142 research outputs found
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
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 hearing-impaired
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 handshapes, 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%
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
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
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
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. We also present a
pre-processed version of the dataset, from which we computed statistics of
movement, position and handshape of the signs.Comment: Published in CACIC XXI
Sign Languague Recognition without frame-sequencing constraints: A proof of concept on the Argentinian Sign Language
Automatic sign language recognition (SLR) 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 hearing-impaired
people, as well as the teaching of sign language for the hearing population.
SLR systems usually employ Hidden Markov Models, Dynamic Time Warping or
similar models to recognize signs. Such techniques exploit the sequential
ordering of frames to reduce the number of hypothesis. This paper presents a
general probabilistic model for sign classification that combines
sub-classifiers based on different types of features such as position, movement
and handshape. The model employs a bag-of-words approach in all classification
steps, to explore the hypothesis that ordering is not essential for
recognition. The proposed model achieved an accuracy rate of 97% on an
Argentinian Sign Language dataset containing 64 classes of signs and 3200
samples, providing some evidence that indeed recognition without ordering is
possible.Comment: IBERAMIA 201
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