Review of Sub-Pixel Edge Detection Algorithms Based on Machine Vision
This paper provides a detailed overview of sub-pixel edge detection algorithms based on machine vision,aiming to comprehensively summarize the research status and progress in this field.Firstly,the importance of sub-pixel edge detection and its wide applications in image processing and computer vision are introduced.Secondly,different sub-pixel edge detection methods were summarized,including those based on gradient information,model fitting,and least squares method.Then,the advantages and disadvantages of various methods were analyzed,and they were compared and evaluated.Once again,the challenges and research directions faced by current sub-pixel edge detection algorithms were proposed,such as improving the robustness,adaptability,and efficiency of the algorithms.Finally,the research significance and value of the algorithm were emphasized,and future research directions were proposed.
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