Introducción al reconocimiento de caras

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Modelado de expresiones para una cara robótica

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Modelado de expresiones para una cara robótica

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Docencia en robótica móvil

Cada vez son más las posibilidades y recursos con los que cuenta el profesorado universitario para impartir conocimientos en temas de contenido disciplinar en continua fase de cambio y adaptación como es el caso de la Robótica Móvil. Son ya muy pocos los centros que no disponen de alguna plataforma móvil más o menos elaborada. En este trabajo presentamos las experiencias derivadas de la impartición de dos cursos de robótica móvil, uno de ellos desarrollado en un ámbito no universitario. En particular, se describen y analizan las posibilidades y limitaciones de dos productos hardware, el kit Lego® MindStorms y la plataforma Pioneer 2-DX de ActivMedia Robotics, así como uno software, el entorno Saphira, utilizados en el contexto de las actividades prácticas de los mencionados cursos.Peer Reviewe

Docencia en robótica móvil

Cada vez son más las posibilidades y recursos con los que cuenta el profesorado universitario para impartir conocimientos en temas de contenido disciplinar en continua fase de cambio y adaptación como es el caso de la Robótica Móvil. Son ya muy pocos los centros que no disponen de alguna plataforma móvil más o menos elaborada. En este trabajo presentamos las experiencias derivadas de la impartición de dos cursos de robótica móvil, uno de ellos desarrollado en un ámbito no universitario. En particular, se describen y analizan las posibilidades y limitaciones de dos productos hardware, el kit Lego® MindStorms y la plataforma Pioneer 2-DX de ActivMedia Robotics, así como uno software, el entorno Saphira, utilizados en el contexto de las actividades prácticas de los mencionados cursos.Peer Reviewe

Docencia en robótica móvil

Cada vez son más las posibilidades y recursos con los que cuenta el profesorado universitario para impartir conocimientos en temas de contenido disciplinar en continua fase de cambio y adaptación como es el caso de la Robótica Móvil. Son ya muy pocos los centros que no disponen de alguna plataforma móvil más o menos elaborada. En este trabajo presentamos las experiencias derivadas de la impartición de dos cursos de robótica móvil, uno de ellos desarrollado en un ámbito no universitario. En particular, se describen y analizan las posibilidades y limitaciones de dos productos hardware, el kit Lego® MindStorms y la plataforma Pioneer 2-DX de ActivMedia Robotics, así como uno software, el entorno Saphira, utilizados en el contexto de las actividades prácticas de los mencionados cursos.Peer Reviewe

Programación de prototipos físicos como herramienta formativa en Informática

En este trabajo se presenta una experiencia docente donde los autores emplean prototipos físicos controlados por computador, réplicas a escala de un sistema real, para motivar el estudio y aprendizaje de materias relacionadas con la Informática Industrial. Los autores plasman su experiencia, superior a diez años, empleando el sistema de construcción de prototipos fischertechnik™ en las prácticas de laboratorio de la asignatura de Automatización Industrial integrada en la titulación de Ingeniería en Informática. La exposición establece el contexto de trabajo, motivación y planteamiento de la experiencia docente adaptada al EEES (Espacio Europeo de Educación Superior), para finalizar con los resultados obtenidos, su discusión y las conclusiones.SUMMARY -- This paper presents an educational experience in an undergraduate course on Informatics where the authors use computer controlled physical prototypes, replicas of real systems, to motivate the study and learning of industrial automation topics. The authors describe an experience of over ten years using the fischertechnik™ prototyping system in the lab of Industrial Automation course within a Computer Science degree. The exposition sets the work context, motivation and implementation for the academic experience within the EHEA (European Higher Education Area).Peer Reviewe

Diffeomorphic transforms for data augmentation of highly variable shape and texture objects

