F.X. Perez, F. Javier Sanchez, Xavier Binefa, Xavier Roca, Jordi Vitria, & Juan J. Villanueva. (1993). A mathematical morphology-based system for IC´s inspection and analysis. In Institute of Physics Conferences Series (Vol. 135, 381–384). Institute of Physics.
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X. Binefa, F. Javier Sanchez, F.X. Perez, Xavier Roca, Jordi Vitria, & Juan J. Villanueva. (1993). Using defocus in optical inspection of integrated circuits. In Institute of Physics Conferences Series (Vol. 135, pp. 389–392). Institute of Physics.
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F. Javier Sanchez, & Jordi Vitria. (1994). ViLi + : Extended Lisp for image Processing and Computer Vision. In S.Impedovo (Ed.), Progress in Image Analysis and Processing III. World Scientific.
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Fernando Vilariño, & Petia Radeva. (2002). Patch-Optimized Discriminant Active Contours for Medical Image Segmentation. In Iberoamerican Conference on Artificial Intelligence. Springer Verlag.
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Fernando Vilariño, & Petia Radeva. (2003). Cardiac Segmentation with Discriminant Active Contours. (211–217). IOS Press.
Abstract: Dynamic tracking of heart moving is one relevant target in medical imag- ing and can be helpful for analyzing heart dynamics in the study of several cardiac diseases. For this aim, a previous segmentation problem of such structures is stated, based on certain relevant features (like edges or intensity levels, textures, etc.) Clas- sical active models have been used, but they fail when overlapping structures or not well-defined contours are present. Automatic feature learning systems may be a pow- erful tool. Discriminant active contours present optimal results in this kind of problem. They are a kind of deformable models that converge to an optimal object segmenta- tion that dynamically adapts to the object contour. The feature space is designed from a filter bank in order to guarantee the search and learning of the set of relevant fea- tures for optimal classification on each part of the object. Tracking of target evolution is obtained through the whole set of images, using information from the actual and previous stages. Feedback systems are implemented to guarantee the minimum well- separable classification set in each segmentation step. Our implementation has been proved with several series of Magnetic Resonance with improved results in segmenta- tion in comparison to previous methods.
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Fernando Vilariño, Debora Gil, & Petia Radeva. (2004). A Novel FLDA Formulation for Numerical Stability Analysis. In P. R. and I. A. J. Vitrià (Ed.), Recent Advances in Artificial Intelligence Research and Development (Vol. 113, pp. 77–84). IOS Press.
Abstract: Fisher Linear Discriminant Analysis (FLDA) is one of the most popular techniques used in classification applying dimensional reduction. The numerical scheme involves the inversion of the within-class scatter matrix, which makes FLDA potentially ill-conditioned when it becomes singular. In this paper we present a novel explicit formulation of FLDA in terms of the eccentricity ratio and eigenvector orientations of the within-class scatter matrix. An analysis of this function will characterize those situations where FLDA response is not reliable because of numerical instability. This can solve common situations of poor classification performance in computer vision.
Keywords: Supervised Learning; Linear Discriminant Analysis; Numerical Stability; Computer Vision
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Joan M. Nuñez. (2011). Computer vision techniques for characterization of finger joints in X-ray image (Dr. Fernando Vilariño and Dra. Debora Gil, Ed.) (Vol. 165). Master's thesis, , .
Abstract: Rheumatoid arthritis (RA) is an autoimmune inflammatory type of arthritis which mainly affects hands on its first stages. Though it is a chronic disease and there is no cure for it, treatments require an accurate assessment of illness evolution. Such assessment is based on evaluation of hand X-ray images by using one of the several available semi-quantitative methods. This task requires highly trained medical personnel. That is why the automation of the assessment would allow professionals to save time and effort. Two stages are involved in this task. Firstly, the joint detection, afterwards, the joint characterization. Unlike the little existing previous work, this contribution clearly separates those two stages and sets the foundations of a modular assessment system focusing on the characterization stage. A hand joint dataset is created and an accurate data analysis is achieved in order to identify relevant features. Since the sclerosis and the lower bone were decided to be the most important features, different computer vision techniques were used in order to develop a detector system for both of them. Joint space width measures are provided and their correlation with Sharp-Van der Heijde is verified
Keywords: Rheumatoid arthritis, X-ray, Sharp Van der Heijde, joint characterization, sclerosis detection, bone detection, edge, ridge
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Carles Sanchez, F. Javier Sanchez, Antoni Rosell, & Debora Gil. (2012). An illumination model of the trachea appearance in videobronchoscopy images. In Image Analysis and Recognition (Vol. 7325, pp. 313–320). LNCS. Springer Berlin Heidelberg.
Abstract: Videobronchoscopy is a medical imaging technique that allows interactive navigation inside the respiratory pathways. This imaging modality provides realistic images and allows non-invasive minimal intervention procedures. Tracheal procedures are routinary interventions that require assessment of the percentage of obstructed pathway for injury (stenosis) detection. Visual assessment in videobronchoscopic sequences requires high expertise of trachea anatomy and is prone to human error.
This paper introduces an automatic method for the estimation of steneosed trachea percentage reduction in videobronchoscopic images. We look for tracheal rings , whose deformation determines the degree of obstruction. For ring extraction , we present a ring detector based on an illumination and appearance model. This model allows us to parametrise the ring detection. Finally, we can infer optimal estimation parameters for any video resolution.
