2013 |
Serial |
2360 |
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Permanent link to this record |
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Author |
Klaus Broelemann; Anjan Dutta; Xiaoyi Jiang; Josep Llados |
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Title |
Hierarchical graph representation for symbol spotting in graphical document images |
Type |
Conference Article |
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Year |
2012 |
Publication |
Structural, Syntactic, and Statistical Pattern Recognition, Joint IAPR International Workshop |
Abbreviated Journal |
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Volume |
7626 |
Issue |
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Pages |
529-538 |
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Abstract |
Symbol spotting can be defined as locating given query symbol in a large collection of graphical documents. In this paper we present a hierarchical graph representation for symbols. This representation allows graph matching methods to deal with low-level vectorization errors and, thus, to perform a robust symbol spotting. To show the potential of this approach, we conduct an experiment with the SESYD dataset. |
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Address |
Miyajima-Itsukushima, Hiroshima |
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Publisher |
Springer Berlin Heidelberg |
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LNCS |
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ISSN |
0302-9743 |
ISBN |
978-3-642-34165-6 |
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SSPR&SPR |
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Notes |
DAG |
Approved |
no |
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Call Number |
Admin @ si @ BDJ2012 |
Serial |
2126 |
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Permanent link to this record |
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Author |
Simone Balocco; Francesco Ciompi; Juan Rigla; Xavier Carrillo; J. Mauri; Petia Radeva |
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Title |
Assessment of intracoronary stent location and extension in intravascular ultrasound sequences |
Type |
Journal Article |
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Year |
2019 |
Publication |
Medical Physics |
Abbreviated Journal |
MEDPHYS |
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Volume |
46 |
Issue |
2 |
Pages |
484-493 |
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Keywords |
IVUS; malapposition; stent; ultrasound |
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Abstract |
PURPOSE:
An intraluminal coronary stent is a metal scaffold deployed in a stenotic artery during percutaneous coronary intervention (PCI). In order to have an effective deployment, a stent should be optimally placed with regard to anatomical structures such as bifurcations and stenoses. Intravascular ultrasound (IVUS) is a catheter-based imaging technique generally used for PCI guiding and assessing the correct placement of the stent. A novel approach that automatically detects the boundaries and the position of the stent along the IVUS pullback is presented. Such a technique aims at optimizing the stent deployment.
METHODS:
The method requires the identification of the stable frames of the sequence and the reliable detection of stent struts. Using these data, a measure of likelihood for a frame to contain a stent is computed. Then, a robust binary representation of the presence of the stent in the pullback is obtained applying an iterative and multiscale quantization of the signal to symbols using the Symbolic Aggregate approXimation algorithm.
RESULTS:
The technique was extensively validated on a set of 103 IVUS of sequences of in vivo coronary arteries containing metallic and bioabsorbable stents acquired through an international multicentric collaboration across five clinical centers. The method was able to detect the stent position with an overall F-measure of 86.4%, a Jaccard index score of 75% and a mean distance of 2.5 mm from manually annotated stent boundaries, and in bioabsorbable stents with an overall F-measure of 88.6%, a Jaccard score of 77.7 and a mean distance of 1.5 mm from manually annotated stent boundaries. Additionally, a map indicating the distance between the lumen and the stent along the pullback is created in order to show the angular sectors of the sequence in which the malapposition is present.
CONCLUSIONS:
Results obtained comparing the automatic results vs the manual annotation of two observers shows that the method approaches the interobserver variability. Similar performances are obtained on both metallic and bioabsorbable stents, showing the flexibility and robustness of the method. |
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Author |
Simone Balocco; Francesco Ciompi; Juan Rigla; Xavier Carrillo; J. Mauri; Petia Radeva |
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Intra-Coronary Stent localization In Intravascular Ultrasound Sequences, A Preliminary Study |
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Conference Article |
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2017 |
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International workshop on Computing and Visualization for Intravascular Imaging and Computer Assisted Stenting (CVII-STENT) |
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Abstract |
An intraluminal coronary stent is a metal scaold deployed in a stenotic artery during Percutaneous Coronary Intervention (PCI).
Intravascular Ultrasound (IVUS) is a catheter-based imaging technique generally used for assessing the correct placement of the stent. All the approaches proposed so far for the stent analysis only focused on the struts detection, while this paper proposes a novel approach to detect the boundaries and the position of the stent along the pullback.
The pipeline of the method requires the identication of the stable frames
of the sequence and the reliable detection of stent struts. Using this data,
a measure of likelihood for a frame to contain a stent is computed. Then,
a robust binary representation of the presence of the stent in the pullback
is obtained applying an iterative and multi-scale approximation of the signal to symbols using the SAX algorithm. Results obtained comparing the automatic results versus the manual annotation of two observers on 80 IVUS in-vivo sequences shows that the method approaches the inter-observer variability scores. |
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Quebec; Canada; September 2017 |
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MICCAIW |
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MILAB; no proj |
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Admin @ si @ BCR2017 |
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2968 |
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