Affiliation:
1. Department of Fundamental Subjects, Wuchang Shouyi University, Wuhan 430064, China
Abstract
In order to study the needs of identifying rock thin-section samples by manual observation in the field of geology, a method of electrochemical intelligent recognition of mineral materials based on superpixel image segmentation is proposed. The image histogram of this method can be used to represent the distribution of each pixel value of the image. This interval is consistent with the number of pixels in the method. And using the experiment, the CPU used in the experiment is Intel® Core™ i7-8700 3.2 GHz, the memory is 16 GB, and the GPU is NVIDIA GeForce GT × 1080 Ti, which ensures the accuracy of the experiment. Based on all the experimental results, it can be seen that after the two-stage processing of the designed superpixel algorithm and the region merging algorithm, the final sandstone slice image segmentation results are close to the results of manual labeling, which is helpful for the subsequent research on sandstone component identification. The feasibility of this method was verified.
Reference28 articles.
1. Image segmentation based on multiscale fast spectral clustering
2. River planform extraction from high-resolution sar images via generalized gamma distribution superpixel classification;O. A. Pappas;IEEE Transactions on Geoscience and Remote Sensing,2020
3. Superpixel segmentation based on grid point density peak clustering;C. Xianyi;IEEE Transactions on Geoscience and Remote Sensing,202 1
4. Deep-learning-based automatic mineral grain segmentation and recognition;L. Ghazanfar;IEEE Transactions on Geoscience and Remote Sensing,202 2
5. Image segmentation algorithm based on superpixel clustering
Cited by
3 articles.
订阅此论文施引文献
订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献