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Author Carles Sanchez; Jorge Bernal; F. Javier Sanchez; Antoni Rosell; Marta Diez-Ferrer; Debora Gil
Title Towards On-line Quantification of Tracheal Stenosis from Videobronchoscopy Type Journal Article
Year 2015 Publication (up) International Journal of Computer Assisted Radiology and Surgery Abbreviated Journal IJCAR
Volume 10 Issue 6 Pages 935-945
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Abstract
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Notes IAM; MV; 600.075 Approved no
Call Number Admin @ si @ SBS2015a Serial 2611
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Author Debora Gil; Ruth Aris; Agnes Borras; Esmitt Ramirez; Rafael Sebastian; Mariano Vazquez
Title Influence of fiber connectivity in simulations of cardiac biomechanics Type Journal Article
Year 2019 Publication (up) International Journal of Computer Assisted Radiology and Surgery Abbreviated Journal IJCAR
Volume 14 Issue 1 Pages 63–72
Keywords Cardiac electromechanical simulations; Diffusion tensor imaging; Fiber connectivity
Abstract PURPOSE:
Personalized computational simulations of the heart could open up new improved approaches to diagnosis and surgery assistance systems. While it is fully recognized that myocardial fiber orientation is central for the construction of realistic computational models of cardiac electromechanics, the role of its overall architecture and connectivity remains unclear. Morphological studies show that the distribution of cardiac muscular fibers at the basal ring connects epicardium and endocardium. However, computational models simplify their distribution and disregard the basal loop. This work explores the influence in computational simulations of fiber distribution at different short-axis cuts.

METHODS:
We have used a highly parallelized computational solver to test different fiber models of ventricular muscular connectivity. We have considered two rule-based mathematical models and an own-designed method preserving basal connectivity as observed in experimental data. Simulated cardiac functional scores (rotation, torsion and longitudinal shortening) were compared to experimental healthy ranges using generalized models (rotation) and Mahalanobis distances (shortening, torsion).

RESULTS:
The probability of rotation was significantly lower for ruled-based models [95% CI (0.13, 0.20)] in comparison with experimental data [95% CI (0.23, 0.31)]. The Mahalanobis distance for experimental data was in the edge of the region enclosing 99% of the healthy population.

CONCLUSIONS:
Cardiac electromechanical simulations of the heart with fibers extracted from experimental data produce functional scores closer to healthy ranges than rule-based models disregarding architecture connectivity.
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Notes IAM; 600.096; 601.323; 600.139; 600.145 Approved no
Call Number Admin @ si @ GAB2019a Serial 3133
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Author Jorge Bernal; Aymeric Histace; Marc Masana; Quentin Angermann; Cristina Sanchez Montes; Cristina Rodriguez de Miguel; Maroua Hammami; Ana Garcia Rodriguez; Henry Cordova; Olivier Romain; Gloria Fernandez Esparrach; Xavier Dray; F. Javier Sanchez
Title GTCreator: a flexible annotation tool for image-based datasets Type Journal Article
Year 2019 Publication (up) International Journal of Computer Assisted Radiology and Surgery Abbreviated Journal IJCAR
Volume 14 Issue 2 Pages 191–201
Keywords Annotation tool; Validation Framework; Benchmark; Colonoscopy; Evaluation
Abstract Abstract Purpose: Methodology evaluation for decision support systems for health is a time consuming-task. To assess performance of polyp detection
methods in colonoscopy videos, clinicians have to deal with the annotation
of thousands of images. Current existing tools could be improved in terms of
exibility and ease of use. Methods:We introduce GTCreator, a exible annotation tool for providing image and text annotations to image-based datasets.
It keeps the main basic functionalities of other similar tools while extending
other capabilities such as allowing multiple annotators to work simultaneously
on the same task or enhanced dataset browsing and easy annotation transfer aiming to speed up annotation processes in large datasets. Results: The
comparison with other similar tools shows that GTCreator allows to obtain
fast and precise annotation of image datasets, being the only one which offers
full annotation editing and browsing capabilites. Conclusions: Our proposed
annotation tool has been proven to be efficient for large image dataset annota-
tion, as well as showing potential of use in other stages of method evaluation
such as experimental setup or results analysis.
