{"title":"基于模糊超像素中心和非线性扩散滤波的主动轮廓模型实例分割","authors":"Yiyang Chen;Fuzheng Zhang;Guina Wang;Guirong Weng;Daniele Fontanelli","doi":"10.1109/TIM.2025.3573369","DOIUrl":null,"url":null,"abstract":"The significant weaknesses of the active contour model (ACM) are the manual setting of contour and the inability to process images with complex information, which limits its efficiency and application scope. In this article, an ACM, called FSC&NDF, is combined with fuzzy superpixel centers (FSCs) and nonlinear diffusion filter (NDF) to solve the above two problems simultaneously. YOLOv9 is adopted to locate the superpixels of interest; the joint boundaries of these superpixels are set as the initial contour, which is close to the morphological features of the target. Improved fuzzy superpixel clustering is applied to extract image features and yield superpixel centers, and the clusters are integrated into the main body of the energy function, NDF module further enhances boundary positioning and suppresses noise. In addition, the proposed connection mechanism makes it possible to convert object detection to instance segmentation. Experimental results show that FSC&NDF overcomes the limitations of previous ACMs in all aspects and its FPS, AP, AP50, and <inline-formula> <tex-math>$\\mathrm {AP}_{M}$ </tex-math></inline-formula> are higher than mainstream deep learning algorithms. The platform experiment based on the telecentric lens further proves the practicality of FSC&NDF.","PeriodicalId":13341,"journal":{"name":"IEEE Transactions on Instrumentation and Measurement","volume":"74 ","pages":"1-13"},"PeriodicalIF":5.6000,"publicationDate":"2025-06-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"An Active Contour Model Based on Fuzzy Superpixel Centers and Nonlinear Diffusion Filter for Instance Segmentation\",\"authors\":\"Yiyang Chen;Fuzheng Zhang;Guina Wang;Guirong Weng;Daniele Fontanelli\",\"doi\":\"10.1109/TIM.2025.3573369\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"The significant weaknesses of the active contour model (ACM) are the manual setting of contour and the inability to process images with complex information, which limits its efficiency and application scope. In this article, an ACM, called FSC&NDF, is combined with fuzzy superpixel centers (FSCs) and nonlinear diffusion filter (NDF) to solve the above two problems simultaneously. YOLOv9 is adopted to locate the superpixels of interest; the joint boundaries of these superpixels are set as the initial contour, which is close to the morphological features of the target. Improved fuzzy superpixel clustering is applied to extract image features and yield superpixel centers, and the clusters are integrated into the main body of the energy function, NDF module further enhances boundary positioning and suppresses noise. In addition, the proposed connection mechanism makes it possible to convert object detection to instance segmentation. Experimental results show that FSC&NDF overcomes the limitations of previous ACMs in all aspects and its FPS, AP, AP50, and <inline-formula> <tex-math>$\\\\mathrm {AP}_{M}$ </tex-math></inline-formula> are higher than mainstream deep learning algorithms. The platform experiment based on the telecentric lens further proves the practicality of FSC&NDF.\",\"PeriodicalId\":13341,\"journal\":{\"name\":\"IEEE Transactions on Instrumentation and Measurement\",\"volume\":\"74 \",\"pages\":\"1-13\"},\"PeriodicalIF\":5.6000,\"publicationDate\":\"2025-06-02\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"IEEE Transactions on Instrumentation and Measurement\",\"FirstCategoryId\":\"5\",\"ListUrlMain\":\"https://ieeexplore.ieee.org/document/11021557/\",\"RegionNum\":2,\"RegionCategory\":\"工程技术\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q1\",\"JCRName\":\"ENGINEERING, ELECTRICAL & ELECTRONIC\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"IEEE Transactions on Instrumentation and Measurement","FirstCategoryId":"5","ListUrlMain":"https://ieeexplore.ieee.org/document/11021557/","RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"ENGINEERING, ELECTRICAL & ELECTRONIC","Score":null,"Total":0}
An Active Contour Model Based on Fuzzy Superpixel Centers and Nonlinear Diffusion Filter for Instance Segmentation
The significant weaknesses of the active contour model (ACM) are the manual setting of contour and the inability to process images with complex information, which limits its efficiency and application scope. In this article, an ACM, called FSC&NDF, is combined with fuzzy superpixel centers (FSCs) and nonlinear diffusion filter (NDF) to solve the above two problems simultaneously. YOLOv9 is adopted to locate the superpixels of interest; the joint boundaries of these superpixels are set as the initial contour, which is close to the morphological features of the target. Improved fuzzy superpixel clustering is applied to extract image features and yield superpixel centers, and the clusters are integrated into the main body of the energy function, NDF module further enhances boundary positioning and suppresses noise. In addition, the proposed connection mechanism makes it possible to convert object detection to instance segmentation. Experimental results show that FSC&NDF overcomes the limitations of previous ACMs in all aspects and its FPS, AP, AP50, and $\mathrm {AP}_{M}$ are higher than mainstream deep learning algorithms. The platform experiment based on the telecentric lens further proves the practicality of FSC&NDF.
期刊介绍:
Papers are sought that address innovative solutions to the development and use of electrical and electronic instruments and equipment to measure, monitor and/or record physical phenomena for the purpose of advancing measurement science, methods, functionality and applications. The scope of these papers may encompass: (1) theory, methodology, and practice of measurement; (2) design, development and evaluation of instrumentation and measurement systems and components used in generating, acquiring, conditioning and processing signals; (3) analysis, representation, display, and preservation of the information obtained from a set of measurements; and (4) scientific and technical support to establishment and maintenance of technical standards in the field of Instrumentation and Measurement.