Compared with grayscale images, color images with color information have more advantages in target recognition, detection and tracking, but the color of the image can be easily affected to be distorted by light. Aiming at solving the problem of color distortion, a method based on wavelet transform is proposed to eliminate the influence of illumination. Firstly, the V-channel components in HSV space are decomposed by wavelet and the illumination component in the image scene is estimated by reconstructing the approximate coefficients of wavelet decomposition. Secondly, according to the imaging principle model, the illumination component is removed from the original image, and the reflection component that characterizes the color information is preserved. Finally, the normalized distribution function is used to normalize the reflection components, and the image color recovery is achieved in RGB space. The experimental results show that the proposed method can reduce the influence of non-uniform illumination on the image to restore the color and improve the image quality.
Hand gesture has been considered a natural, intuitive and less intrusive way for Human-Computer Interaction (HCI). Although many algorithms for hand gesture recognition have been proposed in literature, robust algorithms have been pursued. A recognize algorithm based on the convolutional neural networks is proposed to recognize ten kinds of hand gestures, which include rotation and turnover samples acquired from different persons. When 6000 hand gesture images were used as training samples, and 1100 as testing samples, a 98% recognition rate was achieved with the convolutional neural networks, which is higher than that with some other frequently-used recognition algorithms.