Optical pattern recognition image preprocessing based on hybrid cluster intelligent algorithm.
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| Title: | Optical pattern recognition image preprocessing based on hybrid cluster intelligent algorithm. |
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| Authors: | Liu, Manjun1 (AUTHOR) liumanjun@liaodongu.edu.cn |
| Source: | Optical & Quantum Electronics. Apr2024, Vol. 56 Issue 4, p1-24. 24p. |
| Subjects: | Optical pattern recognition, Particle swarm optimization, Image recognition (Computer vision), Ant algorithms, Algorithms |
| Abstract: | Traditional preprocessing methods have problems of low stability and poor universality. To solve these problems, this article conducted effective research on parameter tuning of image preprocessing methods based on the Ant Colony Particle Swarm Optimization (ACPSO) algorithm. This article used peak signal-to-noise ratio (PSNR) and structural similarity index measure (SSIM) as fitness functions. The particle dimension is defined as 2, which represents the size of the median filter and the size of the histogram equalization window. After the iteration, the results of ant colony algorithm and particle swarm optimization (PSO) algorithm were compared, and the parameter with the highest fitness function value was selected as the final preprocessing parameter. The image was preprocessed using the determined optimal parameters. The results showed that the average PSNR and SSIM values of the ACPSO preprocessed images were 5.58 and 0.08 higher than those of traditional preprocessing methods, and the subjective visual evaluation score was also higher. The Otsu's binarization method was used for segmentation. The method of feature extraction using Histogram of Orientated Gradients (HOG) and Local Binary Patterns (LBP), as well as the recognition model using Convolutional Neural Networks (CNN) and Support Vector Machines (SVM), was more stable than traditional preprocessing methods. On the other two datasets, the recognition results of ACPSO preprocessed images performed better and showed better universality compared to traditional preprocessing methods. ACPSO algorithm, as a hybrid swarm intelligence algorithm, can effectively play an effective role in optical pattern recognition image preprocessing, solving the problems of low stability and poor universality of traditional preprocessing methods. [ABSTRACT FROM AUTHOR] |
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| Database: | Engineering Source |
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| Abstract: | Traditional preprocessing methods have problems of low stability and poor universality. To solve these problems, this article conducted effective research on parameter tuning of image preprocessing methods based on the Ant Colony Particle Swarm Optimization (ACPSO) algorithm. This article used peak signal-to-noise ratio (PSNR) and structural similarity index measure (SSIM) as fitness functions. The particle dimension is defined as 2, which represents the size of the median filter and the size of the histogram equalization window. After the iteration, the results of ant colony algorithm and particle swarm optimization (PSO) algorithm were compared, and the parameter with the highest fitness function value was selected as the final preprocessing parameter. The image was preprocessed using the determined optimal parameters. The results showed that the average PSNR and SSIM values of the ACPSO preprocessed images were 5.58 and 0.08 higher than those of traditional preprocessing methods, and the subjective visual evaluation score was also higher. The Otsu's binarization method was used for segmentation. The method of feature extraction using Histogram of Orientated Gradients (HOG) and Local Binary Patterns (LBP), as well as the recognition model using Convolutional Neural Networks (CNN) and Support Vector Machines (SVM), was more stable than traditional preprocessing methods. On the other two datasets, the recognition results of ACPSO preprocessed images performed better and showed better universality compared to traditional preprocessing methods. ACPSO algorithm, as a hybrid swarm intelligence algorithm, can effectively play an effective role in optical pattern recognition image preprocessing, solving the problems of low stability and poor universality of traditional preprocessing methods. [ABSTRACT FROM AUTHOR] |
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| ISSN: | 03068919 |
| DOI: | 10.1007/s11082-023-05910-6 |
