Machine Vision Image Processing Based on Smooth Denoising Block K-means Algorithm

Abstract

Aiming at the characteristics of low signal-to-noise ratio and high interference of background noise in robot vision target image, the smooth denoising method of Markov random field (MRF) model is used to preprocess the image. On this basis, K-means clustering algorithm is used to cluster the image. Target regions with different features are classified to provide a basis for further target recognition and tracking. At the same time, in order to overcome the defect of slow visual processing speed in the navigation process of mobile robot, the image is divided into blocks. The average value, variance and maximum value of each image block are extracted as eigenvalues to improve the processing speed of the algorithm.