Keywords: Bronchoscopy, tracheal ring, stenosis assesment, trachea appearance model, segmentation
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F. Javier Sanchez, & Jorge Bernal. (2018). Use of Software Tools for Real-time Monitoring of Learning Processes: Application to Compilers subject. In 4th International Conference of Higher Education Advances (pp. 1359–1366).
Abstract: The effective implementation of the Higher European Education Area has meant a change regarding the focus of the learning process, being now the student at its very center. This shift of focus requires a strong involvement and fluent communication between teachers and students to succeed. Considering the difficulties associated to motivate students to take a more active role in the learning process, we explore how the use of a software tool can help both actors to improve the learning experience. We present a tool that can help students to obtain instantaneous feedback with respect to their progress in the subject as well as providing teachers with useful information about the evolution of knowledge acquisition with respect to each of the subject areas. We compare the performance achieved by students in two academic years: results show an improvement in overall performance which, after observing graphs provided by our tool, can be associated to an increase in students interest in the subject.
Keywords: Monitoring; Evaluation tool; Gamification; Student motivation
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Ana Maria Ares, Jorge Bernal, Maria Jesus Nozal, F. Javier Sanchez, & Jose Bernal. (2018). Results of the use of Kahoot! gamification tool in a course of Chemistry. In 4th International Conference on Higher Education Advances (pp. 1215–1222).
Abstract: The present study examines the use of Kahoot! as a gamification tool to explore mixed learning strategies. We analyze its use in two different groups of a theoretical subject of the third course of the Degree in Chemistry. An empirical-analytical methodology was used using Kahoot! in two different groups of students, with different frequencies. The academic results of these two group of students were compared between them and with those obtained in the previous course, in which Kahoot! was not employed, with the aim of measuring the evolution in the students´ knowledge. The results showed, in all cases, that the use of Kahoot! has led to a significant increase in the overall marks, and in the number of students who passed the subject. Moreover, some differences were also observed in students´ academic performance according to the group. Finally, it can be concluded that the use of a gamification tool (Kahoot!) in a university classroom had generally improved students´ learning and marks, and that this improvement is more prevalent in those students who have achieved a better Kahoot! performance.
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Fernando Vilariño. (2017). Bringing and keeping all the stakeholders together: creating a catalog of models of governance for innovation.
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Quentin Angermann, Jorge Bernal, Cristina Sanchez Montes, Maroua Hammami, Gloria Fernandez Esparrach, Xavier Dray, et al. (2017). Real-Time Polyp Detection in Colonoscopy Videos: A Preliminary Study For Adapting Still Frame-based Methodology To Video Sequences Analysis. In 31st International Congress and Exhibition on Computer Assisted Radiology and Surgery.
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Patrick Brandao, O. Zisimopoulos, E. Mazomenos, G. Ciutib, Jorge Bernal, M. Visentini-Scarzanell, et al. (2018). Towards a computed-aided diagnosis system in colonoscopy: Automatic polyp segmentation using convolution neural networks. JMRR - Journal of Medical Robotics Research.
Abstract: Early diagnosis is essential for the successful treatment of bowel cancers including colorectal cancer (CRC) and capsule endoscopic imaging with robotic actuation can be a valuable diagnostic tool when combined with automated image analysis. We present a deep learning rooted detection and segmentation framework for recognizing lesions in colonoscopy and capsule endoscopy images. We restructure established convolution architectures, such as VGG and ResNets, by converting them into fully-connected convolution networks (FCNs), ne-tune them and study their capabilities for polyp segmentation and detection. We additionally use Shape-from-Shading (SfS) to recover depth and provide a richer representation of the tissue's structure in colonoscopy images. Depth is
incorporated into our network models as an additional input channel to the RGB information and we demonstrate that the resulting network yields improved performance. Our networks are tested on publicly available datasets and the most accurate segmentation model achieved a mean segmentation IU of 47.78% and 56.95% on the ETIS-Larib and CVC-Colon datasets, respectively. For polyp
detection, the top performing models we propose surpass the current state of the art with detection recalls superior to 90% for all datasets tested. To our knowledge, we present the rst work to use FCNs for polyp segmentation in addition to proposing a novel combination of SfS and RGB that boosts performance.
Keywords: convolutional neural networks; colonoscopy; computer aided diagnosis
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Quentin Angermann, Jorge Bernal, Cristina Sanchez Montes, Maroua Hammami, Gloria Fernandez Esparrach, Xavier Dray, et al. (2017). Clinical Usability Quantification Of a Real-Time Polyp Detection Method In Videocolonoscopy. In 25th United European Gastroenterology Week.
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Cristina Sanchez Montes, F. Javier Sanchez, Cristina Rodriguez de Miguel, Henry Cordova, Jorge Bernal, Maria Lopez Ceron, et al. (2017). Histological Prediction Of Colonic Polyps By Computer Vision. Preliminary Results. In 25th United European Gastroenterology Week.
Abstract: during colonoscopy, clinicians perform visual inspection of the polyps to predict histology. Kudo’s pit pattern classification is one of the most commonly used for optical diagnosis. These surface patterns present a contrast with respect to their neighboring regions and they can be considered as bright regions in the image that can attract the attention of computational methods.
Keywords: polyps; histology; computer vision
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