基于改进Lazy Snapping算法的红外图像分割方法研究

张莲; 李梦天; 余松林; 宫宇; 杨洪杰

基于改进Lazy Snapping算法的红外图像分割方法研究

重庆理工大学电气与电子工程学院，重庆 400054

基金项目:

国家自然基金 61402063

详细信息

作者简介:
张莲（1967-），女，重庆人，教授，硕士生导师，主要从事远程测试与控制技术，信号处理等方面的研究，E-mail：zh_lian@cqut.edu.cn

中图分类号: TP391
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- PDF下载量: 38
- 被引次数: 0
出版历程
- 收稿日期: 2020-07-08
- 修回日期: 2020-07-15
- 刊出日期: 2021-04-20

An Infrared Image Segmentation Method Based on Improved Lazy Snapping Algorithm

School of Electrical and Electronic Engineering, Chongqing University of Technology, Chongqing 400054, China

摘要

摘要: 针对红外图像含大量噪声以及对比度低等特点，提出一种结合快速模糊C均值聚类的改进Lazy Snapping分割方法。对红外图像使用快速模糊C均值聚类算法进行预分割，通过形态学骨架提取的方法在图像中标记出目标和背景种子点，将Lazy Snapping算法由全局分割转化为聚类区域分割，并构造能量函数，通过最小割算法求解能量函数的最小值并使分割效率得以提升，减少了图像存在的过分割现象，使Lazy Snapping算法由交互式算法变为非交互式算法，实现了红外图像的自动分割，提高了Lazy Snapping算法的实时性。通过对各类不同红外图像进行分割实验，再与其他分割方法进行性能评价比较，结果表明改进的算法具有良好的分割效果及较强的鲁棒性。
- Lazy Snapping /
- 能量函数 /
- 图像分割 /
- 模糊C均值聚类
Abstract: Considering that infrared images contain a considerable amount of noise and are of low contrast, an improved lazy snapping(LS) segmentation method combined with fast fuzzy C-means clustering is proposed. Infrared images are pre-segmented using a fast fuzzy C-means clustering algorithm, and the target and background seed points are marked in the image by the morphological skeleton extraction method. The LS algorithm is converted from global segmentation to cluster region segmentation, and an energy function is constructed. The minimum value of the energy function is solved by the minimum cut algorithm, and the segmentation efficiency is improved. The phenomenon of over-segmentation in the image is reduced, the LS algorithm is changed from an interactive algorithm to a non-interactive algorithm. Thus, the automatic segmentation of infrared images is realized, improving the real-time nature of the LS algorithm. By performing segmentation experiments on various infrared images and then comparing the proposed method's performance with that of other segmentation methods, the results show that the improved algorithm has a good segmentation effect and strong robustness.
- Lazy Snapping /
- energy function /
- image segmentation /
- fuzzy C-means clustering

HTML全文

图 1 本文算法流程图

Figure 1. Algorithm flow chart in this paper

下载: 全尺寸图片幻灯片

图 2 隔离开关红外图像

Figure 2. Infrared image of isolation switch

下载: 全尺寸图片幻灯片

图 3 出线端红外图像

Figure 3. Infrared images at the outlet

下载: 全尺寸图片幻灯片

图 4 断路器红外图像

Figure 4. Circuit breaker infrared image

下载: 全尺寸图片幻灯片

表 1 各类红外图像分割效果

Table 1. Various infrared image segmentation effects

Image type	Global threshold segmentation		Standard Lazy Snapping		The algorithm of this paper
Image type	IOU	FPR	IOU	FPR	IOU	FPR
Isolation switch	0.8551	0.1177	0.9553	0.0199	0.9765	0.0170
Outlet	0.6037	0.3961	0.8993	0.0935	0.9384	0.0613
Circuit breaker	0.8413	0.0361	0.9353	0.0558	0.9412	0.0531

下载: 导出CSV

表 2 算法运行时间比较

Table 2. Comparison of algorithm running times

Image type	Standard Lazy Snapping	The algorithm ofthis paper
Isolation switch	1.0723	1.1319
Outlet	0.9756	1.0170
Circuit breaker	0.9862	0.9733

下载: 导出CSV

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