Comparison of Different Processing Methods of Joint Coordinates Features for Gesture Recognition with a CNN in the MSRC-12 Database

2021 ◽  
pp. 590-599
Author(s):  
Júlia Schubert Peixoto ◽  
Miguel Pfitscher ◽  
Marco Antonio de Souza Leite Cuadros ◽  
Daniel Welfer ◽  
Daniel Fernando Tello Gamarra
Keyword(s):  
Gesture Recognition ◽  
Processing Methods ◽  
Joint Coordinates

Variations in the traditional processing methods of Pentadesma butracea butter in northern Benin

Food Chain ◽  
2014 ◽  
Vol 4 (3) ◽  
pp. 261-274 ◽  
Author(s):  
Eric Badoussi ◽  
Paulin Azokpota ◽  
Yann Madodé ◽  
Polycarpe Kayodé ◽  
Alphonse Dossou ◽  
...  
Keyword(s):  
Processing Methods ◽  
Traditional Processing

Customized Gesture Recognition for Educational Games

2016 ◽  
Vol 3 (2) ◽  
pp. 1
Author(s):  
Seong Jeong ◽  
HongJun Ju ◽  
Hyo-Rim Choi ◽  
TaeYong Kim
Keyword(s):  
Gesture Recognition ◽  
Educational Games

Mobile Robot Operation by Gesture Recognition using Iterative Motion

2015 ◽  
Vol 135 (8) ◽  
pp. 944-953 ◽  
Author(s):  
Kazuki Tanaka ◽  
Hiroyuki Ukida
Keyword(s):  
Mobile Robot ◽  
Gesture Recognition

HUMAN GESTURE RECOGNITION USING BISPECTRUM-BASED WIRELESS SIGNAL PROCESSING

2020 ◽  
Vol 79 (1) ◽  
pp. 47-57
Author(s):  
O. G. Viunytskyi ◽  
A. V. Totsky ◽  
Karen O. Egiazarian
Keyword(s):  
Signal Processing ◽  
Gesture Recognition ◽  
Wireless Signal

Prospective Digital Processing Methods of Multidimensional Signals with the Use of Irregular Meshes

Vestnik MEI ◽  
2019 ◽  
Vol 3 (3) ◽  
pp. 98-107
Author(s):  
Sergey V. Vishnyakov ◽  
◽  
Elizaveta A. Sokolova ◽  
Vitaliy V. Pekhterev ◽  
◽  
...  
Keyword(s):  
Digital Processing ◽  
Processing Methods ◽  
Multidimensional Signals ◽  
Irregular Meshes

GESTURE RECOGNITION SYSTEM FOR NIGERIAN TRIBAL GREETING POSTURES USING SUPPORT VECTOR MACHINE

2020 ◽  
Vol 5 (2) ◽  
pp. 609
Author(s):  
Segun Aina ◽  
Kofoworola V. Sholesi ◽  
Aderonke R. Lawal ◽  
Samuel D. Okegbile ◽  
Adeniran I. Oluwaranti
Keyword(s):  
Support Vector Machine ◽  
Gesture Recognition ◽  
Recognition Rate ◽  
Recognition Task ◽  
Recognition System ◽  
Human Interaction ◽  
Support Vector ◽  
System A ◽  
Extraction Algorithm ◽  
Gaussian Blur

This paper presents the application of Gaussian blur filters and Support Vector Machine (SVM) techniques for greeting recognition among the Yoruba tribe of Nigeria. Existing efforts have considered different recognition gestures. However, tribal greeting postures or gestures recognition for the Nigerian geographical space has not been studied before. Some cultural gestures are not correctly identified by people of the same tribe, not to mention other people from different tribes, thereby posing a challenge of misinterpretation of meaning. Also, some cultural gestures are unknown to most people outside a tribe, which could also hinder human interaction; hence there is a need to automate the recognition of Nigerian tribal greeting gestures. This work hence develops a Gaussian Blur – SVM based system capable of recognizing the Yoruba tribe greeting postures for men and women. Videos of individuals performing various greeting gestures were collected and processed into image frames. The images were resized and a Gaussian blur filter was used to remove noise from them. This research used a moment-based feature extraction algorithm to extract shape features that were passed as input to SVM. SVM is exploited and trained to perform the greeting gesture recognition task to recognize two Nigerian tribe greeting postures. To confirm the robustness of the system, 20%, 25% and 30% of the dataset acquired from the preprocessed images were used to test the system. A recognition rate of 94% could be achieved when SVM is used, as shown by the result which invariably proves that the proposed method is efficient.

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