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Author |
Debora Gil; Petia Radeva |
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Title  |
Curvature Vector Flow to Assure Convergent Deformable Models for Shape Modelling |
Type |
Book Chapter |
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Year |
2003 |
Publication |
Energy Minimization Methods In Computer Vision And Pattern Recognition |
Abbreviated Journal |
LNCS |
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Volume |
2683 |
Issue |
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Pages |
357-372 |
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Keywords |
Initial condition; Convex shape; Non convex analysis; Increase; Segmentation; Gradient; Standard; Standards; Concave shape; Flow models; Tracking; Edge detection; Curvature |
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Abstract |
Poor convergence to concave shapes is a main limitation of snakes as a standard segmentation and shape modelling technique. The gradient of the external energy of the snake represents a force that pushes the snake into concave regions, as its internal energy increases when new inexion points are created. In spite of the improvement of the external energy by the gradient vector ow technique, highly non convex shapes can not be obtained, yet. In the present paper, we develop a new external energy based on the geometry of the curve to be modelled. By tracking back the deformation of a curve that evolves by minimum curvature ow, we construct a distance map that encapsulates the natural way of adapting to non convex shapes. The gradient of this map, which we call curvature vector ow (CVF), is capable of attracting a snake towards any contour, whatever its geometry. Our experiments show that, any initial snake condition converges to the curve to be modelled in optimal time. |
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Publisher |
Springer, Berlin |
Place of Publication |
Lisbon, PORTUGAL |
Editor |
Springer, B. |
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Series Title |
Lecture Notes in Computer Science |
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LNCS |
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ISSN |
0302-9743 |
ISBN |
3-540-40498-8 |
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Notes |
IAM;MILAB |
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no |
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Call Number |
IAM @ iam @ GIR2003b |
Serial |
1535 |
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Author |
Debora Gil; Petia Radeva |
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Title |
Curvature based Distance Maps |
Type |
Report |
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Year |
2003 |
Publication |
CVC Technical Report |
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70 |
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Publisher |
Computer Vision Center |
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IAM;MILAB |
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no |
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Call Number |
IAM @ iam @ GIR2003a |
Serial |
1534 |
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Author |
Katerine Diaz; Jesus Martinez del Rincon; Aura Hernandez-Sabate; Debora Gil |
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Title |
Continuous head pose estimation using manifold subspace embedding and multivariate regression |
Type |
Journal Article |
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Year |
2018 |
Publication |
IEEE Access |
Abbreviated Journal |
ACCESS |
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Volume |
6 |
Issue |
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Pages |
18325 - 18334 |
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Keywords |
Head Pose estimation; HOG features; Generalized Discriminative Common Vectors; B-splines; Multiple linear regression |
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Abstract |
In this paper, a continuous head pose estimation system is proposed to estimate yaw and pitch head angles from raw facial images. Our approach is based on manifold learningbased methods, due to their promising generalization properties shown for face modelling from images. The method combines histograms of oriented gradients, generalized discriminative common vectors and continuous local regression to achieve successful performance. Our proposal was tested on multiple standard face datasets, as well as in a realistic scenario. Results show a considerable performance improvement and a higher consistence of our model in comparison with other state-of-art methods, with angular errors varying between 9 and 17 degrees. |
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ISSN |
2169-3536 |
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Notes |
ADAS; 600.118 |
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no |
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Call Number |
Admin @ si @ DMH2018b |
Serial |
3091 |
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Author |
Hanne Kause; Aura Hernandez-Sabate; Patricia Marquez; Andrea Fuster; Luc Florack; Hans van Assen; Debora Gil |
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Title |
Confidence Measures for Assessing the HARP Algorithm in Tagged Magnetic Resonance Imaging |
Type |
Book Chapter |
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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 |
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Volume |
9534 |
Issue |
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Pages |
69-79 |
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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. |
