New method for optimization of static hand gesture recognition

Badi, Haitham; Omran, Alaa Hamza; Hasan, S.B.

doi:10.1109/intellisys.2017.8324347

Cited by 11 publications

(4 citation statements)

References 2 publications

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“…During the preprocessing stage, edge detection, homogeneity, and other filtering operations ware performed. And then by using complex hand contour and Ahzat methods the lines are extracted from the hand gestures [19].…”

Section: Related Workmentioning

confidence: 99%

Convolution neural networks for hand gesture recognation

Somanatti¹,

Patil²,

Hadimani³

2022

IJ-AI

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Hand gestures (not static or fixed positions) are movements of fingers and the arm to communicate messages. Hand gesture recognition is the process of identifying meaningful expressions involving the human hand. Pictorial representation of gestures will enable to understand human computer interaction (HCI). This paper describes a system using convolution neural network (CNN) for recognizing the 26 letters of the English alphabet signaled with hand gestures. A Python program was developed to recognize the gestures made in front of a web camera. The hand gestures obtained are categorized using CNN with a trained model. The model was constructed using 1,100 gestures images. The recognition rate was obtained with 91% of accuracy. The proposed method was found to be highly efficient in distinguishing and classifying gestures.

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Section: Related Workmentioning

confidence: 99%

Convolution neural networks for hand gesture recognation

Somanatti¹,

Patil²,

Hadimani³

2022

IJ-AI

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“…Es por eso, que el diseño del sistema de visión artificial se apoya en las etapas primordiales para el proceso de visión artificial (Piedra, 2008), tomando en consideración que el proyecto va orientado a satisfacer necesidades del aprendizaje del Lenguaje de Señas Colombiana (LSC) en entornos no controlados y con beneficiarios distintos. Como se observa en la Figura 3, el sistema se basa en la adquisición de las imágenes a través de una cámara de un celular (Noor et al, 2017) tomando como objetivo principal textos existentes en letreros o avisos, que a través de las adecuadas técnicas de tratamiento de imágenes, identificar y obtener las características más significativas, en este caso del texto que está contenidos en estos objetos: infografías, señalizaciones o anuncios comerciales (Salían et al, 2017), dicha imagen se constituye en una entrada adecuada para una Red Neuronal Artificial (Constante et al, 2016;Badi, Hamza, & Hasan, 2017) que se encarga de la interpretación del texto y de su búsqueda en el banco de significantes a la lengua de señas colombiana LSC.…”

Section: La Tecnología Con Aplicación Social E Incluyenteunclassified

Movilidad y aprendizaje: utilización de la inteligencia artificial para la traducción de textos en LSC

CASTIBLANCO¹,

MONTES²

2021

Espacios

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Dado que las personas sordas se enfrentan a barreras comunicativas por el uso frecuente del castellano, y que los procesos educativos para los sordos presentan deficiencias para crear habilidades de comprensión de letreros y etiquetas de su entorno, un grupo de docentes de la UNAD presenta los resultados de su investigación a la comunidad del país los avances del diseño de un aplicativo móvil que permite al sordo traducir textos en castellano a LSC.

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“…Finally, classification techniques [17] are used for accurately predicting the gesture based on the optimal set of features. However, the existing works [18][19][20] are facing the difficulties of high complexity in operations, inaccurate or over segmentation affecting the performance of classifier, and more time consumption. Hence, the proposed work object is to develop an efficient hand gesture image recognition system by an intelligent image processing methodologies.…”

Section: Introductionmentioning

confidence: 99%

An Intelligent Heuristic Manta-Ray Foraging Optimization and Adaptive Extreme Learning Machine for Hand Gesture Image Recognition

Khetavath¹,

Sendhilkumar²,

Mukunthan³

et al. 2023

Big Data Min. Anal.

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The development of hand gesture recognition systems has gained more attention in recent days, due to its support of modern human-computer interfaces. Moreover, sign language recognition is mainly developed for enabling communication between deaf and dumb people. In conventional works, various image processing techniques like segmentation, optimization, and classification are deployed for hand gesture recognition. Still, it limits the major problems of inefficient handling of large dimensional datasets and requires more time consumption, increased false positives, error rate, and misclassification outputs. Hence, this research work intends to develop an efficient hand gesture image recognition system by using advanced image processing techniques. During image segmentation, skin color detection and morphological operations are performed for accurately segmenting the hand gesture portion. Then, the Heuristic Manta-ray Foraging Optimization (HMFO) technique is employed for optimally selecting the features by computing the best fitness value. Moreover, the reduced dimensionality of features helps to increase the accuracy of classification with a reduced error rate. Finally, an Adaptive Extreme Learning Machine (AELM) based classification technique is employed for predicting the recognition output. During results validation, various evaluation measures have been used to compare the proposed model's performance with other classification approaches.

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New method for optimization of static hand gesture recognition

Cited by 11 publications

References 2 publications

Convolution neural networks for hand gesture recognation

Convolution neural networks for hand gesture recognation

Movilidad y aprendizaje: utilización de la inteligencia artificial para la traducción de textos en LSC

An Intelligent Heuristic Manta-Ray Foraging Optimization and Adaptive Extreme Learning Machine for Hand Gesture Image Recognition

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