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Author |
G. de Oliveira; Mariella Dimiccoli; Petia Radeva |
![find record details (via OpenURL) openurl](img/xref.gif)
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Title |
Egocentric Image Retrieval With Deep Convolutional Neural Networks |
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Conference Article |
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2016 |
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19th International Conference of the Catalan Association for Artificial Intelligence |
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71-76 |
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Barcelona; Spain; October 2016 |
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MILAB |
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no |
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Admin @ si @ODR2016 |
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2790 |
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Author |
Fernando Vilariño |
![find record details (via OpenURL) openurl](img/xref.gif)
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Title |
Dissemination, creation and education from archives: Case study of the collection of Digitized Visual Poems from Joan Brossa Foundation |
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Conference Article |
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2016 |
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International Workshop on Poetry: Archives, Poetries and Receptions |
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Address ![sorted by Address field, descending order (down)](img/sort_desc.gif) |
Barcelona; Spain; October 2016 |
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POETRY |
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MV; 600.097;SIAI |
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no |
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Admin @ si @Vil2016b |
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2805 |
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Author |
Jose A. Garcia; David Masip; Valerio Sbragaglia; Jacopo Aguzzi |
![download PDF file pdf](img/file_PDF.gif)
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Title |
Automated Identification and Tracking of Nephrops norvegicus (L.) Using Infrared and Monochromatic Blue Light |
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Conference Article |
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2016 |
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19th International Conference of the Catalan Association for Artificial Intelligence |
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computer vision; video analysis; object recognition; tracking; behaviour; social; decapod; Nephrops norvegicus |
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Automated video and image analysis can be a very efficient tool to analyze
animal behavior based on sociality, especially in hard access environments
for researchers. The understanding of this social behavior can play a key role in the sustainable design of capture policies of many species. This paper proposes the use of computer vision algorithms to identify and track a specific specie, the Norway lobster, Nephrops norvegicus, a burrowing decapod with relevant commercial value which is captured by trawling. These animals can only be captured when are engaged in seabed excursions, which are strongly related with their social behavior.
This emergent behavior is modulated by the day-night cycle, but their social
interactions remain unknown to the scientific community. The paper introduces an identification scheme made of four distinguishable black and white tags (geometric shapes). The project has recorded 15-day experiments in laboratory pools, under monochromatic blue light (472 nm.) and darkness conditions (recorded using Infra Red light). Using this massive image set, we propose a comparative of state-ofthe-art computer vision algorithms to distinguish and track the different animals’ movements. We evaluate the robustness to the high noise presence in the infrared video signals and free out-of-plane rotations due to animal movement. The experiments show promising accuracies under a cross-validation protocol, being adaptable to the automation and analysis of large scale data. In a second contribution, we created an extensive dataset of shapes (46027 different shapes) from four daily experimental video recordings, which will be available to the community. |
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Barcelona; Spain; October 2016 |
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OR;MV; |
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Admin @ si @ GMS2016 |
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2816 |
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Fernando Alonso; Xavier Baro; Sergio Escalera; Jordi Gonzalez; Martha Mackay; Anna Serrahima |
![download PDF file pdf](img/file_PDF.gif)
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Title |
CARE RESPITE: TAKING CARE OF THE CAREGIVERS, Theme 5 The Strategic use of Mobile and Digital Health and Care Solutions |
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Conference Article |
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2016 |
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16th International Conference for Integrated Care |
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Poster |
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Barcelona; Spain; May 2016 |
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ICIC |
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HuPBA; ISE;MV |
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no |
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Admin @ si @ ABE2016 |
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2855 |
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Author |
Yaxing Wang; L. Zhang; Joost Van de Weijer |
![download PDF file pdf](img/file_PDF.gif)
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Title |
Ensembles of generative adversarial networks |
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Conference Article |
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2016 |
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30th Annual Conference on Neural Information Processing Systems Worshops |
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Ensembles are a popular way to improve results of discriminative CNNs. The
combination of several networks trained starting from different initializations
improves results significantly. In this paper we investigate the usage of ensembles of GANs. The specific nature of GANs opens up several new ways to construct ensembles. The first one is based on the fact that in the minimax game which is played to optimize the GAN objective the generator network keeps on changing even after the network can be considered optimal. As such ensembles of GANs can be constructed based on the same network initialization but just taking models which have different amount of iterations. These so-called self ensembles are much faster to train than traditional ensembles. The second method, called cascade GANs, redirects part of the training data which is badly modeled by the first GAN to another GAN. In experiments on the CIFAR10 dataset we show that ensembles of GANs obtain model probability distributions which better model the data distribution. In addition, we show that these improved results can be obtained at little additional computational cost. |
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Address ![sorted by Address field, descending order (down)](img/sort_desc.gif) |
Barcelona; Spain; December 2016 |
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NIPSW |
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LAMP; 600.068 |
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Admin @ si @ WZW2016 |
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2905 |
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Author |
Guim Perarnau; Joost Van de Weijer; Bogdan Raducanu; Jose Manuel Alvarez |
![download PDF file pdf](img/file_PDF.gif)
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Title |
Invertible conditional gans for image editing |
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Conference Article |
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2016 |
Publication |
30th Annual Conference on Neural Information Processing Systems Worshops |
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Generative Adversarial Networks (GANs) have recently demonstrated to successfully approximate complex data distributions. A relevant extension of this model is conditional GANs (cGANs), where the introduction of external information allows to determine specific representations of the generated images. In this work, we evaluate encoders to inverse the mapping of a cGAN, i.e., mapping a real image into a latent space and a conditional representation. This allows, for example, to reconstruct and modify real images of faces conditioning on arbitrary attributes.