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Address |
Munich; Germany; January 2015 |
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Publisher |
Springer International Publishing |
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LNCS |
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ISSN |
0302-9743 |
ISBN |
978-3-319-28711-9 |
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Conference |
STACOM |
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Notes |
ADAS; IAM; 600.075; 600.076; 600.060; 601.145 |
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Call Number |
Admin @ si @ KHM2015 |
Serial |
2734 |
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Author |
Debora Gil; Jordi Gonzalez; Gemma Sanchez (eds) |
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Title |
Computer Vision: Advances in Research and Development |
Type |
Book Whole |
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Year |
2007 |
Publication |
Proceedings of the 2nd CVC International Workshop |
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Publisher |
UAB |
Place of Publication |
Bellaterra (Spain) |
Editor |
Debora Gil; Jordi Gonzalez; Gemma Sanchez |
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2 |
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978-84-935251-4-9 |
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Notes |
IAM; ISE; DAG |
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no |
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Call Number |
IAM @ iam @ GGS2007 |
Serial |
1493 |
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Author |
Sergio Vera; Debora Gil; Agnes Borras; F. Javier Sanchez; Frederic Perez; Marius G. Linguraru; Miguel Angel Gonzalez Ballester |
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Title |
Computation and Evaluation of Medial Surfaces for Shape Representation of Abdominal Organs |
Type |
Book Chapter |
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Year |
2012 |
Publication |
Workshop on Computational and Clinical Applications in Abdominal Imaging |
Abbreviated Journal |
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Volume |
7029 |
Issue |
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Pages |
223–230 |
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Keywords |
medial manifolds, abdomen. |
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Abstract |
Medial representations are powerful tools for describing and parameterizing the volumetric shape of anatomical structures. Existing methods show excellent results when applied to 2D
objects, but their quality drops across dimensions. This paper contributes to the computation of medial manifolds in two aspects. First, we provide a standard scheme for the computation of medial
manifolds that avoid degenerated medial axis segments; second, we introduce an energy based method which performs independently of the dimension. We evaluate quantitatively the performance of our
method with respect to existing approaches, by applying them to synthetic shapes of known medial geometry. Finally, we show results on shape representation of multiple abdominal organs,
exploring the use of medial manifolds for the representation of multi-organ relations. |
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Address |
Toronto; Canada; |
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Publisher |
Springer Link |
Place of Publication |
Berlin |
Editor |
H. Yoshida et al |
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Language |
English |
Summary Language |
English |
Original Title |
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Series Title |
Lecture Notes in Computer Science |
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LNCS |
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ISSN |
0302-9743 |
ISBN |
978-3-642-28556-1 |
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Conference |
ABDI |
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Notes |
IAM;MV |
Approved |
no |
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Call Number |
IAM @ iam @ VGB2012 |
Serial |
1834 |
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Author |
Sergio Vera; Debora Gil; Agnes Borras; F. Javier Sanchez; Frederic Perez; Marius G. Linguraru |
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Title |
Computation and Evaluation of Medial Surfaces for Shape Representation of Abdominal Organs |
Type |
Conference Article |
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Year |
2011 |
Publication |
Workshop on Computational and Clinical Applications in Abdominal Imaging |
Abbreviated Journal |
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Volume |
7029 |
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Pages |
223-230 |
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Abstract |
Medial representations are powerful tools for describing and parameterizing the volumetric shape of anatomical structures. Existing methods show excellent results when applied to 2D objects, but their quality drops across dimensions. This paper contributes to the computation of medial manifolds in two aspects. First, we provide a standard scheme for the computation of medial manifolds that avoid degenerated medial axis segments; second, we introduce an energy based method which performs independently of the dimension. We evaluate quantitatively the performance of our method with respect to existing approaches, by applying them to synthetic shapes of known medial geometry. Finally, we show results on shape representation of multiple abdominal organs, exploring the use of medial manifolds for the representation of multi-organ relations. |
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Nice, France |
