Fast template matching in multi-modal image under pixel distribution mapping

Abstract

A scheme that can achieve fast template matching by both traditional methods and neural network methods, called Hybrid Matching by Pixel Distribution Mapping (HMPDM), is proposed for the matching difficulty caused by the problem of nonlinear intensity differences between multi-modal images. The HMPDM scheme first extracts the distribution information of image pixels through the developed expanded Slice Transform (eSLT) matrix, to overcome the nonlinear intensity differences between visible and thermal infrared images; and then maps the eSLT matrices corresponding to different modal images into correlation surface images with the same modality through traditional integer mapping mechanism or neural network mapping mechanism; finally, template matching between visible and thermal infrared images is achieved by evaluating the similarity of correlation surface images through the Zero-mean Normalized Cross-Correlation (ZNCC) algorithm. The fast and classic ZNCC algorithm ensures the real-time performance of the HMPDM scheme in the most time-consuming sliding window matching stage. The traditional integer mapping mechanism and the neural network mapping mechanism enable the HMPDM scheme to use the traditional method and the neural network method alone to achieve template matching, or to combine the two methods to achieve template matching. The comprehensive utilization of traditional methods and neural network methods makes the HMPDM scheme adaptable to various platforms with limited hardware resources. The experimental results show that, on a hardware platform with limited resources, it only takes 0.18 s for a 64 × 64 template image to slide on a 256 × 256 query image to achieve template matching; and it only takes at least 1.2 s for neural network training on the CPU. The matching performance is also better than many popular multi-modal image template matching algorithms.