Additionally, we evaluate the design of cGANs. The combination of an encoder
with a cGAN, which we call Invertible cGAN (IcGAN), enables to re-generate real
images with deterministic complex modifications. |
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Address ![sorted by Address field, descending order (down)](img/sort_desc.gif) |
Barcelona; Spain; December 2016 |
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LAMP; ADAS; 600.068 |
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Admin @ si @ PWR2016 |
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2906 |
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Author |
Xavier Baro; Sergio Escalera; Isabelle Guyon; Julio C. S. Jacques Junior; Lukasz Romaszko; Lisheng Sun; Sebastien Treguer; Evelyne Viegas |
![find record details (via OpenURL) openurl](img/xref.gif)
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Title |
Coompetitions in machine learning: case studies |
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Conference Article |
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2016 |
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30th Annual Conference on Neural Information Processing Systems Worshops |
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Barcelona; Spain; December 2016 |
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HuPBA |
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no |
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Admin @ si @ BEG2016 |
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2911 |
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Author |
Ivet Rafegas; Maria Vanrell |
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Title |
Colour Visual Coding in trained Deep Neural Networks |
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2016 |
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European Conference on Visual Perception |
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Barcelona; Spain; August 2016 |
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ECVP |
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CIC |
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Admin @ si @ RaV2016b |
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2895 |
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Author |
Arash Akbarinia; C. Alejandro Parraga |
![find record details (via OpenURL) openurl](img/xref.gif)
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Title |
Dynamically Adjusted Surround Contrast Enhances Boundary Detection, European Conference on Visual Perception |
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2016 |
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European Conference on Visual Perception |
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Barcelona; Spain; August 2016 |
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NEUROBIT |
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Admin @ si @ AkP2016b |
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2900 |
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Author |
Fernando Vilariño; Dan Norton; Onur Ferhat |
![find record details (via OpenURL) openurl](img/xref.gif)
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Title |
The Eye Doesn't Click – Eyetracking and Digital Content Interaction |
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2016 |
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4S/EASST Conference |
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Barcelona; Spain; September 2016 |
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EASST |
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MV; 600.097;SIAI |
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Admin @ si @VNF2016 |
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2801 |
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Author |
Alvaro Peris; Marc Bolaños; Petia Radeva; Francisco Casacuberta |
![download PDF file pdf](img/file_PDF.gif)
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Title |
Video Description Using Bidirectional Recurrent Neural Networks |
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Conference Article |
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2016 |
Publication |
25th International Conference on Artificial Neural Networks |
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2 |
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3-11 |
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Video description; Neural Machine Translation; Birectional Recurrent Neural Networks; LSTM; Convolutional Neural Networks |
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Although traditionally used in the machine translation field, the encoder-decoder framework has been recently applied for the generation of video and image descriptions. The combination of Convolutional and Recurrent Neural Networks in these models has proven to outperform the previous state of the art, obtaining more accurate video descriptions. In this work we propose pushing further this model by introducing two contributions into the encoding stage. First, producing richer image representations by combining object and location information from Convolutional Neural Networks and second, introducing Bidirectional Recurrent Neural Networks for capturing both forward and backward temporal relationships in the input frames. |
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Barcelona; September 2016 |
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ICANN |
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MILAB; |
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Admin @ si @ PBR2016 |
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2833 |
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Author |
Petia Radeva |
![find record details (via OpenURL) openurl](img/xref.gif)
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Title |
Can Deep Learning and Egocentric Vision for Visual Lifelogging Help Us Eat Better? |
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Conference Article |
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2016 |
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19th International Conference of the Catalan Association for Artificial Intelligence |
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4 |
