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Patricia Marquez, Debora Gil, & Aura Hernandez-Sabate. (2011). "A Confidence Measure for Assessing Optical Flow Accuracy in the Absence of Ground Truth " In IEEE International Conference on Computer Vision – Workshops (pp. 2042–2049). Barcelona (Spain): IEEE.
Abstract: Optical flow is a valuable tool for motion analysis in autonomous navigation systems. A reliable application requires determining the accuracy of the computed optical flow. This is a main challenge given the absence of ground truth in real world sequences. This paper introduces a measure of optical flow accuracy for Lucas-Kanade based flows in terms of the numerical stability of the data-term. We call this measure optical flow condition number. A statistical analysis over ground-truth data show a good statistical correlation between the condition number and optical flow error. Experiments on driving sequences illustrate its potential for autonomous navigation systems.
Keywords: IEEE International Conference on Computer Vision – Workshops
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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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Patricia Marquez, Debora Gil, & Aura Hernandez-Sabate. (2012). "Error Analysis for Lucas-Kanade Based Schemes " In 9th International Conference on Image Analysis and Recognition (Vol. 7324, pp. 184–191). Lecture Notes in Computer Science. Springer-Verlag Berlin Heidelberg.
Abstract: Optical flow is a valuable tool for motion analysis in medical imaging sequences. A reliable application requires determining the accuracy of the computed optical flow. This is a main challenge given the absence of ground truth in medical sequences. This paper presents an error analysis of Lucas-Kanade schemes in terms of intrinsic design errors and numerical stability of the algorithm. Our analysis provides a confidence measure that is naturally correlated to the accuracy of the flow field. Our experiments show the higher predictive value of our confidence measure compared to existing measures.
Keywords: Optical flow, Confidence measure, Lucas-Kanade, Cardiac Magnetic Resonance
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Oriol Rodriguez-Leor, J. Mauri, Eduard Fernandez-Nofrerias, Antonio Tovar, Vicente del Valle, Aura Hernandez-Sabate, et al. (2004)." Utilizacion de la estructura de los campos vectoriales para la deteccion de la Adventicia en imagenes de Ecografia Intracoronaria" . Revista Española de Cardiología, 57(2), 100.
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Oriol Rodriguez-Leor, J. Mauri, Eduard Fernandez-Nofrerias, Antonio Tovar, Vicente del Valle, Aura Hernandez-Sabate, et al. (2004)." Utilización de la Estructura de los Campos Vectoriales para la Detección de la Adventicia en Imágenes de Ecografía Intracoronaria" . Revista Internacional de Enfermedades Cardiovasculares Revista Española de Cardiología, 57(2), 100.
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Oriol Rodriguez-Leor, A. Carol, H. Tizon, Eduard Fernandez-Nofrerias, J. Mauri, Vicente del Valle, et al. (2005)." Model estadístic-determinístic per la segmentació de l adventicia en imatges d ecografía intracoronaria" . Rev Societat Catalana Cardiologia, 5, 41.
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Katerine Diaz, Jesus Martinez del Rincon, Marçal Rusiñol, & Aura Hernandez-Sabate. (2019). "Feature Extraction by Using Dual-Generalized Discriminative Common Vectors " . Journal of Mathematical Imaging and Vision, 61(3), 331–351.
Abstract: In this paper, a dual online subspace-based learning method called dual-generalized discriminative common vectors (Dual-GDCV) is presented. The method extends incremental GDCV by exploiting simultaneously both the concepts of incremental and decremental learning for supervised feature extraction and classification. Our methodology is able to update the feature representation space without recalculating the full projection or accessing the previously processed training data. It allows both adding information and removing unnecessary data from a knowledge base in an efficient way, while retaining the previously acquired knowledge. The proposed method has been theoretically proved and empirically validated in six standard face recognition and classification datasets, under two scenarios: (1) removing and adding samples of existent classes, and (2) removing and adding new classes to a classification problem. Results show a considerable computational gain without compromising the accuracy of the model in comparison with both batch methodologies and other state-of-art adaptive methods.
Keywords: Online feature extraction; Generalized discriminative common vectors; Dual learning; Incremental learning; Decremental learning
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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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Katerine Diaz, Jesus Martinez del Rincon, Aura Hernandez-Sabate, & Debora Gil. (2018). "Continuous head pose estimation using manifold subspace embedding and multivariate regression " . IEEE Access, 6, 18325–18334.
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.
Keywords: Head Pose estimation; HOG features; Generalized Discriminative Common Vectors; B-splines; Multiple linear regression
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Katerine Diaz, Jesus Martinez del Rincon, & Aura Hernandez-Sabate. (2017). "Decremental generalized discriminative common vectors applied to images classification " . Knowledge-Based Systems, 131, 46–57.
Abstract: In this paper, a novel decremental subspace-based learning method called Decremental Generalized Discriminative Common Vectors method (DGDCV) is presented. The method makes use of the concept of decremental learning, which we introduce in the field of supervised feature extraction and classification. By efficiently removing unnecessary data and/or classes for a knowledge base, our methodology is able to update the model without recalculating the full projection or accessing to the previously processed training data, while retaining the previously acquired knowledge. The proposed method has been validated in 6 standard face recognition datasets, showing a considerable computational gain without compromising the accuracy of the model.
Keywords: Decremental learning; Generalized Discriminative Common Vectors; Feature extraction; Linear subspace methods; Classification
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