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Patricia Marquez, Debora Gil, R.Mester, & Aura Hernandez-Sabate. (2014). "Local Analysis of Confidence Measures for Optical Flow Quality Evaluation " In 9th International Conference on Computer Vision Theory and Applications (Vol. 3, pp. 450–457).
Abstract: Optical Flow (OF) techniques facing the complexity of real sequences have been developed in the last years. Even using the most appropriate technique for our specific problem, at some points the output flow might fail to achieve the minimum error required for the system. Confidence measures computed from either input data or OF output should discard those points where OF is not accurate enough for its further use. It follows that evaluating the capabilities of a confidence measure for bounding OF error is as important as the definition
itself. In this paper we analyze different confidence measures and point out their advantages and limitations for their use in real world settings. We also explore the agreement with current tools for their evaluation of confidence measures performance.
Keywords: Optical Flow; Confidence Measure; Performance Evaluation.
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Enric Marti, Jaume Rocarias, Ricardo Toledo, & Aura Hernandez-Sabate. (2009). "Caronte: plataforma Moodle con gestion flexible de grupos. Primeras experiencias en asignaturas de Ingenieria Informatica ".
Abstract: En este artículo se presenta Caronte, entorno LMS (Learning Management System) basado en Moodle. Una característica importante del entorno es la gestión flexible de grupos en una asignatura. Entendemos por grupo un conjunto de alumnos que realizan un trabajo y uno de ellos entrega la actividad propuesta (práctica, encuesta, etc.) en representación del grupo. Hemos trabajado en la confección de estos grupos, implementando un sistema de inscripción por contraseña.
Caronte ofrece un conjunto de actividades basadas en este concepto de grupo: encuestas, tareas (entrega de trabajos o prácticas), encuestas de autoevaluación y cuestionarios, entre otras.
Basada en nuestra actividad de encuesta, hemos definido una actividad de Control, que permite un cierto feedback electrónico del profesor sobre la actividad de los alumnos.
Finalmente, se presenta un resumen de las experiencias de uso de Caronte sobre asignaturas de Ingeniería Informática en el curso 2007-08.
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Katerine Diaz, Jesus Martinez del Rincon, Aura Hernandez-Sabate, Marçal Rusiñol, & Francesc J. Ferri. (2018). "Fast Kernel Generalized Discriminative Common Vectors for Feature Extraction " . Journal of Mathematical Imaging and Vision, 60(4), 512–524.
Abstract: This paper presents a supervised subspace learning method called Kernel Generalized Discriminative Common Vectors (KGDCV), as a novel extension of the known Discriminative Common Vectors method with Kernels. Our method combines the advantages of kernel methods to model complex data and solve nonlinear
problems with moderate computational complexity, with the better generalization properties of generalized approaches for large dimensional data. These attractive combination makes KGDCV specially suited for feature extraction and classification in computer vision, image processing and pattern recognition applications. Two different approaches to this generalization are proposed, a first one based on the kernel trick (KT) and a second one based on the nonlinear projection trick (NPT) for even higher efficiency. Both methodologies
have been validated on four different image datasets containing faces, objects and handwritten digits, and compared against well known non-linear state-of-art methods. Results show better discriminant properties than other generalized approaches both linear or kernel. In addition, the KGDCV-NPT approach presents a considerable computational gain, without compromising the accuracy of the model.
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Aura Hernandez-Sabate, Debora Gil, David Roche, Monica M. S. Matsumoto, & Sergio S. Furuie. (2011). "Inferring the Performance of Medical Imaging Algorithms " In Pedro Real, Daniel Diaz-Pernil, Helena Molina-Abril, Ainhoa Berciano, & Walter Kropatsch (Eds.), 14th International Conference on Computer Analysis of Images and Patterns (Vol. 6854, pp. 520–528). L. Berlin: Springer-Verlag Berlin Heidelberg.
Abstract: Evaluation of the performance and limitations of medical imaging algorithms is essential to estimate their impact in social, economic or clinical aspects. However, validation of medical imaging techniques is a challenging task due to the variety of imaging and clinical problems involved, as well as, the difficulties for systematically extracting a reliable solely ground truth. Although specific validation protocols are reported in any medical imaging paper, there are still two major concerns: definition of standardized methodologies transversal to all problems and generalization of conclusions to the whole clinical data set.
We claim that both issues would be fully solved if we had a statistical model relating ground truth and the output of computational imaging techniques. Such a statistical model could conclude to what extent the algorithm behaves like the ground truth from the analysis of a sampling of the validation data set. We present a statistical inference framework reporting the agreement and describing the relationship of two quantities. We show its transversality by applying it to validation of two different tasks: contour segmentation and landmark correspondence.
Keywords: Validation, Statistical Inference, Medical Imaging Algorithms.
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Patricia Marquez, Debora Gil, & Aura Hernandez-Sabate. (2013). "Evaluation of the Capabilities of Confidence Measures for Assessing Optical Flow Quality " In ICCV Workshop on Computer Vision in Vehicle Technology: From Earth to Mars (pp. 624–631).
Abstract: Assessing Optical Flow (OF) quality is essential for its further use in reliable decision support systems. The absence of ground truth in such situations leads to the computation of OF Confidence Measures (CM) obtained from either input or output data. A fair comparison across the capabilities of the different CM for bounding OF error is required in order to choose the best OF-CM pair for discarding points where OF computation is not reliable. This paper presents a statistical probabilistic framework for assessing the quality of a given CM. Our quality measure is given in terms of the percentage of pixels whose OF error bound can not be determined by CM values. We also provide statistical tools for the computation of CM values that ensures a given accuracy of the flow field.
