Orthogonal polynomial embedded image kernel

Preprocessing operations of images and video frame sequences are beneficial in computer vision algorithms. For example, smoothing frames is used to eliminate noise; while computing frame gradient in x-direction and y-direction is used for frame feature extraction or for finding frame edges. Such operations involve convolving operators (image kernels) with an image precomputing moments will add extra computation cost to computer vision algorithm. In case of video, the computational time accumulatively increased because of the convolution operation for each frame is performed. To overcome this problem, a mathematical model is established for computing preprocessed frame moments via embedding the operator (image kernel) in the orthogonal polynomial (OP) functions. The experimental results show that the computation time for feature extraction using the proposed method is noticeably reduced in the both trends: image size and moment selection order. The average speed up ratio of the proposed method to traditional method is 3x, 5x, 8x, and 40x for moment selection ratio 100%, 25%, 10%, and 5%, respectively. In addition, the percentage reduction in processing time for small image size is ~ 99% and for large image size is ~ 40%.