A dual evaluation multi‐scale template matching algorithm based on wavelet transform

Abstract

It is difficult to achieve the requirements simultaneously of real‐time operation and accuracy when the template matching method encounters the problems of rotation and scale invariance. This letter proposes a dual‐evaluation multiscale template‐matching algorithm based on wavelet transform. First, the image grids generated from a strengthened edge image based on the wavelet transform are defined to reduce the region for detecting feature points. Then, an evaluation strategy based on gradient direction entropy is proposed to evaluate the local pixel for detecting local candidate points that contain rich information. Another evaluation strategy, based on the dominant gradient direction and the uniform LBP model, has been proposed to extract rotation‐invariant feature pixels. To conduct fast and robust matching, the proposed strategies extract fewer feature points with rich local information. The experimental results demonstrate that the proposed method is robust to rotation and scale invariance and achieves real‐time accuracy.

Fault Diagnosis Method of Rotating Machinery Based on Collaborative Hybrid Metaheuristic Algorithm to Optimize VMD

“…For example, Chen et al proposed an improved VMD method based on fractional Fourier transform (FRFT), which makes VMD more sensitive to periodic pulses, so that the early fault features of rolling bearings can be extracted [8]. Zhu et al improved the WT method by using the double evaluation multiscale template matching algorithm, which greatly improved the operation speed and performance of the WT method [9]. Chen et al combined EMD with deep neural network (DNN) and proposed an EMD-DNN method for acceleration signal noise reduction, which achieved better results in acceleration data baseline correction [10].…”