It is of great significance to image de-noising since the image has been the main medium of acquiring and transmitting information in human life. The image is not only destroyed by signal-independent noise, but also destroyed by signaldependent noise largely. In order to fill the gap between stochastic resonance for image processing with signalindependent noise and signal-dependent noise and eliminate the shortcoming of unsatisfactory processing results of image in the traditional image de-noising methods, an algorithm of image de-noising for Gaussian-Gaussian mixed noise based on stochastic resonance is innovatively proposed in this paper. And three main steps involved are image segmentation, add mixed noise to the image and stochastic resonance processing. First, the original image is clustered and segmented to obtain multiple regions. Then, signal-independent Gaussian noise and signal-dependent Gaussian noise are added to the image in sequence. Finally the multiple noisy regions are respectively processing by stochastic resonance. The proposed method is experimented with different noise variance combinations. The experimental results show that the proposed method can achieve higher peak-signal-to-noise (PSNR) and structural similarity (SSIM), and meanwhile the visual effect is also better.
It is of great significance to recognize hand posture since people with hearing and speech disabilities use sign language as the main medium of communication. To eliminate the shortcoming of low recognition rate caused by the redundant features in the traditional 3D hand posture recognition methods, an algorithm of 3D hand posture recognition with space coordinates based on optimal feature selection is proposed in this paper, which innovatively combines with XGBoost method. And three main steps involved are feature extraction, optimal feature selection and posture recognition respectively. Firstly, self-defined attributes and features are extracted from 3D coordinate data collected by Leap Motion Controller. Then, the XGBoost model combined with cross validation is employed to select optimal features from different attributes. Finally, the selected features instead of all extracted features are then fed into Gaussian Naive Bayes classifier to recognize the target posture. The proposed method is experimented on different data sequences containing ten heavily-used postures of Chinese Sign Language. The experimental results show that after processed by optimal feature selection, the proposed method can achieve higher recognition rate than the traditional methods, and reduce the number of training samples by half at the peak recognition rate.