Improved small curved arm image segmentation method for sign language images

Abstract

The invention discloses an improved small curved arm image segmentation method for sign language images. The method comprises: reading an input sign language image, performing YCbCr color space conversion, and performing skin color clustering and segmentation to obtain a skin color region in the image; smoothing processing is carried out through median filtering, burrs or white points around a skin color area are removed, maximum threshold binaryzation is carried out, and holes of a sign language area are filled with an image area filling algorithm; processing by adopting area operator and centroid positioning to obtain hand-arm area and method for judging position of cutting line by using transverse distance and the complete segmentation of the hand area is realized through the positioning of the arm area cutting line. According to the method, the transverse distance is used for judging the position of the cutting line for fine segmentation, the cutting line between the arms is positioned, complete segmentation of the hand area is achieved, the problem of segmentation of the arm area is solved, the method is suitable for segmentation of the small bent arm, and the arm removing method has good robustness.

Description

technical field [0001] The invention relates to an image segmentation method, in particular to an improved small curved arm image segmentation method for sign language images. Background technique [0002] Gesture segmentation is a key step in the process of gesture recognition, and ensuring a good segmentation effect is an important condition for accurate recognition of sign language. How to accurately segment gestures from complex backgrounds is always a difficult point, mainly because the gesture environment is easily affected by factors such as lighting. Currently common gesture segmentation techniques include segmentation based on contour information, segmentation based on skin color information, and segmentation based on motion information. [0003] There are many methods of gesture segmentation, but in the actual segmentation process, it is difficult for a single method to meet the requirements of speed, accuracy and fitness at the same time. In practical application...

AI Technical Summary

Problems solved by technology

[0003] There are many methods of gesture segmentation, but in the actual segmentation process, it is difficult for a single method to meet the requirements of speed, accuracy and fitness at the same time. In practical applications, multi-method joint segmentation is usually combined with the advantages of various algorithms in the application scene

The main challenges of current gesture segmentation are as follows: 1) The skin-like region in the gesture image will affect the result of gesture segmentation; 2) The existing arm removal method is mainly designed based on the narrow wrist and wide palm and arm

This method is not applicable when there is an upper arm in the sign language image and the arm is bent to a large extent

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