Abstract
Image segmentation is a key problem in the field of machine vision. Its core goal is to separate the target and background in the region of interest from the image and directly affect the accuracy of subsequent operations such as target recognition and image understanding. In the past decades, there have been many good image segmentation algorithms. In recent years, the deep learning method represented by deep learning has made great progress in the field of image segmentation. In this paper, some commonly used image segmentation algorithms based on machine learning were reviewed, and their theoretical and experimental studies were carried out. In this paper, the application prospect of machine learning in image segmentation was prospected. The existing image segmentation methods are mainly divided into the following categories: threshold-based segmentation methods, region-based segmentation methods, edge-based segmentation methods, and segmentation methods based on specific theories. In recent years, with the rapid progress of computer vision technology, the requirements for the accuracy of object image edge information segmentation have become increasingly high. The main reason for image segmentation is to better obtain object information. However, due to interference conditions such as lighting and noise, image blurry edge information segmentation has become the most difficult point in the development of computer vision technology. In the comparative experiment of algorithms, the results showed that in the training set, the response time of Deep Neural Network (DNN) algorithm, Cluster Analysis (CA) algorithm, and Support Vector Machine (SVM) algorithm was 13.72 s, 16.88 s and 17.29 s when the number of samples was 150. In the test set, when the sample number was 50, the recognition rate of DNN algorithm was 93.7%; the recognition rate of CA algorithm was 87.9%; the recognition rate of SVM algorithm was 84.3%. Therefore, the research of image fuzzy edge information segmentation based on computer vision and machine learning is essential.
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The data that support the findings of this study are available from the corresponding author upon reasonable request.
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Funding
This research was supported by the Science and Technology Department of JilinProvince [20210302009NC http://kjt.jl.gov.cn], and the Science and Technology Bureau of Changchun City [21ZGN27 http://kjj.changchun.gov.cn
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Tianye Luo and Shijun Li designed the experiments, collected data for the number of trajectory points, trained, performed the characterization modeling, and wrote the first draft of the paper.
Ji Li and Jie Guo critically reviewed the method used and contributed to structuring the paper.
Ruilong Feng and Ye Mu implemented the proposed prototype, ran the experiments for the performance study.
Tianli Hun, Yu Sun, Ying Guo and He Gong collected data for experimental comparison usage and drew the curve.
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Luo, T., Li, S., Li, J. et al. Image Fuzzy Edge Information Segmentation Based on Computer Vision and Machine Learning. J Grid Computing 21, 56 (2023). https://doi.org/10.1007/s10723-023-09697-4
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DOI: https://doi.org/10.1007/s10723-023-09697-4