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Publisher Place of Publication Editor
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Series Editor Series Title Abbreviated Series Title
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ISSN ISBN Medium
Area Expedition Conference
Notes MV; 600.096; 600.109; 600.119; 601.305 Approved no
Call Number Admin @ si @ BHM2019 Serial 3163
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Author Debora Gil; Antonio Esteban Lansaque; Agnes Borras; Carles Sanchez
Title Enhancing virtual bronchoscopy with intra-operative data using a multi-objective GAN Type Journal Article
Year 2019 Publication (up) International Journal of Computer Assisted Radiology and Surgery Abbreviated Journal IJCAR
Volume 7 Issue 1 Pages
Keywords
Abstract This manuscript has been withdrawn by bioRxiv due to upload of an incorrect version of the manuscript by the authors. Therefore, this manuscript should not be cited as reference for this project.
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Publisher Place of Publication Editor
Language Summary Language Original Title
Series Editor Series Title Abbreviated Series Title
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Area Expedition Conference
Notes IAM; 600.139; 600.145 Approved no
Call Number Admin @ si @ GEB2019 Serial 3307
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Author Guillermo Torres; Debora Gil
Title A multi-shape loss function with adaptive class balancing for the segmentation of lung structures Type Journal Article
Year 2020 Publication (up) International Journal of Computer Assisted Radiology and Surgery Abbreviated Journal IJCAR
Volume 15 Issue 1 Pages S154-55
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Abstract
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Publisher Place of Publication Editor
Language Summary Language Original Title
Series Editor Series Title Abbreviated Series Title
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Notes IAM Approved no
Call Number Admin @ si @ ToG2020 Serial 3590
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Author Guillermo Torres; Debora Gil; Antoni Rosell; S. Mena; Carles Sanchez
Title Virtual Radiomics Biopsy for the Histological Diagnosis of Pulmonary Nodules – Intermediate Results of the RadioLung Project Type Journal Article
Year 2023 Publication (up) International Journal of Computer Assisted Radiology and Surgery Abbreviated Journal IJCARS
Volume Issue Pages
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Abstract
Address
Corporate Author Thesis
Publisher Place of Publication Editor
Language Summary Language Original Title
Series Editor Series Title Abbreviated Series Title
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Area Expedition Conference
Notes IAM Approved no
Call Number Admin @ si @ TGM2023 Serial 3830
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Author Juan Borrego-Carazo; Carles Sanchez; David Castells; Jordi Carrabina; Debora Gil
Title A benchmark for the evaluation of computational methods for bronchoscopic navigation Type Journal Article
Year 2022 Publication (up) International Journal of Computer Assisted Radiology and Surgery Abbreviated Journal IJCARS
Volume 17 Issue 1 Pages
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Abstract
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Publisher Place of Publication Editor
Language Summary Language Original Title
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Notes IAM Approved no
Call Number Admin @ si @ BSC2022 Serial 3832
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Author Arjan Gijsenij; Theo Gevers; Joost Van de Weijer
Title Generalized Gamut Mapping using Image Derivative Structures for Color Constancy Type Journal Article
Year 2010 Publication (up) International Journal of Computer Vision Abbreviated Journal IJCV
Volume 86 Issue 2-3 Pages 127-139
Keywords
Abstract The gamut mapping algorithm is one of the most promising methods to achieve computational color constancy. However, so far, gamut mapping algorithms are restricted to the use of pixel values to estimate the illuminant. Therefore, in this paper, gamut mapping is extended to incorporate the statistical nature of images. It is analytically shown that the proposed gamut mapping framework is able to include any linear filter output. The main focus is on the local n-jet describing the derivative structure of an image. It is shown that derivatives have the advantage over pixel values to be invariant to disturbing effects (i.e. deviations of the diagonal model) such as saturated colors and diffuse light. Further, as the n-jet based gamut mapping has the ability to use more information than pixel values alone, the combination of these algorithms are more stable than the regular gamut mapping algorithm. Different methods of combining are proposed. Based on theoretical and experimental results conducted on large scale data sets of hyperspectral, laboratory and realworld scenes, it can be derived that (1) in case of deviations of the diagonal model, the derivative-based approach outperforms the pixel-based gamut mapping, (2) state-of-the-art algorithms are outperformed by the n-jet based gamut mapping, (3) the combination of the different n-jet based gamut