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Aura Hernandez-Sabate, Debora Gil, J. Mauri, & Petia Radeva. (2006). "Reducing cardiac motion in IVUS sequences " In Proceeding of Computers in Cardiology (Vol. 33, pp. 685–688).
Abstract: Cardiac vessel displacement is a main artifact in IVUS sequences. It hinders visualization of the main structures in an appropriate orientation and alignment and affects extracting vessel measurements. In this paper, we present a novel approach for image sequence alignment based on spectral analysis, which removes rigid dynamics, preserving at the same time the vessel geometry. First, we suppress the translation by taking, for each frame, the center of mass of the image as origin of coordinates. In polar coordinates with such point as origin, the rotation appears as a horizontal displacement. The translation induces a phase shift in the Fourier coefficients of two consecutive polar images. We estimate the phase by adjusting a regression plane to the phases of the principal frequencies. Experiments show that the presented strategy suppress cardiac motion regardless of the acquisition device. 1.
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Jaume Garcia, Debora Gil, Luis Badiella, Aura Hernandez-Sabate, Francesc Carreras, Sandra Pujades, et al. (2010). "A Normalized Framework for the Design of Feature Spaces Assessing the Left Ventricular Function " . IEEE Transactions on Medical Imaging, 29(3), 733–745.
Abstract: A through description of the left ventricle functionality requires combining complementary regional scores. A main limitation is the lack of multiparametric normality models oriented to the assessment of regional wall motion abnormalities (RWMA). This paper covers two main topics involved in RWMA assessment. We propose a general framework allowing the fusion and comparison across subjects of different regional scores. Our framework is used to explore which combination of regional scores (including 2-D motion and strains) is better suited for RWMA detection. Our statistical analysis indicates that for a proper (within interobserver variability) identification of RWMA, models should consider motion and extreme strains.
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Debora Gil, Aura Hernandez-Sabate, Oriol Rodriguez, J. Mauri, & Petia Radeva. (2006). "Statistical Strategy for Anisotropic Adventitia Modelling in IVUS " . IEEE Transactions on Medical Imaging, 25(6), 768–778.
Abstract: Vessel plaque assessment by analysis of intravascular ultrasound sequences is a useful tool for cardiac disease diagnosis and intervention. Manual detection of luminal (inner) and mediaadventitia (external) vessel borders is the main activity of physicians in the process of lumen narrowing (plaque) quantification. Difficult definition of vessel border descriptors, as well as, shades, artifacts, and blurred signal response due to ultrasound physical properties trouble automated adventitia segmentation. In order to efficiently approach such a complex problem, we propose blending advanced anisotropic filtering operators and statistical classification techniques into a vessel border modelling strategy. Our systematic statistical analysis shows that the reported adventitia detection achieves an accuracy in the range of interobserver variability regardless of plaque nature, vessel geometry, and incomplete vessel borders. Index Terms–-Anisotropic processing, intravascular ultrasound (IVUS), vessel border segmentation, vessel structure classification.
Keywords: Corners; T-junctions; Wavelets
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Saad Minhas, Aura Hernandez-Sabate, Shoaib Ehsan, Katerine Diaz, Ales Leonardis, Antonio Lopez, et al. (2016). "LEE: A photorealistic Virtual Environment for Assessing Driver-Vehicle Interactions in Self-Driving Mode " In 14th European Conference on Computer Vision Workshops (Vol. 9915, pp. 894–900).
Abstract: Photorealistic virtual environments are crucial for developing and testing automated driving systems in a safe way during trials. As commercially available simulators are expensive and bulky, this paper presents a low-cost, extendable, and easy-to-use (LEE) virtual environment with the aim to highlight its utility for level 3 driving automation. In particular, an experiment is performed using the presented simulator to explore the influence of different variables regarding control transfer of the car after the system was driving autonomously in a highway scenario. The results show that the speed of the car at the time when the system needs to transfer the control to the human driver is critical.
Keywords: Simulation environment; Automated Driving; Driver-Vehicle interaction
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Aura Hernandez-Sabate, Monica Mitiko, Sergio Shiguemi, & Debora Gil. (2010). "A validation protocol for assessing cardiac phase retrieval in IntraVascular UltraSound " In Computing in Cardiology (Vol. 37, pp. 899–902). IEEE.
Abstract: A good reliable approach to cardiac triggering is of utmost importance in obtaining accurate quantitative results of atherosclerotic plaque burden from the analysis of IntraVascular UltraSound. Although, in the last years, there has been an increase in research of methods for retrospective gating, there is no general consensus in a validation protocol. Many methods are based on quality assessment of longitudinal cuts appearance and those reporting quantitative numbers do not follow a standard protocol. Such heterogeneity in validation protocols makes faithful comparison across methods a difficult task. We propose a validation protocol based on the variability of the retrieved cardiac phase and explore the capability of several quality measures for quantifying such variability. An ideal detector, suitable for its application in clinical practice, should produce stable phases. That is, it should always sample the same cardiac cycle fraction. In this context, one should measure the variability (variance) of a candidate sampling with respect a ground truth (reference) sampling, since the variance would indicate how spread we are aiming a target. In order to quantify the deviation between the sampling and the ground truth, we have considered two quality scores reported in the literature: signed distance to the closest reference sample and distance to the right of each reference sample. We have also considered the residuals of the regression line of reference against candidate sampling. The performance of the measures has been explored on a set of synthetic samplings covering different cardiac cycle fractions and variabilities. From our simulations, we conclude that the metrics related to distances are sensitive to the shift considered while the residuals are robust against fraction and variabilities as far as one can establish a pair-wise correspondence between candidate and reference. We will further investigate the impact of false positive and negative detections in experimental data.
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