Research on Wavelet Transform Modulus Maxima and OTSU in Edge Detection
Source abstract
Abstract
During routine bridge maintenance, edge detection allows the partial condition of the bridge to be viewed. However, many edge detection methods often have unsatisfactory performances when dealing with images with complex backgrounds. Moreover, the processing often involves the manual selection of thresholds, which can result in repeated testing and comparisons. To address these problems in this paper, the wavelet transform modulus maxima method is used to detect the target image, and then the threshold value of the image can be determined automatically according to the OTSU method to remove the pseudo-edges. Thus, the real image edges can be detected. The results show that the information entropy and SSIM of the detection results are the highest when compared with the commonly used Canny and Laplace algorithms, which means that the detection quality is optimal. To more fully illustrate the advantages of the algorithms, images with more complex backgrounds were detected and the processing results of the algorithms in this paper are still optimal. In addition, the automatic selection of thresholds saves the operator’s effort and improves the detection efficiency. Thanks to the combined use of the above two methods, detection quality and efficiency are significantly improved, which has a good application in engineering practice.
Source: crossref · Quality: baseline
Research context
OpenAlex topics: Image and Signal Denoising Methods · Industrial Vision Systems and Defect Detection · Advanced Image Fusion Techniques
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
Affiliations
Faculty of Land Resources Engineering, Kunming University of Science and Technology, Kunming 650093, China
PLA Army Academy of Artillery and Air Defense, Zhengzhou 450000, China
Funding
Research on Key Technologies of ecological environment monitoring and intelligent management of natural resources in Yunnan
References (49)
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