Address
Corporate Author Thesis
Publisher Kluwer Academic Publishers Hingham, MA, USA Place of Publication Editor
Language Summary Language Original Title
Series Editor Series Title Abbreviated Series Title
Series Volume Series Issue Edition
ISSN 0920-5691 ISBN Medium
Area Expedition Conference
Notes ISE Approved no
Call Number CAT @ cat @ GGW2010 Serial 1274
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Author Jose Manuel Alvarez; Theo Gevers; Antonio Lopez
Title Learning photometric invariance for object detection Type Journal Article
Year 2010 Publication (up) International Journal of Computer Vision Abbreviated Journal IJCV
Volume 90 Issue 1 Pages 45-61
Keywords road detection
Abstract Impact factor: 3.508 (the last available from JCR2009SCI). Position 4/103 in the category Computer Science, Artificial Intelligence. Quartile
Color is a powerful visual cue in many computer vision applications such as image segmentation and object recognition. However, most of the existing color models depend on the imaging conditions that negatively affect the performance of the task at hand. Often, a reflection model (e.g., Lambertian or dichromatic reflectance) is used to derive color invariant models. However, this approach may be too restricted to model real-world scenes in which different reflectance mechanisms can hold simultaneously.
Therefore, in this paper, we aim to derive color invariance by learning from color models to obtain diversified color invariant ensembles. First, a photometrical orthogonal and non-redundant color model set is computed composed of both color variants and invariants. Then, the proposed method combines these color models to arrive at a diversified color ensemble yielding a proper balance between invariance (repeatability) and discriminative power (distinctiveness). To achieve this, our fusion method uses a multi-view approach to minimize the estimation error. In this way, the proposed method is robust to data uncertainty and produces properly diversified color invariant ensembles. Further, the proposed method is extended to deal with temporal data by predicting the evolution of observations over time.
Experiments are conducted on three different image datasets to validate the proposed method. Both the theoretical and experimental results show that the method is robust against severe variations in imaging conditions. The method is not restricted to a certain reflection model or parameter tuning, and outperforms state-of-the-art detection techniques in the field of object, skin and road recognition. Considering sequential data, the proposed method (extended to deal with future observations) outperforms the other methods
Address
Corporate Author Thesis
Publisher Springer US Place of Publication Editor
Language Summary Language Original Title
Series Editor Series Title Abbreviated Series Title
Series Volume Series Issue Edition
ISSN 0920-5691 ISBN Medium
Area Expedition Conference
Notes ADAS;ISE Approved no
Call Number ADAS @ adas @ AGL2010c Serial 1451
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Author Jasper Uilings; Koen E.A. van de Sande; Theo Gevers; Arnold Smeulders
Title Selective Search for Object Recognition Type Journal Article
Year 2013 Publication (up) International Journal of Computer Vision Abbreviated Journal IJCV
Volume 104 Issue 2 Pages 154-171
Keywords
Abstract This paper addresses the problem of generating possible object locations for use in object recognition. We introduce selective search which combines the strength of both an exhaustive search and segmentation. Like segmentation, we use the image structure to guide our sampling process. Like exhaustive search, we aim to capture all possible object locations. Instead of a single technique to generate possible object locations, we diversify our search and use a variety of complementary image partitionings to deal with as many image conditions as possible. Our selective search results in a small set of data-driven, class-independent, high quality locations, yielding 99 % recall and a Mean Average Best Overlap of 0.879 at 10,097 locations. The reduced number of locations compared to an exhaustive search enables the use of stronger machine learning techniques and stronger appearance models for object recognition. In this paper we show that our selective search enables the use of the powerful Bag-of-Words model for recognition. The selective search software is made publicly available (Software: http://disi.unitn.it/~uijlings/SelectiveSearch.html).
