Gesture image segmentation with Otsu’s method based on noise adaptive angle threshold

By analyzing the essence and deficiency of the improved Otsu’s method, this paper proposes a noise adaptive angle threshold based Otsu’s method for gesture image segmentation. It first designs a two-dimensional histogram of gray value-neighborhood truncated gray mean to avoid the interference of extreme noise by discarding the extremes of the neighborhood. Then, the probability that the pixel is noise is calculated according to the actual situation, adaptive filtering is implemented to enhance the algorithm’s universal applicability. It finally converts the threshold space to an angle space from 0° to 90°, and the threshold search range is compressed to improve its efficiency. As the gesture is close to the background and the boundary is blurred, this paper combines the global and local Otsu’s method to segment the gesture images based on the angle space. On the one hand, it uses the global Otsu’s method to obtain the global threshold t1. On the other hand, it uses the local Otsu’s method to obtain the local threshold t2, and segments gesture images based on t2. Experimental results show that the proposed method is effective and can accurately segment gesture images with different noises.