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Barcelona; October 2016 |
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MILAB |
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Admin @ si @ Rad2016 |
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2832 |
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Author |
Antoni Gurgui; Debora Gil; Enric Marti; Vicente Grau |
![goto web page (via DOI) doi](img/doi.gif)
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Title |
Left-Ventricle Basal Region Constrained Parametric Mapping to Unitary Domain |
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Conference Article |
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2016 |
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7th International Workshop on Statistical Atlases & Computational Modelling of the Heart |
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10124 |
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163-171 |
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Laplacian; Constrained maps; Parameterization; Basal ring |
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Due to its complex geometry, the basal ring is often omitted when putting different heart geometries into correspondence. In this paper, we present the first results on a new mapping of the left ventricle basal rings onto a normalized coordinate system using a fold-over free approach to the solution to the Laplacian. To guarantee correspondences between different basal rings, we imposed some internal constrained positions at anatomical landmarks in the normalized coordinate system. To prevent internal fold-overs, constraints are handled by cutting the volume into regions defined by anatomical features and mapping each piece of the volume separately. Initial results presented in this paper indicate that our method is able to handle internal constrains without introducing fold-overs and thus guarantees one-to-one mappings between different basal ring geometries. |
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Athens; October 2016 |
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STACOM |
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IAM; |
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Admin @ si @ GGM2016 |
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2884 |
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Author |
Jose Marone; Simone Balocco; Marc Bolaños; Jose Massa; Petia Radeva |
![download PDF file pdf](img/file_PDF.gif)
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Title |
Learning the Lumen Border using a Convolutional Neural Networks classifier |
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2016 |
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19th International Conference on Medical Image Computing and Computer Assisted Intervention Workshop |
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IntraVascular UltraSound (IVUS) is a technique allowing the diagnosis of coronary plaque. An accurate (semi-)automatic assessment of the luminal contours could speed up the diagnosis. In most of the approaches, the information on the vessel shape is obtained combining a supervised learning step with a local refinement algorithm. In this paper, we explore for the first time, the use of a Convolutional Neural Networks (CNN) architecture that on one hand is able to extract the optimal image features and at the same time can serve as a supervised classifier to detect the lumen border in IVUS images. The main limitation of CNN, relies on the fact that this technique requires a large amount of training data due to the huge amount of parameters that it has. To
solve this issue, we introduce a patch classification approach to generate an extended training-set from a few annotated images. An accuracy of 93% and F-score of 71% was obtained with this technique, even when it was applied to challenging frames containig calcified plaques, stents and catheter shadows. |
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Address ![sorted by Address field, descending order (down)](img/sort_desc.gif) |
Athens; Greece; October 2016 |
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MILAB; |
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Admin @ si @ MBB2016 |
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2822 |
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Author |
Antonio Esteban Lansaque; Carles Sanchez; Agnes Borras; Marta Diez-Ferrer; Antoni Rosell; Debora Gil |
![download PDF file pdf](img/file_PDF.gif)
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Stable Anatomical Structure Tracking for video-bronchoscopy Navigation |
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2016 |
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19th International Conference on Medical Image Computing and Computer Assisted Intervention Workshops |
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Lung cancer diagnosis; video-bronchoscopy; airway lumen detection; region tracking |
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Bronchoscopy allows to examine the patient airways for detection of lesions and sampling of tissues without surgery. A main drawback in lung cancer diagnosis is the diculty to check whether the exploration is following the correct path to the nodule that has to be biopsied. The most extended guidance uses uoroscopy which implies repeated radiation of clinical sta and patients. Alternatives such as virtual bronchoscopy or electromagnetic navigation are very expensive and not completely robust to blood, mocus or deformations as to be extensively used. We propose a method that extracts and tracks stable lumen regions at dierent levels of the bronchial tree. The tracked regions are stored in a tree that encodes the anatomical structure of the scene which can be useful to retrieve the path to the lesion that the clinician should follow to do the biopsy. We present a multi-expert validation of our anatomical landmark extraction in 3 intra-operative ultrathin explorations. |
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Athens; Greece; October 2016 |
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IAM; 600.075 |
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Admin @ si @ LSB2016b |
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2857 |
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