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Publisher Place of Publication Editor
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ISSN 0920-5691 ISBN Medium
Area Expedition Conference
Notes ALTRES;ISE Approved no
Call Number Admin @ si @ USG2013 Serial 2362
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Author Xavier Boix; Josep M. Gonfaus; Joost Van de Weijer; Andrew Bagdanov; Joan Serrat; Jordi Gonzalez
Title Harmony Potentials: Fusing Global and Local Scale for Semantic Image Segmentation Type Journal Article
Year 2012 Publication (up) International Journal of Computer Vision Abbreviated Journal IJCV
Volume 96 Issue 1 Pages 83-102
Keywords
Abstract The Hierarchical Conditional Random Field(HCRF) model have been successfully applied to a number of image labeling problems, including image segmentation. However, existing HCRF models of image segmentation do not allow multiple classes to be assigned to a single region, which limits their ability to incorporate contextual information across multiple scales.
At higher scales in the image, this representation yields an oversimpli ed model since multiple classes can be reasonably expected to appear within large regions. This simpli ed model particularly limits the impact of information at higher scales. Since class-label information at these scales is usually more reliable than at lower, noisier scales, neglecting this information is undesirable. To
address these issues, we propose a new consistency potential for image labeling problems, which we call the harmony potential. It can encode any possible combi-
nation of labels, penalizing only unlikely combinations of classes. We also propose an e ective sampling strategy over this expanded label set that renders tractable the underlying optimization problem. Our approach obtains state-of-the-art results on two challenging, standard benchmark datasets for semantic image segmentation: PASCAL VOC 2010, and MSRC-21.
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Publisher Place of Publication Editor
Language Summary Language Original Title
Series Editor Series Title Abbreviated Series Title
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ISSN 0920-5691 ISBN Medium
Area Expedition Conference
Notes ISE;CIC;ADAS Approved no
Call Number Admin @ si @ BGW2012 Serial 1718
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Author R. Valenti; N. Sebe; Theo Gevers
Title What are you looking at? Improving Visual gaze Estimation by Saliency Type Journal Article
Year 2012 Publication (up) International Journal of Computer Vision Abbreviated Journal IJCV
Volume 98 Issue 3 Pages 324-334
Keywords
Abstract Impact factor 2010: 5.15
Impact factor 2011/12?: 5.36
In this paper we present a novel mechanism to obtain enhanced gaze estimation for subjects looking at a scene or an image. The system makes use of prior knowledge about the scene (e.g. an image on a computer screen), to define a probability map of the scene the subject is gazing at, in order to find the most probable location. The proposed system helps in correcting the fixations which are erroneously estimated by the gaze estimation device by employing a saliency framework to adjust the resulting gaze point vector. The system is tested on three scenarios: using eye tracking data, enhancing a low accuracy webcam based eye tracker, and using a head pose tracker. The correlation between the subjects in the commercial eye tracking data is improved by an average of 13.91%. The correlation on the low accuracy eye gaze tracker is improved by 59.85%, and for the head pose tracker we obtain an improvement of 10.23%. These results show the potential of the system as a way to enhance and self-calibrate different visual gaze estimation systems.
Address
Corporate Author Thesis
Publisher Place of Publication Editor
Language Summary Language Original Title
Series Editor Series Title Abbreviated Series Title
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ISSN 0920-5691 ISBN Medium
Area Expedition Conference
Notes ALTRES;ISE Approved no
Call Number Admin @ si @ VSG2012 Serial 1848
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Author Fahad Shahbaz Khan; Joost Van de Weijer; Maria Vanrell
Title Modulating Shape Features by Color Attention for Object Recognition Type Journal Article
Year 2012 Publication (up) International Journal of Computer Vision Abbreviated Journal IJCV
Volume 98 Issue 1 Pages 49-64
Keywords
Abstract Bag-of-words based image representation is a successful approach for object recognition. Generally, the subsequent stages of the process: feature detection,feature description, vocabulary construction and image representation are performed independent of the intentioned object classes to be detected. In such a framework, it was found that the combination of different image cues, such as shape and color, often obtains below expected results. This paper presents a novel method for recognizing object categories when using ultiple cues by separately processing the shape and color cues and combining them by modulating the shape features by category specific color attention. Color is used to compute bottom up and top-down attention maps. Subsequently, these color attention maps are used to modulate the weights of the shape features. In regions with higher attention shape features are given more weight than in regions with low attention. We compare our approach with existing methods that combine color and shape cues on five data sets containing varied importance of both cues, namely, Soccer (color predominance), Flower (color and hape parity), PASCAL VOC 2007 and 2009 (shape predominance) and Caltech-101 (color co-interference). The experiments clearly demonstrate that in all five data sets our proposed framework significantly outperforms existing methods for combining color and shape information.
