Multidimensional Representations for the Gesture Phase Segmentation Problem - An Exploratory Study using Multilayer Perceptrons

Feitosa, Ricardo Augusto de Sabóia; Rocha, Jallysson M.; Lima, Clodoaldo A. M.; Peres, Sarajane Marques

doi:10.5220/0006556003470354

Cited by 4 publications

(4 citation statements)

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“…The output of the FCN was then binarised, and the adjacent middle splitting points were formed as pairs (start and end of character), while the other points were removed [35]. Using the gesture phase segmentation dataset, they classified the gesturing phase (act) into segments (rest position, preparation, pre-stroke, strokes, post-stroke, retraction, and rest position) based on spatial and temporal features obtained from each frame using multi-layer perceptron [36]. The above literature consists of the works done on the segmentation of hand gestures; it shows that researchers have focused more on segmenting/to separate different gestures for the further stages of gesture recognition.…”

Section: Removal Of Sca Strokementioning

confidence: 99%

Segregation of meaningful strokes, a pre‐requisite for self co‐articulation removal in isolated dynamic gestures

Monsley

Yadav

Misra

et al. 2020

IET Image Processing

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Gesture formation, a pre-processing step, has its importance when variations in patterns, scale, and speed come into play. Self co-articulations are intentional movements performed by an individual to complete a gesture, whose presence in the trajectory alters its original meaning. For recognition, most researchers have directly used the trajectory formed along with these self co-articulated strokes, with a few removing it using visible trait-like velocity. Usage of velocity has shortcomings as gesturing in air differs from gesturing over a solid surface; hence, we propose a gesture formation model, which incorporates global and local measures to remove these self co-articulations. The global measure uses Euclidean distance, instantaneous velocity, and polarity calculated from the complete gesture, while the local measure segments the gesture into stroke-level segments by using the minimummaximum-polarity algorithm and applies the selective bypass rules. The proposed model, when experimented on gestures patterns with premeditated speed variation, has a mean error rate of 0.0069 and 7.40% self co-articulations;individuals' natural gesticulation has a mean error rate of 0.0371 and 12.07% self co-articulations. Experimentation on each gesture of NITS hand gesture databases showed a relative improvement of 40% (accuracy 97%) over the existing baseline models.

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Section: Removal Of Sca Strokementioning

confidence: 99%

Segregation of meaningful strokes, a pre‐requisite for self co‐articulation removal in isolated dynamic gestures

Monsley

Yadav

Misra

et al. 2020

IET Image Processing

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show abstract

“…Fonte: Ricardo Alves Feitosa, 2018 Figura 10 -Sinal filtrado (filtro wavelet) -Velocidade escalar da mão direita (eixo y) ao longo dos frames (eixo x). Neste trabalho, a reconstrução do espaço de fases será considerada com atraso de um frame de forma que o sinal do gesto para cada frame será na realidade representado por uma janela retangular simples de tamanho j, em que j será parametrizado para 1, 20 e 40 nos experimentos.…”

Section: Pré-processamentounclassified

“…Fonte: Ricardo Alves Feitosa, 2018 As implementações fornecem saídas no domínio dos números reais, limitadas no intervalo [-1,1]. Assim, para que fosse possível obter um resultado referenteà classificação dos frames do vídeo dentro do conjunto de classes finito ({D, P, S, H, R}), duas estratégias foram adotadas:…”

Section: Fonte: Ricardo Alves Feitosa 2018unclassified

“…Fonte: Ricardo Alves Feitosa, 2018 Por exemplo, um dos casos de testes foi utilizar uma representação construída com todas as coordenadas da mão direita, sobre a qual foi aplicado um pré-processamento com filtro wavelet e com normalização para então ser processada por um MLP configurado como uma rede feedforward com Levenberg-Marquardt, taxa de aprendizado de 0,01, 10 neurônios na camada oculta e a divisão dos dados entre treinamento, validação e testes definida com o uso de um k-fold com k = 10.…”

Section: Figura 15 -Organização De Testes Para O Mlpunclassified

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Estudo de representações multidimensionais para segmentação das fases do gesto

Feitosa¹

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Autorizo a reprodução e divulgação total ou parcial deste trabalho, por qualquer meio convencional ou eletrônico, para fins de estudo e pesquisa, desde que citada a fonte. Gestures analysis systems have stood out for their contributions to the interaction between humans, humans and machines, and humans and environments. In this interaction, natural gesticulation is seen as part of a linguistic system that supports the communication, and all information systems aiming at the use of such an interaction in making decisions should be able to interpret it. Such an interpretation can be carried out through the gesture phases segmentation. In order to solve this task, the establishment of an efficient data representation for gestures is a critical issue. The chosen representation as well as its combination with techniques for analysis can or can not favor the solution being developed. In this work, different forms representation for gestures are applied to pattern recognition algorithms MLP and SOM to create an adequate environment to identify the more discriminative representations, which aspect the different representations describe with more efficiency, and how they can be combined in order to improve gesture phases segmentation. To construct the multidimensional representations we use spatial and temporal aspects combined with the normalization of the data and the application of the wavelet filter in the search for the most discriminating representation for the recognition of the gesture phases. Both algorithms achieved good results with the use of temporal aspects. MLP was able to classify all gesture phases using representation settings containing data about all monitored members. SOM presented good ability to form groups containing data of the same gesture phase even with the use of few characteristics in the construction of the representation, but it was not possible to identify the proposal of a new gesture phase with unsupervised learning.

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Time series motifs discovery under DTW allows more robust discovery of conserved structure

Alaee

Mercer

Kamgar

et al. 2021

Data Min Knowl Disc

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Multidimensional Representations for the Gesture Phase Segmentation Problem - An Exploratory Study using Multilayer Perceptrons

Cited by 4 publications

References 0 publications

Segregation of meaningful strokes, a pre‐requisite for self co‐articulation removal in isolated dynamic gestures

Segregation of meaningful strokes, a pre‐requisite for self co‐articulation removal in isolated dynamic gestures

Estudo de representações multidimensionais para segmentação das fases do gesto

Time series motifs discovery under DTW allows more robust discovery of conserved structure

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