基于区域特征的线阵图像分割
Line-Scan CCD Image Segmentation Based on Region Feature
DOI: 10.12677/CSA.2022.125136, PDF,   
作者: 程志鹏:天津商业大学理学院,天津;韩建枫:天津商业大学理学院,天津;天津商业大学信息工程学院,天津
关键词: 线阵图像区域特征图像分割差分法图像质量评价Line-Scan CCD Image Region Feature Image Segmentation Difference Method Image Quality Evaluation
摘要: 图像分割是图像处理中的一个重要问题,也是一个经典难题。图像分割可以降低后续算法的难度和空间复杂度。然而,现有的图像分割方法的阈值难确定、算法复杂、耗时长等问题依然存在。本文针对线阵图像的区域特征,提出一种自适应阈值的图像分割算法。利用线阵图像相对运动和相对静止的物体在图像中差异显著的优良特性,计算线阵图像每行像素的方差。相对运动部分方差差异显著,相对静止部分方差差异较小,以此来实现对线阵图像的分割。
Abstract: Image segmentation is an important and classic problem in image processing. Image segmentation can reduce the difficulty and spatial complexity of subsequent algorithms. However, the existing image segmentation methods still have some problems such as being difficult to determine the threshold, complex algorithm, being time-consuming and others. In this paper, an image segmentation algorithm based on adaptive threshold is proposed. The variance of each row pixel of line-scan image is calculated by using the excellent characteristic that the relative moving object and the relatively stationary object differ significantly in the image. The variance difference of the relative moving part is significant, but the variance difference of the relative stationary part is small, so as to realize the segmentation of the line array image.
文章引用:程志鹏, 韩建枫. 基于区域特征的线阵图像分割[J]. 计算机科学与应用, 2022, 12(5): 1371-1377. https://doi.org/10.12677/CSA.2022.125136

参考文献

[1] 杨凌辉. 基于线阵图像傅里叶与灰度匹配算法的三维测量[J]. 激光与光电子学进展, 2021, 58(20): 319-327.
[2] 俞大海. 基于线阵CCD图像纹理特征的铁路货车货物分割算法研究[J]. 铁道学报, 2013, 35(9): 59-64.
[3] 李良宇. 基于线阵CCD的弹药质量无损检测仿真[J]. 计算机仿真, 2020, 37(7): 4-8.
[4] 董家顺. 基于改进K-means算法的钢管表面缺陷视觉检测方法[J]. 武汉科技大学学报, 2020, 43(6): 439-446.
[5] 乐英. 基于背景差分法的多运动目标检测与分割[J]. 中国工程机械学报, 2020, 18(4): 305-309.
[6] 汤元会. 一种基于图像处理的交通运动目标快速检测方法[J]. 计量学报, 2019, 40(S1): 94-98.
[7] 曹玉东. 基于深度学习的图像质量评价方法综述[J]. 计算机工程与应用, 2021, 57(23): 27-36.