Algoritmos de Identificación de piel humana y su relación con los sistemas de color : Su aplicación a la segmentación de piel basada en píxeles

El objetivo principal de este trabajo se centra en determinar cuál es el modelo de representación de color más conveniente para realizar segmentación por píxeles en imágenes de piel humana. Para encontrar una respuesta al objetivo planteado se realiza un estudio comparativo de mas de 30 publicaciones en este área de aplicación. En este documento se hace particular hincapié en la realización de un relevamiento lo más amplio posible de manera de incluir publicaciones diversas con la mayor cantidad de modelos de representación de color y algoritmos de segmentación. Se describen y estudian en profundidad los sistemas de representación de color, se realiza un análisis de la distribución de la piel en cada uno de éstos sistemas, se incluye una revisión y análisis de más de treinta artículos publicados y se documentan los juegos de datos utilizados en los experimentos de los artículos. Esta información es de fundamental importancia para replicar y complementar los resultados obtenidos por los autores de los trabajos estudiados para realizar futuros experimentos.Facultad de Informátic

E-mail processing using data mining techniques

A proposal to use data mining techniques to analyze e-mails corresponding to courses carried out through a distance education plat- form is made. The purpose of this type of analyses is determining which are the groups of relevant words that allow establishing communication topics of interest. Even though this new information can have various applications, they all involve an improvement in student service. The method proposed has been applied to the e-mails of the PACENI Project (Support Project for Improving First-Year Teaching in Courses of Studies in Exact and Natural Sciences, Economic Science and Computer Science) with satisfactory results.Presentado en el IX Workshop Tecnología Informática aplicada en Educación (WTIAE)Red de Universidades con Carreras en Informática (RedUNCI

E-Mail Processing with Fuzzy SOMs and Association Rules

E-mail texts are hard to process due to their short length. In this article, the use of a diffuse neural network that is capable of identifying the most relevant terms in a set of e-mails is proposed. The associations between these terms will be measured through association rules built with the terms identified by the network. The metrics support, confidence and interest of the rules will be used to qualify the corresponding terms. The method proposed has been used to process e-mails of the PACENI Project (Support Project for Improving First-Year Teaching in Courses of Studies in Exact and Natural Sciences, Economic Science and Computer Science). With this type of analysis, the most common topics of student questions have been identified. Even though this new information can have various applications, they all involve, as a first instance, an improvement in student service.Facultad de Informátic

E-Mail Processing with Fuzzy SOMs and Association Rules

E-mail texts are hard to process due to their short length. In this article, the use of a diffuse neural network that is capable of identifying the most relevant terms in a set of e-mails is proposed. The associations between these terms will be measured through association rules built with the terms identified by the network. The metrics support, confidence and interest of the rules will be used to qualify the corresponding terms. The method proposed has been used to process e-mails of the PACENI Project (Support Project for Improving First-Year Teaching in Courses of Studies in Exact and Natural Sciences, Economic Science and Computer Science). With this type of analysis, the most common topics of student questions have been identified. Even though this new information can have various applications, they all involve, as a first instance, an improvement in student service.Facultad de Informátic

Voto electrónico

A principios del año pasado una empresa solicitó nuestros servicios informáticos con el fin de entrar en el mercado de las máquinas de voto en nuestro país, esto planteó un desafío para nuestro Instituto ya que además de los requerimientos funcionales fundamentales de un máquina de voto electrónico, debían contemplarse otros como que la misma debía ser compacta, utilizar software libre en todos los niveles de programación para garantizar la transparencia, permitir la auditabilidad mediante la impresión del voto y cumplir con todas las legislaciones vigentes sobre las elecciones en nuestro país. Cumpliendo con este conjunto de requerimientos se lograría la inserción de los equipos en el mercado ya que superarían algunas falencias detectadas en los conocidos precedentemente.Instituto de Investigación en Informátic

