纺织学报 ›› 2009, Vol. 30 ›› Issue (04): 45-49.

• 纺织工程 • 上一篇    下一篇

混纺纱截面图像分析中的光斑扩散模型

陶晨   

  • 收稿日期:1900-01-01 修回日期:1900-01-01 出版日期:2009-04-15 发布日期:2009-04-15

Facula diffusion model for analysis of blended yarn cross-section images

TAO Chen   

  • Received:1900-01-01 Revised:1900-01-01 Online:2009-04-15 Published:2009-04-15

摘要: 针对传统的轮廓跟踪算法只能应用在二值图像上,无法处理目标间相互溶结的现象,提出基于人眼视觉原理的光斑扩散模型,并从逼近、贴近、径长突变和命中率4个方面阐述了其控制参数。该模型直接运行在灰度图像上,能够自动分离相互溶结的目标。在光斑扩散模型的基础上,构建了面积系数、异形度和波动强度3个新的特征指标,分别用于描述目标形状的尺寸大小、与圆形偏离的程度以及边缘的粗糙程度。结果表明,新的模型可以准确地提取纤维截面的轮廓,在此基础上构建的特征指标能够有效地反映目标形状特征。

Abstract: In allusion to the traditional contour tracing arithmetic which can only work on binary images and is not able to handle the interlinking phenomena among objects, this paper introduces facula diffusion model based on the principle of human vision, and illustrates its controlling parameters including approximation, closing, length-limiting and hit-rate. This model works directly on grayscaled images and can automatically separate the interlinked objects. Taking advantage of the facula diffusion model, three new feature indices including acreage, abnormity and fluctuation are composed to describe the object shape in its dimension, the departure from roundness and the roughness of its contour respectively. It turns out that the new model can extract the contour of fiber cross-section accurately and the feature indices built on this model can reflect the shape characters of the object in an effective way.

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