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M. Gomez and 6 others. 2002. Reconstrucción de un modelo espacio-temporal de la luz del vaso a partir de secuencias de ecografía intracoronaria. XXXVIII Congreso Nacional de la Sociedad Española de Cardiología..
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Oriol Rodriguez-Leor and 10 others. 2002. Ecografia Intracoronària: Segmentació Automàtica de area de la llum. XXXVIII Congreso Nacional de la Sociedad Española de Cardiología..
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Enric Marti, Carme Julia and Debora Gil. 2007. A PBL Experience in the Teaching of Computer Graphics. XVII Congreso Español de Informàtica Gráfica.95–103.
Abstract: Project-Based Learning (PBL) is an educational strategy to improve student’s learning capability that, in recent years, has had a progressive acceptance in undergraduate studies. This methodology is based on solving a problem or project in a student working group. In this way, PBL focuses on learning the necessary tools to correctly find a solution to given problems. Since the learning initiative is transferred to the student, the PBL method promotes students own abilities. This allows a better assessment of the true workload that carries out the student in the subject. It follows that the methodology conforms to the guidelines of the Bologna document, which quantifies the student workload in a subject by means of the European credit transfer system (ECTS). PBL is currently applied in undergraduate studies needing strong practical training such as medicine, nursing or law sciences. Although this is also the case in engineering studies, amazingly, few experiences have been reported. In this paper we propose to use PBL in the educational organization of the Computer Graphics subjects in the Computer Science degree. Our PBL project focuses in the development of a C++ graphical environment based on the OpenGL libraries for visualization and handling of different graphical objects. The starting point is a basic skeleton that already includes lighting functions, perspective projection with mouse interaction to change the point of view and three predefined objects. Students have to complete this skeleton by adding their own functions to solve the project. A total number of 10 projects have been proposed and successfully solved. The exercises range from human face rendering to articulated objects, such as robot arms or puppets. In the present paper we extensively report the statement and educational objectives for two of the projects: solar system visualization and a chess game. We report our earlier educational experience based on the standard classroom theoretical, problem and practice sessions and the reasons that motivated searching for other learning methods. We have mainly chosen PBL because it improves the student learning initiative. We have applied the PBL educational model since the beginning of the second semester. The student’s feedback increases in his interest for the subject. We present a comparative study of the teachers’ and students’ workload between PBL and the classic teaching approach, which suggests that the workload increase in PBL is not as high as it seems.
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Xavier Boix and 7 others. 2009. Combining local and global bag-of-word representations for semantic segmentation. Workshop on The PASCAL Visual Object Classes Challenge.
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Joan Serrat, J. Argemi and Juan J. Villanueva. 1991. Automatization of TW2 method using a knowledge-based image analysis system. VIth International Congress of Auxology..
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Petia Radeva and Joan Serrat. 1993. Rubber Snake: Implementation on Signed Distance Potential. Vision Conference.187–194.
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Antonio Lopez and Joan Serrat. 1995. Image Analysis through Surface Geometric Descriptors. VI National Simposium on Pattern Recognition and image Analysis..
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Joan Serrat. 1995. Aplicacion del analisis de imagenes en radiologia. VI National Simposium on Pattern Recognition and image Analysis.
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Craig Von Land, Ricardo Toledo and Juan J. Villanueva. 1996. CARE: Computer Assisted Radiology Environment. Tecnologia de Imagenes Medicas, Convencion Iberoamericana sobre la Salud en la Sociedad Global de la Informacion..
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Mohamed Ramzy Ibrahim, Robert Benavente, Daniel Ponsa and Felipe Lumbreras. 2023. Unveiling the Influence of Image Super-Resolution on Aerial Scene Classification. Progress in Pattern Recognition, Image Analysis, Computer Vision, and Applications.214–228. (LNCS.)
Abstract: Deep learning has made significant advances in recent years, and as a result, it is now in a stage where it can achieve outstanding results in tasks requiring visual understanding of scenes. However, its performance tends to decline when dealing with low-quality images. The advent of super-resolution (SR) techniques has started to have an impact on the field of remote sensing by enabling the restoration of fine details and enhancing image quality, which could help to increase performance in other vision tasks. However, in previous works, contradictory results for scene visual understanding were achieved when SR techniques were applied. In this paper, we present an experimental study on the impact of SR on enhancing aerial scene classification. Through the analysis of different state-of-the-art SR algorithms, including traditional methods and deep learning-based approaches, we unveil the transformative potential of SR in overcoming the limitations of low-resolution (LR) aerial imagery. By enhancing spatial resolution, more fine details are captured, opening the door for an improvement in scene understanding. We also discuss the effect of different image scales on the quality of SR and its effect on aerial scene classification. Our experimental work demonstrates the significant impact of SR on enhancing aerial scene classification compared to LR images, opening new avenues for improved remote sensing applications.
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