首页|Investigators at North China University of Science and Technology Detail Findings in Support Vector Machines (Pellet Image Segmentation Model of Superpixel Feature-based Support Vector Machine In Digital Twin)
Investigators at North China University of Science and Technology Detail Findings in Support Vector Machines (Pellet Image Segmentation Model of Superpixel Feature-based Support Vector Machine In Digital Twin)
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NETL
NSTL
Elsevier
Researchers detail new data in Support Vector Machines. According to news reporting out of Tangshan, People’s Republic of China, by NewsRx editors, research stated, “A digital twin model based on superpixel features is established to solve the problem of noise and similar gray values between foreground and background of pellet images. With superpixel as the basic unit of segmentation, the influence of single pixel on segmentation results is reduced, and allows for higher segmentation accuracy.” Funders for this research include Natural Science Foundation of Hebei Province, Basic Research Funds for Universities. Our news journalists obtained a quote from the research from the North China University of Science and Technology, “The gray-level co-occurrence matrix is used to represent the superpixel characteristic information, and the color moment and gray level distribution are combined to comprehensively characterize the superpixel. Through principal component analysis and correlation analysis, The feature compression of superpixel is realized, and the computational efficiency is improved. The superpixel binary classification data set is built, and the multidimensional feature information of superpixel is extracted as input vector to train the binary classification model of SVM, and the image segmentation problem is transformed into foreground and background classification problem. A multi-scale superpixel segmentation boundary optimization method is proposed to further refine the boundary region of foreground and background. A four-neighborhood search algorithm is proposed to reduce the missegmentation rate of edge superpixels. Experimental results show that the accuracy of the proposed method can reach 95.87%, the precision of image edge segmentation is high, and the foreground and background of granular image are accurately segmented.”
TangshanPeople’s Republic of ChinaAsiaEmerging TechnologiesMachine LearningSupport Vector MachinesVector MachinesNorth China University of Science and Technology
NETL
NSTL
Elsevier
