A Recognition Method of Restricted Hand Shapes in Still Image and Moving Image Hand Shapes in Still Image and Moving Image as a Man-Machine Interface Speaker.
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A Recognition Method of RestrictedA Recognition Method of Restricted Hand Shapes in Still Image and Moving Image Hand Shapes in Still Image and Moving Image
as a Man-Machine Interfaceas a Man-Machine Interface
Featuring a simple user interface, this paper presents a simple recognition algorithm of restricted hand shapes from an image taken by only a (not multi-) camera.
The proposed method can be divided into two parts: one is the hand region extraction process hand region extraction process from an input imagefrom an input image; another is the hand shape hand shape recognition process from the extracted image.recognition process from the extracted image.
In the hand shape recognition process, we first make a mask image from the extracted hand region, and we recognize hand shapes based on the image with uneven hand surface by using the mask.
The effectiveness of the proposed method is evaluated by recognition success rate and computation time.
Here we prepare an ellipse curve that crosses all the fingers. The number of fingers can be detected from the pixel value R on the ellipse curve. Then hand shapes can be recognized by the angle of fingers from List point.
In the case of the grayscale background, average recognition rate was 96.8%.
Note that the recognition cannot be successful when the background contains skin color. That problem still remains as one of future studies.
The recognition rate by the method of Ref. [3] was 87.9% for grayscale, and 83.1% for color images. The proposed algorithm achieves higher recognition rate, the reason would be that Ref. [3] did not employ normalization process.
we see that the total recognition processing time is about 30[ms] at most, which can be considered as fast enough processing time to be used as hand shape recognition system.
The processing time was around several 10 of milliseconds which can be regarded enough to recognize a hand shape. So, this method enables real-time hand shape recognition.
The present algorithm could recognize 9 hand shapes at the accuracy of 96.8% for the case of grayscale backgrounds.
Cannot be successful when the background contains skin color.