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Akhil Gurram, Onay Urfalioglu, Ibrahim Halfaoui, Fahd Bouzaraa, & Antonio Lopez. (2018). Monocular Depth Estimation by Learning from Heterogeneous Datasets. In IEEE Intelligent Vehicles Symposium (pp. 2176–2181).
Abstract: Depth estimation provides essential information to perform autonomous driving and driver assistance. Especially, Monocular Depth Estimation is interesting from a practical point of view, since using a single camera is cheaper than many other options and avoids the need for continuous calibration strategies as required by stereo-vision approaches. State-of-the-art methods for Monocular Depth Estimation are based on Convolutional Neural Networks (CNNs). A promising line of work consists of introducing additional semantic information about the traffic scene when training CNNs for depth estimation. In practice, this means that the depth data used for CNN training is complemented with images having pixel-wise semantic labels, which usually are difficult to annotate (eg crowded urban images). Moreover, so far it is common practice to assume that the same raw training data is associated with both types of ground truth, ie, depth and semantic labels. The main contribution of this paper is to show that this hard constraint can be circumvented, ie, that we can train CNNs for depth estimation by leveraging the depth and semantic information coming from heterogeneous datasets. In order to illustrate the benefits of our approach, we combine KITTI depth and Cityscapes semantic segmentation datasets, outperforming state-of-the-art results on Monocular Depth Estimation.
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German Ros, Angel Sappa, Daniel Ponsa, & Antonio Lopez. (2012). Visual SLAM for Driverless Cars: A Brief Survey. In IEEE Workshop on Navigation, Perception, Accurate Positioning and Mapping for Intelligent Vehicles.
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Simone Balocco, Mauricio Gonzalez, Ricardo Ñancule, Petia Radeva, & Gabriel Thomas. (2018). Calcified Plaque Detection in IVUS Sequences: Preliminary Results Using Convolutional Nets. In International Workshop on Artificial Intelligence and Pattern Recognition (Vol. 11047, pp. 34–42). LNCS.
Abstract: The manual inspection of intravascular ultrasound (IVUS) images to detect clinically relevant patterns is a difficult and laborious task performed routinely by physicians. In this paper, we present a framework based on convolutional nets for the quick selection of IVUS frames containing arterial calcification, a pattern whose detection plays a vital role in the diagnosis of atherosclerosis. Preliminary experiments on a dataset acquired from eighty patients show that convolutional architectures improve detections of a shallow classifier in terms of 𝐹1-measure, precision and recall.
Keywords: Intravascular ultrasound images; Convolutional nets; Deep learning; Medical image analysis
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Fadi Dornaika, & Bogdan Raducanu. (2007). Efficient Facial Expression Recognition for Human Robot Interaction. In Computational and Ambient Intelligence, 9th International Work–Conference on Artificial Neural Networks (Vol. 4507, 700–708). LNCS.
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Xavier Perez Sala, Cecilio Angulo, & Sergio Escalera. (2011). Biologically Inspired Path Execution Using SURF Flow in Robot Navigation. In 11th International Work Conference on Artificial Neural Networks (Vol. II, pp. 581–588). Springer Berlin Heidelberg.
Abstract: An exportable and robust system using only camera images is proposed for path execution in robot navigation. Motion information is extracted in the form of optical flow from SURF robust descriptors of consecutive frames, so the method is called SURF flow. This information is used to correct robot displacement when a straight forward path command is sent to the robot, but it is not really executed due to several robot and environmental concerns. The proposed system has been successfully tested on the legged robot Aibo.
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Victoria Ruiz, Angel Sanchez, Jose F. Velez, & Bogdan Raducanu. (2019). Automatic Image-Based Waste Classification. In International Work-Conference on the Interplay Between Natural and Artificial Computation. From Bioinspired Systems and Biomedical Applications to Machine Learning (Vol. 11487, 422–431). LNCS.
Abstract: The management of solid waste in large urban environments has become a complex problem due to increasing amount of waste generated every day by citizens and companies. Current Computer Vision and Deep Learning techniques can help in the automatic detection and classification of waste types for further recycling tasks. In this work, we use the TrashNet dataset to train and compare different deep learning architectures for automatic classification of garbage types. In particular, several Convolutional Neural Networks (CNN) architectures were compared: VGG, Inception and ResNet. The best classification results were obtained using a combined Inception-ResNet model that achieved 88.6% of accuracy. These are the best results obtained with the considered dataset.
