摘要
针对浮选泡沫图像分割中传统分水岭算法的分割误差问题,研究结合SoftEdge模型与改进的分水岭算法,首先对泡沫图像进行高斯低通滤波降噪,再利用SoftEdge模型提取软边缘,从而削弱光噪声对边缘检测的干扰,进而采用基于前置背景标记技术优化的分水岭算法,通过精确提取前景与背景标记,指导分水岭算法在限定区域内执行分割,显著减少了分割误差现象。研究结果表明,该方法规避了对先验知识和复杂参数的依赖,并大幅提升了分割精度。
In order to address the segmentation errors of the traditional watershed algorithm in flotation foam image segmentation,this study integrates the SoftEdge model with an improved watershed algorithm.First,Gaussian low-pass filtering is applied to denoise the foam images.Then,the SoftEdge model is used to extract soft edges,thereby reducing the interference of light noise in edge detection.Subsequently,a watershed algorithm optimized with foreground-background markers is adopted.By accurately extracting these markers,the algorithm performs segmentation only within predefined regions,which significantly reduces segmentation errors.The results demonstrate that this method avoids reliance on prior knowledge and complex parameter settings,while substantially improving segmentation accuracy.
作者
卢才武
曹越
刘迪
江松
李冠东
张泽家
赵旭阳
LU Caiwu;CAO Yue;LIU Di;JIANG Song;LI Guandong;ZHANG Zejia;ZHAO Xuyang(School of Resource Engineering,Xi'an University of Architecture and Technology,Xi'an 710055,China;Xi'an Key Laboratory of Intelligent Industrial Perception,Computing,and Decision-Making,Xi'an 710055,China;China ENFI Engineering Corporation,Beijing 100000,China;School of Land and Resources Engineering,Kunming University of Science and Technology,Kunming 650005,China;Luanchuan Longyu Molybdenum Industry Co.,Ltd.,Luanchuan 471500,China)
出处
《金属矿山》
北大核心
2025年第8期158-164,共7页
Metal Mine
基金
国家自然科学基金项目(编号:52404140)
陕西省自然科学基金项目(编号:S2023-JC-QN-0687)
陕西省社会科学基金项目(编号:2023R035)。
关键词
浮选泡沫图像分割
SoftEdge模型
改进分水岭算法
前景背景标记技术
image segmentation of flotation froth
SoftEdge model
improved watershed algorithm
foreground and background marking techniques
