Patricia Suarez, Dario Carpio, & Angel Sappa. (2023). A Deep Learning Based Approach for Synthesizing Realistic Depth Maps. In 22nd International Conference on Image Analysis and Processing (Vol. 14234, 369–380). LNCS.
Abstract: This paper presents a novel cycle generative adversarial network (CycleGAN) architecture for synthesizing high-quality depth maps from a given monocular image. The proposed architecture uses multiple loss functions, including cycle consistency, contrastive, identity, and least square losses, to enable the generation of realistic and high-fidelity depth maps. The proposed approach addresses this challenge by synthesizing depth maps from RGB images without requiring paired training data. Comparisons with several state-of-the-art approaches are provided showing the proposed approach overcome other approaches both in terms of quantitative metrics and visual quality.
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Jordi Vitria, & J. Llacer. (1993). Recovering Depth from Focus Using Iterative image Estimation Techniques..
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Lubomir Latchev, Maya Dimitrova, & David Rotger. (2006). A Classifier of Technical Diagnostic States of Electrocardiograph.
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Jordi Gonzalez, X. Varona, Xavier Roca, & Juan J. Villanueva. (2001). Human Activity Learning and Recognition from Appearance..
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Josep Llados, Felipe Lumbreras, V. Chapaprieta, & J. Queralt. (2001). ICAR: Identity Card Automatic Reader..
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Gemma Sanchez, & Josep Llados. (2001). A Graph Grammar to Recognize Textured Symbols..
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Jorge Bernal, Fernando Vilariño, F. Javier Sanchez, M. Arnold, Anarta Ghosh, & Gerard Lacey. (2014). Experts vs Novices: Applying Eye-tracking Methodologies in Colonoscopy Video Screening for Polyp Search. In 2014 Symposium on Eye Tracking Research and Applications (pp. 223–226).
Abstract: We present in this paper a novel study aiming at identifying the differences in visual search patterns between physicians of diverse levels of expertise during the screening of colonoscopy videos. Physicians were clustered into two groups -experts and novices- according to the number of procedures performed, and fixations were captured by an eye-tracker device during the task of polyp search in different video sequences. These fixations were integrated into heat maps, one for each cluster. The obtained maps were validated over a ground truth consisting of a mask of the polyp, and the comparison between experts and novices was performed by using metrics such as reaction time, dwelling time and energy concentration ratio. Experimental results show a statistically significant difference between experts and novices, and the obtained maps show to be a useful tool for the characterisation of the behaviour of each group.
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Fadi Dornaika, & Bogdan Raducanu. (2009). Simultaneous 3D face pose and person-specific shape estimation from a single image using a holistic approach. In IEEE Workshop on Applications of Computer Vision.
Abstract: This paper presents a new approach for the simultaneous estimation of the 3D pose and specific shape of a previously unseen face from a single image. The face pose is not limited to a frontal view. We describe a holistic approach based on a deformable 3D model and a learned statistical facial texture model. Rather than obtaining a person-specific facial surface, the goal of this work is to compute person-specific 3D face shape in terms of a few control parameters that are used by many applications. The proposed holistic approach estimates the 3D pose parameters as well as the face shape control parameters by registering the warped texture to a statistical face texture, which is carried out by a stochastic and genetic optimizer. The proposed approach has several features that make it very attractive: (i) it uses a single grey-scale image, (ii) it is person-independent, (iii) it is featureless (no facial feature extraction is required), and (iv) its learning stage is easy. The proposed approach lends itself nicely to 3D face tracking and face gesture recognition in monocular videos. We describe extensive experiments that show the feasibility and robustness of the proposed approach.
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Robert Benavente, Ernest Valveny, Jaume Garcia, Agata Lapedriza, Miquel Ferrer, & Gemma Sanchez. (2008). Una experiencia de adaptacion al EEES de las asignaturas de programacion en Ingenieria Informatica.
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Monica Piñol, Angel Sappa, Angeles Lopez, & Ricardo Toledo. (2012). Feature Selection Based on Reinforcement Learning for Object Recognition. In Adaptive Learning Agents Workshop (pp. 33–39).
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Karla Lizbeth Caballero, Joel Barajas, Oriol Pujol, J. Mauri, & Petia Radeva. (2006). Using Radio Frequency Reconstructed IVUS Images in Tissue Classification.
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David Rotger, Petia Radeva, & Oriol Rodriguez. (2006). Vessel Tortuosity Extraction from IVUS Images.
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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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C. Santa-Marta, Jaume Garcia, A. Bajo, J.J. Vaquero, M. Ledesma-Carbayo, & Debora Gil. (2008). Influence of the Temporal Resolution on the Quantification of Displacement Fields in Cardiac Magnetic Resonance Tagged Images. In S. A. Roberto hornero (Ed.), XXVI Congreso Anual de la Sociedad Española de Ingenieria Biomedica (352–353).
Abstract: It is difficult to acquire tagged cardiac MR images with a high temporal and spatial resolution using clinical MR scanners. However, if such images are used for quantifying scores based on motion, it is essential a resolution as high as possibl e. This paper explores the influence of the temporal resolution of a tagged series on the quantification of myocardial dynamic parameters. To such purpose we have designed a SPAMM (Spatial Modulation of Magnetization) sequence allowing acquisition of sequences at simple and double temporal resolution. Sequences are processed to compute myocardial motion by an automatic technique based on the tracking of the harmonic phase of tagged images (the Harmonic Phase Flow, HPF). The results have been compared to manual tracking of myocardial tags. The error in displacement fields for double resolution sequences reduces 17%.
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