Handshape recognition for Argentinian Sign Language using ProbSom

Automatic sign language recognition is an important topic within the areas of human-computer interaction and machine learning. On the one hand, it poses a complex challenge that requires the intervention of various knowledge areas, such as video processing, image processing, intelligent systems and linguistics. On the other hand, robust recognition of sign language could assist in the translation process and the integration of hearingimpaired people. This paper offers two main contributions: first, the creation of a database of handshapes for the Argentinian Sign Language (LSA), which is a topic that has barely been discussed so far. Secondly, a technique for image processing, descriptor extraction and subsequent handshape classification using a supervised adaptation of self-organizing maps that is called ProbSom. This technique is compared to others in the state of the art, such as Support Vector Machines (SVM), Random Forests, and Neural Networks. The database that was built contains 800 images with 16 LSA conjurations, and is a first step towards building a comprehensive database of Argentinian signs. The ProbSom-based neural classifier, using the proposed descriptor, achieved an accuracy rate above 90%.Facultad de Informátic

Handshape recognition for Argentinian Sign Language using ProbSom

Automatic sign language recognition is an important topic within the areas of human-computer interaction and machine learning. On the one hand, it poses a complex challenge that requires the intervention of various knowledge areas, such as video processing, image processing, intelligent systems and linguistics. On the other hand, robust recognition of sign language could assist in the translation process and the integration of hearingimpaired people. This paper offers two main contributions: first, the creation of a database of handshapes for the Argentinian Sign Language (LSA), which is a topic that has barely been discussed so far. Secondly, a technique for image processing, descriptor extraction and subsequent handshape classification using a supervised adaptation of self-organizing maps that is called ProbSom. This technique is compared to others in the state of the art, such as Support Vector Machines (SVM), Random Forests, and Neural Networks. The database that was built contains 800 images with 16 LSA conjurations, and is a first step towards building a comprehensive database of Argentinian signs. The ProbSom-based neural classifier, using the proposed descriptor, achieved an accuracy rate above 90%.Facultad de Informátic

LSA64: An Argentinian Sign Language Dataset

Automatic sign language recognition is a research area that encompasses human-computer interaction, computer vision and machine learning. Robust automatic recognition of sign language could assist in the translation process and the integration of hearing-impaired people, as well as the teaching of sign language to the hearing population. Sign languages differ significantly in different countries and even regions, and their syntax and semantics are different as well from those of written languages. While the techniques for automatic sign language recognition are mostly the same for different languages, training a recognition system for a new language requires having an entire dataset for that language. This paper presents a dataset of 64 signs from the Argentinian Sign Language (LSA). The dataset, called LSA64, contains 3200 videos of 64 different LSA signs recorded by 10 subjects, and is a first step towards building a comprehensive research-level dataset of Argentinian signs, specifically tailored to sign language recognition or other machine learning tasks. The subjects that performed the signs wore colored gloves to ease the hand tracking and segmentation steps, allowing experiments on the dataset to focus specifically on the recognition of signs.XIII Workshop Bases de datos y Minería de Datos (WBDMD).Red de Universidades con Carreras en Informática (RedUNCI

LSA64: An Argentinian Sign Language Dataset

Automatic sign language recognition is a research area that encompasses human-computer interaction, computer vision and machine learning. Robust automatic recognition of sign language could assist in the translation process and the integration of hearing-impaired people, as well as the teaching of sign language to the hearing population. Sign languages differ significantly in different countries and even regions, and their syntax and semantics are different as well from those of written languages. While the techniques for automatic sign language recognition are mostly the same for different languages, training a recognition system for a new language requires having an entire dataset for that language. This paper presents a dataset of 64 signs from the Argentinian Sign Language (LSA). The dataset, called LSA64, contains 3200 videos of 64 different LSA signs recorded by 10 subjects, and is a first step towards building a comprehensive research-level dataset of Argentinian signs, specifically tailored to sign language recognition or other machine learning tasks. The subjects that performed the signs wore colored gloves to ease the hand tracking and segmentation steps, allowing experiments on the dataset to focus specifically on the recognition of signs.XIII Workshop Bases de datos y Minería de Datos (WBDMD).Red de Universidades con Carreras en Informática (RedUNCI

E-mail processing using data mining techniques

A proposal to use data mining techniques to analyze e-mails corresponding to courses carried out through a distance education plat- form is made. The purpose of this type of analyses is determining which are the groups of relevant words that allow establishing communication topics of interest. Even though this new information can have various applications, they all involve an improvement in student service. The method proposed has been applied to the e-mails of the PACENI Project (Support Project for Improving First-Year Teaching in Courses of Studies in Exact and Natural Sciences, Economic Science and Computer Science) with satisfactory results.Presentado en el IX Workshop Tecnología Informática aplicada en Educación (WTIAE)Red de Universidades con Carreras en Informática (RedUNCI