[EN] Background and objective: Training a deep convolutional neural network (CNN) for automatic image classification requires a large database with images of labeled samples. However, in some applications such as biology and medicine only a few experts can correctly categorize each sample. Experts are able to identify small changes in shape and texture which go unnoticed by untrained people, as well as distinguish between objects in the same class that present drastically different shapes and textures. This means that currently available databases are too small and not suitable to train deep learning models from scratch. To deal with this problem, data augmentation techniques are commonly used to increase the dataset size. However, typical data augmentation methods introduce artifacts or apply distortions to the original image, which instead of creating new realistic samples, obtain basic spatial variations of the original ones. Methods: We propose a novel data augmentation procedure which generates new realistic samples, by combining two samples that belong to the same class. Although the idea behind the method described in this paper is to mimic the variations that diatoms experience in different stages of their life cycle, it has also been demonstrated in glomeruli and pollen identification problems. This new data augmentation procedure is based on morphing and image registration methods that perform diffeomorphic transformations. Results: The proposed technique achieves an increase in accuracy over existing techniques of 0.47%, 1.47%, and 0.23% for diatom, glomeruli and pollen problems respectively. Conclusions: For the Diatom dataset, the method is able to simulate the shape changes in different diatom life cycle stages, and thus, images generated resemble newly acquired samples with intermediate shapes. In fact, the other methods compared obtained worse results than those which were not using data augmentation. For the Glomeruli dataset, the method is able to add new samples with different shapes and degrees of sclerosis (through different textures). This is the case where our proposed DA method is more beneficial, when objects highly differ in both shape and texture. Finally, for the Pollen dataset, since there are only small variations between samples in a few classes and this dataset has other features such as noise which are likely to benefit other existing DA techniques, the method still shows an improvement of the resultsSIThe authors acknowledge financial support of the Spanish Government and Junta de Comunidades de Castilla-La Mancha under projects AQUALITAS (Ref. CTM2014-51907-C2-R-MINECO), HYPERDEEP (Ref. SBPLY/19/180501/000273), and APRENDAMOS (Ref. SBPLY/17/180501/000543). They would also like to extend the acknowledgment to technicians Enrique Cepeda and Jesus Diaz for their help in running some experiment

Automated Diatom Classification (Part B): A Deep Learning Approach

Diatoms, a kind of algae microorganisms with several species, are quite useful for water quality determination, one of the hottest topics in applied biology nowadays. At the same time, deep learning and convolutional neural networks (CNN) are becoming an extensively used technique for image classification in a variety of problems. This paper approaches diatom classification with this technique, in order to demonstrate whether it is suitable for solving the classification problem. An extensive dataset was specifically collected (80 types, 100 samples/type) for this study. The dataset covers different illumination conditions and it was computationally augmented to more than 160,000 samples. After that, CNNs were applied over datasets pre-processed with different image processing techniques. An overall accuracy of 99% is obtained for the 80-class problem and different kinds of images (brightfield, normalized). Results were compared to previous presented classification techniques with different number of samples. As far as the authors know, this is the first time that CNNs are applied to diatom classification.We acknowledge support by the CSIC Open Access Publication Initiative through its Unit of Information Resources for Research (URICI).The authors acknowledge financial support of the Spanish Government under the Aqualitas-retos project (Ref. CTM2014-51907-C2-2-R-MINECO) http://aqualitas-retos.es/en/

Automated Diatom Classification (Part B): A Deep Learning Approach

This article belongs to the Special Issue Automated Analysis and Identification of Phytoplankton Images[EN] Diatoms, a kind of algae microorganisms with several species, are quite useful for water quality determination, one of the hottest topics in applied biology nowadays. At the same time, deep learning and convolutional neural networks (CNN) are becoming an extensively used technique for image classification in a variety of problems. This paper approaches diatom classification with this technique, in order to demonstrate whether it is suitable for solving the classification problem. An extensive dataset was specifically collected (80 types, 100 samples/type) for this study. The dataset covers different illumination conditions and it was computationally augmented to more than 160,000 samples. After that, CNNs were applied over datasets pre-processed with different image processing techniques. An overall accuracy of 99% is obtained for the 80-class problem and different kinds of images (brightfield, normalized). Results were compared to previous presented classification techniques with different number of samples. As far as the authors know, this is the first time that CNNs are applied to diatom classificationSIThe authors acknowledge financial support of the Spanish Government under the Aqualitas-retos project (Ref. CTM2014-51907-C2-2-R-MINECO) http://aqualitas-retos.es/en