Haitham Elwahsh^1,*, Ahmed Elkhouly¹, Emad Abouel Nasr², Ali K. Kamrani³, Engy El-shafeiy⁴

CMC-Computers, Materials & Continua, Vol.72, No.3, pp. 6045-6060, 2022, DOI:10.32604/cmc.2022.026309 - 21 April 2022

Abstract

People who are deaf or have difficulty speaking use sign language, which consists of hand gestures with particular motions that symbolize the “language” they are communicating. A gesture in a sign language is a particular movement of the hands with a specific shape from the fingers and whole hand. In this paper, we present an Intelligent for Deaf/Dumb People approach in real time based on Deep Learning using Gloves (IDLG). The approach IDLG offers scientific contributions based deep-learning, a multi-mode command techniques, real-time, and effective use, and high accuracy rates. For this purpose, smart gloves working in

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Chana Chansri, Jakkree Srinonchat^*

CMC-Computers, Materials & Continua, Vol.71, No.3, pp. 4377-4389, 2022, DOI:10.32604/cmc.2022.024026 - 14 January 2022

Abstract Egocentric recognition is exciting computer vision research by acquiring images and video from the first-person overview. However, an image becomes noisy and dark under low illumination conditions, making subsequent hand detection tasks difficult. Thus, image enhancement is necessary to make buried detail more visible. This article addresses the challenge of egocentric hand grasp recognition in low light conditions by utilizing the flex sensor and image enhancement algorithm based on adaptive gamma correction with weighting distribution. Initially, a flex sensor is installed to the thumb for object manipulation. The thumb placement that holds in a different More >