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Fernando Vilariño. (2019)." Public Libraries Exploring how technology transforms the cultural experience of people" In Workshop on Social Impact of AI. Open Living Lab Days Conference..
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Fernando Vilariño. (2020)." Unveiling the Social Impact of AI" In Workshop at Digital Living Lab Days Conference.
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Fernando Vilariño. (2019)." 3D Scanning of Capitals at Library Living Lab" .
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Jorge Bernal, F. Javier Sanchez, & Fernando Vilariño. (2011). "Depth of Valleys Accumulation Algorithm for Object Detection " In 14th Congrès Català en Intel·ligencia Artificial (Vol. 1, pp. 71–80).
Abstract: This work aims at detecting in which regions the objects in the image are by using information about the intensity of valleys, which appear to surround ob- jects in images where the source of light is in the line of direction than the camera. We present our depth of valleys accumulation method, which consists of two stages: first, the definition of the depth of valleys image which combines the output of a ridges and valleys detector with the morphological gradient to measure how deep is a point inside a valley and second, an algorithm that denotes points of the image as interior to objects those which are inside complete or incomplete boundaries in the depth of valleys image. To evaluate the performance of our method we have tested it on several application domains. Our results on object region identification are promising, specially in the field of polyp detection in colonoscopy videos, and we also show its applicability in different areas.
Keywords: Object Recognition, Object Region Identification, Image Analysis, Image Processing
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Jorge Bernal, F. Javier Sanchez, & Fernando Vilariño. (2012). "Towards Automatic Polyp Detection with a Polyp Appearance Model " . Pattern Recognition, 45(9), 3166–3182.
Abstract: This work aims at the automatic polyp detection by using a model of polyp appearance in the context of the analysis of colonoscopy videos. Our method consists of three stages: region segmentation, region description and region classification. The performance of our region segmentation method guarantees that if a polyp is present in the image, it will be exclusively and totally contained in a single region. The output of the algorithm also defines which regions can be considered as non-informative. We define as our region descriptor the novel Sector Accumulation-Depth of Valleys Accumulation (SA-DOVA), which provides a necessary but not sufficient condition for the polyp presence. Finally, we classify our segmented regions according to the maximal values of the SA-DOVA descriptor. Our preliminary classification results are promising, especially when classifying those parts of the image that do not contain a polyp inside.
Keywords: Colonoscopy,PolypDetection,RegionSegmentation,SA-DOVA descriptot
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Jorge Bernal, F. Javier Sanchez, & Fernando Vilariño. (2011). "Integration of Valley Orientation Distribution for Polyp Region Identification in Colonoscopy " In In MICCAI 2011 Workshop on Computational and Clinical Applications in Abdominal Imaging (Vol. 6668, pp. 76–83). Lecture Notes in Computer Science. Springer Link.
Abstract: This work presents a region descriptor based on the integration of the information that the depth of valleys image provides. The depth of valleys image is based on the presence of intensity valleys around polyps due to the image acquisition. Our proposed method consists of defining, for each point, a series of radial sectors around it and then accumulates the maxima of the depth of valleys image only if the orientation of the intensity valley coincides with the orientation of the sector above. We apply our descriptor to a prior segmentation of the images and we present promising results on polyp detection, outperforming other approaches that also integrate depth of valleys information.
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Jorge Bernal, F. Javier Sanchez, & Fernando Vilariño. (2011). "Current Challenges on Polyp Detection in Colonoscopy Videos: From Region Segmentation to Region Classification. a Pattern Recognition-based Approach.ased Approach " In K. Djemal (Ed.), 2nd International Workshop on Medical Image Analysis and Descriptionfor Diagnosis Systems (pp. 62–71). SciTePress.
Abstract: In this paper we present our approach on real-time polyp detection in colonoscopy videos. Our method consists of three stages: Image Segmentation, Region Description and Image Classification. Taking into account the constraints of our project, we introduce our segmentation system that is based on the model of appearance of the polyp that we have defined after observing real videos from colonoscopy processes. The output of this stage will ideally be a low number of regions of which one of them should cover the whole polyp region (if there is one in the image). This regions will be described in terms of features and, as a result of a machine learning schema, classified based on the values that they have for the several features that we will use on their description. Although we are still on the early stages of the project, we present some preliminary segmentation results that indicates that we are going in a good direction.