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Springer Berlin Heidelberg |
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Editor |
In H. Yoshida et al |
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ABDI |
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Notes |
IAM; MV |
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Call Number |
VGB2011 |
Serial |
2036 |
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Author |
Gloria Fernandez Esparrach; Jorge Bernal; Cristina Rodriguez de Miguel; Debora Gil; Fernando Vilariño; Henry Cordova; Cristina Sanchez Montes; I.Araujo ; Maria Lopez Ceron; J.Llach; F. Javier Sanchez |
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Title |
Colonic polyps are correctly identified by a computer vision method using wm-dova energy maps |
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Conference Article |
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Year |
2015 |
Publication |
Proceedings of 23 United European- UEG Week 2015 |
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UEG |
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Notes |
MV; IAM; 600.075;SIAI |
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Admin @ si @ FBR2015 |
Serial |
2732 |
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Author |
Rosa Maria Ortiz; Debora Gil; Elisa Minchole; Marta Diez-Ferrer; Noelia Cubero de Frutos |
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Title |
Classification of Confolcal Endomicroscopy Patterns for Diagnosis of Lung Cancer |
Type |
Conference Article |
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Year |
2017 |
Publication |
18th World Conference on Lung Cancer |
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Abstract |
Confocal Laser Endomicroscopy (CLE) is an emerging imaging technique that allows the in-vivo acquisition of cell patterns of potentially malignant lesions. Such patterns could discriminate between inflammatory and neoplastic lesions and, thus, serve as a first in-vivo biopsy to discard cases that do not actually require a cell biopsy.
The goal of this work is to explore whether CLE images obtained during videobronchoscopy contain enough visual information to discriminate between benign and malign peripheral lesions for lung cancer diagnosis. To do so, we have performed a pilot comparative study with 12 patients (6 adenocarcinoma and 6 benign-inflammatory) using 2 different methods for CLE pattern analysis: visual analysis by 3 experts and a novel methodology that uses graph methods to find patterns in pre-trained feature spaces. Our preliminary results indicate that although visual analysis can only achieve a 60.2% of accuracy, the accuracy of the proposed unsupervised image pattern classification raises to 84.6%.
We conclude that CLE images visual information allow in-vivo detection of neoplastic lesions and graph structural analysis applied to deep-learning feature spaces can achieve competitive results. |
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Yokohama; Japan; October 2017 |
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IASLC WCLC |
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IAM; 600.096; 600.075; 600.145 |
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Call Number |
Admin @ si @ OGM2017 |
Serial |
3044 |
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Author |
Debora Gil; Oriol Ramos Terrades; Elisa Minchole; Carles Sanchez; Noelia Cubero de Frutos; Marta Diez-Ferrer; Rosa Maria Ortiz; Antoni Rosell |
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Title |
Classification of Confocal Endomicroscopy Patterns for Diagnosis of Lung Cancer |
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Conference Article |
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2017 |
Publication |
6th Workshop on Clinical Image-based Procedures: Translational Research in Medical Imaging |
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10550 |
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151-159 |
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Abstract |
Confocal Laser Endomicroscopy (CLE) is an emerging imaging technique that allows the in-vivo acquisition of cell patterns of potentially malignant lesions. Such patterns could discriminate between inflammatory and neoplastic lesions and, thus, serve as a first in-vivo biopsy to discard cases that do not actually require a cell biopsy.
The goal of this work is to explore whether CLE images obtained during videobronchoscopy contain enough visual information to discriminate between benign and malign peripheral lesions for lung cancer diagnosis. To do so, we have performed a pilot comparative study with 12 patients (6 adenocarcinoma and 6 benign-inflammatory) using 2 different methods for CLE pattern analysis: visual analysis by 3 experts and a novel methodology that uses graph methods to find patterns in pre-trained feature spaces. Our preliminary results indicate that although visual analysis can only achieve a 60.2% of accuracy, the accuracy of the proposed unsupervised image pattern classification raises to 84.6%.
We conclude that CLE images visual information allow in-vivo detection of neoplastic lesions and graph structural analysis applied to deep-learning feature spaces can achieve competitive results. |
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Quebec; Canada; September 2017 |
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LNCS |
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CLIP |
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IAM; 600.096; 600.075; 600.145 |
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Call Number |
Admin @ si @ GRM2017 |
Serial |
2957 |
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