Matching by pixel distribution comparison: Multisource image template matching
Lichun Mei, Yuanfu Zhao, Huaiye Wang, Caiyun Wang, Jun Zhang, Xiaoxia Zhao · IET Signal Processing · 2022
Abstract Efficient and accurate template matching in multisource images is a difficult task when hardware platform resources are limited. Motivated by the problems encountered in present methods, a template matching scheme, called Matching by Pixel Distribution Comparison (MPDC), is proposed to reduce the use of system resources by combining traditional algorithms and neural networks. The MPDC scheme first extracts the distribution information of image pixels through the developed extended Slice Transform (eSLT) matrix to overcome the non‐linear intensity difference between heterogeneous images. Then the similarity between heterogeneous images is evaluated by comparing the pixel distribution rules reflected by the eSLT matrix. A similarity score table is constructed based on the pixel distribution comparison, and the similarity score between heterogeneous image pairs can be calculated by querying this table. The comparison of eSLT matrices can be completed through either experience scoring or neural network learning. The experimental results show that, on a hardware platform with limited resources, the 64 × 64 template can be matched by sliding on the 256 × 256 query image in 2.85 s, and it only takes at least 19.84 s for neural network training on the CPU. The matching performance is also better than many popular multisource image algorithms.