Brain-inspired visual recognition algorithm with memory based on NCC feature matching

Human’s brain can recognize objects, scenes and other objects in a short time with high accuracy. However, the existing machine learning algorithms may fail to recognize objects due to the slight changes in the image, mainly because existing machine learning algorithms focused on extracting high-level features from low-level features of objects layer by layer in the recognition process, and cannot extract high-level features directly from the images of observed objects. A algorithm using Normalized Cross Correlation (NCC) as the region matching part could be proposed which establishes a kind of brain visual recognition memory model simulates and analyzes the speed, the recognition rate and robustness of image gray level change in order to examine the feasibility of the proposed algorithm, and provide new directions for subsequent recognition algorithms.