Address
Corporate Author Thesis
Publisher Springer Netherlands Place of Publication Editor
Language Summary Language Original Title
Series Editor Series Title Abbreviated Series Title
Series Volume Series Issue Edition
ISSN 0920-5691 ISBN Medium
Area Expedition Conference
Notes CIC Approved no
Call Number Admin @ si @ KWV2012 Serial 1864
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Author Fahad Shahbaz Khan; Muhammad Anwer Rao; Joost Van de Weijer; Andrew Bagdanov; Antonio Lopez; Michael Felsberg
Title Coloring Action Recognition in Still Images Type Journal Article
Year 2013 Publication (up) International Journal of Computer Vision Abbreviated Journal IJCV
Volume 105 Issue 3 Pages 205-221
Keywords
Abstract In this article we investigate the problem of human action recognition in static images. By action recognition we intend a class of problems which includes both action classification and action detection (i.e. simultaneous localization and classification). Bag-of-words image representations yield promising results for action classification, and deformable part models perform very well object detection. The representations for action recognition typically use only shape cues and ignore color information. Inspired by the recent success of color in image classification and object detection, we investigate the potential of color for action classification and detection in static images. We perform a comprehensive evaluation of color descriptors and fusion approaches for action recognition. Experiments were conducted on the three datasets most used for benchmarking action recognition in still images: Willow, PASCAL VOC 2010 and Stanford-40. Our experiments demonstrate that incorporating color information considerably improves recognition performance, and that a descriptor based on color names outperforms pure color descriptors. Our experiments demonstrate that late fusion of color and shape information outperforms other approaches on action recognition. Finally, we show that the different color–shape fusion approaches result in complementary information and combining them yields state-of-the-art performance for action classification.
Address
Corporate Author Thesis
Publisher Springer US Place of Publication Editor
Language Summary Language Original Title
Series Editor Series Title Abbreviated Series Title
Series Volume Series Issue Edition
ISSN 0920-5691 ISBN Medium
Area Expedition Conference
Notes CIC; ADAS; 600.057; 600.048 Approved no
Call Number Admin @ si @ KRW2013 Serial 2285
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Author Jiaolong Xu; Sebastian Ramos; David Vazquez; Antonio Lopez
Title Hierarchical Adaptive Structural SVM for Domain Adaptation Type Journal Article
Year 2016 Publication (up) International Journal of Computer Vision Abbreviated Journal IJCV
Volume 119 Issue 2 Pages 159-178
Keywords Domain Adaptation; Pedestrian Detection
Abstract A key topic in classification is the accuracy loss produced when the data distribution in the training (source) domain differs from that in the testing (target) domain. This is being recognized as a very relevant problem for many
computer vision tasks such as image classification, object detection, and object category recognition. In this paper, we present a novel domain adaptation method that leverages multiple target domains (or sub-domains) in a hierarchical adaptation tree. The core idea is to exploit the commonalities and differences of the jointly considered target domains.
Given the relevance of structural SVM (SSVM) classifiers, we apply our idea to the adaptive SSVM (A-SSVM), which only requires the target domain samples together with the existing source-domain classifier for performing the desired adaptation. Altogether, we term our proposal as hierarchical A-SSVM (HA-SSVM).
As proof of concept we use HA-SSVM for pedestrian detection, object category recognition and face recognition. In the former we apply HA-SSVM to the deformable partbased model (DPM) while in the rest HA-SSVM is applied to multi-category classifiers. We will show how HA-SSVM is effective in increasing the detection/recognition accuracy with respect to adaptation strategies that ignore the structure of the target data. Since, the sub-domains of the target data are not always known a priori, we shown how HA-SSVM can incorporate sub-domain discovery for object category recognition.
Address
Corporate Author Thesis
Publisher Springer US Place of Publication Editor
Language Summary Language Original Title
Series Editor Series Title Abbreviated Series Title
Series Volume Series Issue Edition
ISSN 0920-5691 ISBN Medium
Area Expedition Conference
Notes ADAS; 600.085; 600.082; 600.076 Approved no
Call Number Admin @ si @ XRV2016 Serial 2669
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