Best-Performing Color Space for Land-Sea Segmentation

Toure, Seynabou; Diop, Oumar; Kpalma, Kidiyo; Maiga, Amadou Seidou

doi:10.1109/tsp.2018.8441437

Cited by 4 publications

(1 citation statement)

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“…There are plenty of models employed for skin detection such as RGB (Red, Green, and Blue), YCbCr (Luminance Chrominance) and HSV (Hue, Saturation Value). However, our system adopted HSV model which offer better results for segmentation applications than other methods [27,28].…”

Section: Skin Detectionmentioning

confidence: 99%

Smart hand gestures recognition using K-NN based algorithm for video annotation purposes

Alksasbeh

Alomari

Alqaralleh

et al. 2021

IJEECS

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<span>Sign languages are the most basic and natural form of languages which were used even before the evolution of spoken languages. These sign languages were developed using various sign "gestures" that are made using hand palm. Such gestures are called "hand gestures". Hand gestures are being widely used as an international assistive communication method for deaf people and many life aspects such as sports, traffic control and religious acts. However, the meanings of hand gestures vary among different civilization cultures. Therefore, because of the importance of understanding the meanings of hand gestures, this study presents a procedure whichcan translate such gestures into an annotated explanation. The proposed system implements image and video processing which are recently conceived as one of the most important technologies. The system initially, analyzes a classroom video as an input, and then extracts the vocabulary of twenty gestures. Various methods have been applied sequentially, namely: motion detection, RGB to HSV conversion, and noise removing using labeling algorithms. The extraction of hand parameters is determined by a K-NN algorithm to eventually determine the hand gesture and, hence showing their meanings. To estimate the performance of the proposed method, an experiment using a hand gesture database is performed. The results showed that the suggested method has an average recognition rate of 97%. </span>

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Section: Skin Detectionmentioning

confidence: 99%