Hand movement recognition for Brazilian Sign Language: A study using distance-based neural networks

Dias, Daniel B.; Madeo, Renata Cristina Barros; Rocha, Tânia Rúbia Flores da; Bíscaro, Helton Hideraldo; Peres, Sarajane Marques

doi:10.1109/ijcnn.2009.5178917

Cited by 75 publications

(45 citation statements)

References 11 publications

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“…Using an analog readout technique, a good overall testing classification accuracy of around 82% was reported on this dataset. Additional optimization of the eSNN parameters and the features of the data using the DQiPSO algorithm further increased this accuracy to around 89% which compares very favourably to the results reported in [15] where a traditional Multilayer Perceptron was used.…”

Section: Sign Language Recognitionsupporting

confidence: 80%

“…In [67], this method was investigated using a real-world dataset called LIBRAS [15]. LIBRAS is the acronym for LIngua BRAsileira de Sinais, which is the official Brazilian sign language.…”

Section: Sign Language Recognitionmentioning

confidence: 99%

“…All samples have been organized in ten sub-datasets, each representing a different classification scenario. More comprehensive details about the dataset can be found in [15]. The data can be obtained from the UCI machine learning repository.…”

Section: Sign Language Recognitionmentioning

confidence: 99%

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Evolving spiking neural network—a survey

2013

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This paper provides a comprehensive literature survey on the evolving Spiking Neural Network (eSNN) architecture since its introduction in 2006 as a further extension of the ECoS paradigm introduced by Kasabov in 1998. We summarize the functioning of the method, discuss several of its extensions and present a number of applications in which the eSNN method was employed. We focus especially on some proposed extensions that allow the processing of spatio-temporal data and for feature and parameter optimisation of eSNN models to achieve better accuracy on classification/prediction problems and to facilitate new knowledge discovery. Finally, some open problems are discussed and future directions highlighted.

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Section: Sign Language Recognitionsupporting

confidence: 80%

“…In [67], this method was investigated using a real-world dataset called LIBRAS [15]. LIBRAS is the acronym for LIngua BRAsileira de Sinais, which is the official Brazilian sign language.…”

Section: Sign Language Recognitionmentioning

confidence: 99%

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Evolving spiking neural network—a survey

2013

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“…The total data consists of 360 instances of 90 numeric attributes. More information on data can be found in Dias et al (2009). The data set is available on the UCI Machine Learning Repository.…”

Section: Libras Movement Databasementioning

confidence: 99%

Improved methods for the imputation of missing data by nearest neighbor methods

Tutz

Ramzan

2015

Computational Statistics & Data Analysis

124

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Missing data is an important issue in almost all fields of quantitative research. A nonparametric procedure that has been shown to be useful is the nearest neighbor imputation method. We suggest a weighted nearest neighbor imputation method based on L q -distances. The weighted method is shown to have smaller imputation error than available NN estimates. In addition we consider weighted neighbor imputation methods that use selected distances. The careful selection of distances that carry information on the missing values yields an imputation tool that outperforms competing nearest neighbor methods distinctly. Simulation studies show that the suggested weighted imputation with selection of distances provides the smallest imputation error, in particular when the number of predictors is large. In addition, the selected procedure is applied to real data from different fields.

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“…In recent years, due to the increasing need for universal accessibility of computing resources, gesture recognition has been widely researched, and involves various types of problems, including recognition of the hand structure and movement, the body and the face [2]. Data regarding hand behavior may be obtained through several sources, such as hand sensors [3] and images achieved with one or more cameras pointed towards the hands.…”

Section: Accepted Manuscriptmentioning

confidence: 99%