Keywords: Medical Imaging, Colonoscopy, Pattern Recognition, Segmentation, Polyp Detection, Region Description, Machine Learning, Real-time.
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Jorge Bernal, Fernando Vilariño, & F. Javier Sanchez. (2011). "Towards Intelligent Systems for Colonoscopy " In Paul Miskovitz (Ed.), Colonoscopy (Vol. 1, pp. 257–282). Intech.
Abstract: In this chapter we present tools that can be used to build intelligent systems for colonoscopy.
The idea is, by using methods based on computer vision and artificial intelligence, add significant value to the colonoscopy procedure. Intelligent systems are being used to assist in other medical interventions
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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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Onur Ferhat, Fernando Vilariño, & F. Javier Sanchez. (2014). A cheap portable eye-tracker solution for common setups. Journal of Eye Movement Research, 7(3), 1–10.
Abstract: We analyze the feasibility of a cheap eye-tracker where the hardware consists of a single webcam and a Raspberry Pi device. Our aim is to discover the limits of such a system and to see whether it provides an acceptable performance. We base our work on the open source Opengazer (Zielinski, 2013) and we propose several improvements to create a robust, real-time system which can work on a computer with 30Hz sampling rate. After assessing the accuracy of our eye-tracker in elaborated experiments involving 12 subjects under 4 different system setups, we install it on a Raspberry Pi to create a portable stand-alone eye-tracker which achieves 1.42° horizontal accuracy with 3Hz refresh rate for a building cost of 70 Euros.
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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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Gloria Fernandez Esparrach, Jorge Bernal, Cristina Rodriguez de Miguel, Debora Gil, Fernando Vilariño, Henry Cordova, et al. (2015). "Colonic polyps are correctly identified by a computer vision method using wm-dova energy maps " In Proceedings of 23 United European- UEG Week 2015.
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Santiago Segui, Laura Igual, Fernando Vilariño, Petia Radeva, Carolina Malagelada, Fernando Azpiroz, et al. (2008). "Diagnostic System for Intestinal Motility Disfunctions Using Video Capsule Endoscopy " In and J.K. Tsotsos M. V. A. Gasteratos (Ed.), Computer Vision Systems. 6th International (Vol. 5008, 251–260). Berlin Heidelberg: Springer-Verlag.
Abstract: Wireless Video Capsule Endoscopy is a clinical technique consisting of the analysis of images from the intestine which are pro- vided by an ingestible device with a camera attached to it. In this paper we propose an automatic system to diagnose severe intestinal motility disfunctions using the video endoscopy data. The system is based on the application of computer vision techniques within a machine learn- ing framework in order to obtain the characterization of diverse motil- ity events from video sequences. We present experimental results that demonstrate the effectiveness of the proposed system and compare them with the ground-truth provided by the gastroenterologists.
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Helena Muñoz, Fernando Vilariño, & Dimosthenis Karatzas. (2019). "Eye-Movements During Information Extraction from Administrative Documents " In International Conference on Document Analysis and Recognition Workshops (pp. 6–9).
Abstract: A key aspect of digital mailroom processes is the extraction of relevant information from administrative documents. More often than not, the extraction process cannot be fully automated, and there is instead an important amount of manual intervention. In this work we study the human process of information extraction from invoice document images. We explore whether the gaze of human annotators during an manual information extraction process could be exploited towards reducing the manual effort and automating the process. To this end, we perform an eye-tracking experiment replicating real-life interfaces for information extraction. Through this pilot study we demonstrate that relevant areas in the document can be identified reliably through automatic fixation classification, and the obtained models generalize well to new subjects. Our findings indicate that it is in principle possible to integrate the human in the document image analysis loop, making use of the scanpath to automate the extraction process or verify extracted information.
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