To improve the timeliness of the three-dimensional (3-D) maximum entropy method, an image segmentation method based on equivalent 3-D entropy and artificial fish swarm optimization algorithm is proposed. An equivalent 3-D entropy method without logarithmic operation is developed, and its equivalence is proved theoretically. The optimal threshold is determined based on the artificial fish swarm optimization algorithm so as to avoid exhaustive search and improve algorithm efficiency. The experimental results demonstrate that the proposed method is more time-efficient than the traditional 3-D entropy method and the equivalent 3-D entropy method without affecting segmentation. Compared with the one-dimensional entropy method and the two-dimensional entropy method, it is obviously superior in noise immunity and detail preservation.
The use of machine vision to recognize mature pomegranates in natural environments is of major significance in improving the applicability and work efficiency of picking robots. By analyzing the color characteristics of color images of mature pomegranates under different illumination conditions, the feasibility of the YCbCr color model for pomegranate image recognition under different illumination conditions was proven. First, the Cr component map of pomegranate image is selected and then the pomegranate fruit is segmented by the kernel fuzzy C-means clustering algorithm to obtain the pomegranate image. Contrast experiments of pomegranate image segmentation under different illumination conditions were then performed using the proposed kernel fuzzy C-means clustering algorithm, the fuzzy C-means clustering algorithm, the Otsu algorithm and the threshold segmentation algorithm. Results of the experiments verified the effectiveness and superiority of the proposed algorithm.
Conventional fuzzy clustering algorithms present several disadvantages with respect to image segmentation, including a tendency to arrive at local optima and a relatively high sensitivity to noise and initial cluster centers. To address these issues, we herein propose a kernel-based intuitionistic fuzzy clustering approach combining an improved grey wolf optimizer with a kernel-based intuitionistic fuzzy Cmeans clustering algorithm capable of carrying out differential mutations for image segmentation. The proposed method extracts spatial information from images and then applies a kernel-based intuitionistic fuzzy clustering objective function to improve the robustness of the algorithm against noise. To cope with the initial sensitivity and local optima issues, we developed an improved grey wolf optimizer based on differential mutation for the global optimization of the cluster centers. A comparative optimization assessment against six classic functions revealed that the improved grey wolf optimizer algorithm outperformed the grey wolf optimizer and mean grey wolf optimizer algorithms in terms of searching ability, whereas the improved grey wolf optimizer-kernel-based intuitionistic fuzzy C-means clustering algorithm outperformed the other algorithms in terms of kernel-based intuitionistic fuzzy C-means clustering optimization of the Iris dataset and achieved satisfactory results in terms of segmenting images with various types of noises.
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