Keywords: Computer Vision; Deep learning; Convolutional neural networks; Waste classification
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Lluis Gomez, & Dimosthenis Karatzas. (2014). Scene Text Recognition: No Country for Old Men? In 1st International Workshop on Robust Reading.
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Henry Velesaca, Steven Araujo, Patricia Suarez, Angel Sanchez, & Angel Sappa. (2020). Off-the-Shelf Based System for Urban Environment Video Analytics. In 27th International Conference on Systems, Signals and Image Processing.
Abstract: This paper presents the design and implementation details of a system build-up by using off-the-shelf algorithms for urban video analytics. The system allows the connection to
public video surveillance camera networks to obtain the necessary information to generate statistics from urban scenarios (e.g., amount of vehicles, type of cars, direction, numbers of persons, etc.). The obtained information could be used not only for traffic management but also to estimate the carbon footprint of urban scenarios. As a case study, a university campus is selected to evaluate the performance of the proposed system. The system is implemented in a modular way so that it is being used as a testbed to evaluate different algorithms. Implementation results are provided showing the validity and utility of the proposed approach.
Keywords: greenhouse gases; carbon footprint; object detection; object tracking; website framework; off-the-shelf video analytics
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Enric Marti, Jaume Rocarias, Ricardo Toledo, & Aura Hernandez-Sabate. (2009). Caronte: plataforma Moodle con gestion flexible de grupos. Primeras experiencias en asignaturas de Ingenieria Informatica.
Abstract: En este artículo se presenta Caronte, entorno LMS (Learning Management System) basado en Moodle. Una característica importante del entorno es la gestión flexible de grupos en una asignatura. Entendemos por grupo un conjunto de alumnos que realizan un trabajo y uno de ellos entrega la actividad propuesta (práctica, encuesta, etc.) en representación del grupo. Hemos trabajado en la confección de estos grupos, implementando un sistema de inscripción por contraseña.
Caronte ofrece un conjunto de actividades basadas en este concepto de grupo: encuestas, tareas (entrega de trabajos o prácticas), encuestas de autoevaluación y cuestionarios, entre otras.
Basada en nuestra actividad de encuesta, hemos definido una actividad de Control, que permite un cierto feedback electrónico del profesor sobre la actividad de los alumnos.
Finalmente, se presenta un resumen de las experiencias de uso de Caronte sobre asignaturas de Ingeniería Informática en el curso 2007-08.
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Joan Oliver, Ricardo Toledo, J. Pujol, J. Sorribes, & E. Valderrama. (2009). Un ABP basado en la robotica para las ingenierias informaticas.
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Enric Marti, Ferran Poveda, Antoni Gurgui, & Debora Gil. (2011). Aprendizaje Basado en Proyectos en Ingeniería Informática. Resultados y reflexiones de seis años de experiencia.
Abstract: In this workshop a 6 years experience in Project Based Learning (PBL) in Computer Graphics, Computer Engineering course at the Autonomous University of Barcelona (UAB) is presented. We use a Moodle environment suited to manage the documentation generated in PBL. The course is organized by means of two alternative routes: a classic itinerary of lectures and test-based evaluation and another with PBL. In the PBL itinerary we explain the organization in teamgroups, homework tutoring and monitoring and evaluation guidelines for students. We provide some of the work done by students, and the results of assessment surveys carried out to students during these years. We report the evolution of our PBL itinerary in terms of, both, organization and student surveys.
The workshop aims at discussing about on the advantages and disadvantages of using these active methodologies in technical degrees such as computer engineering, in order to debate about the most suitable way of organizing PBL and assessing students learning rate.
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Enric Marti, Debora Gil, Marc Vivet, & Carme Julia. (2009). Aprendizaje Basado en Proyectos en la asignatura de Gráficos por Computador en Ingeniería Informática. Balance de cuatro años de experiencia. Barcelona, Spain.
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Enric Marti, Debora Gil, Marc Vivet, & Carme Julia. (2009). Uso de recursos virtuales en Aprendizaje Basado en Proyectos. Una experiencia en la asignatura de Gráficos por Computador. Octava Jornada sobre Aprendizaje Cooperativo. Lleida.
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Laura Igual, & Xavier Baro. (2013). Experiencia de aprendizaje de programación basada en proyectos. Simposio-Taller Estrategias y herramientas para el aprendizaje y la evaluación.
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Xavier Baro, David Masip, Elena Planas, & Julia Minguillon. (2013). PeLP: Plataforma para el Aprendizaje de Lenguajes de Programación.
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