摘要
针对目标三维重构图像处理算法要求背景单一、对实验环境依赖性较大的弊端,提出了一种复杂背景下目标三维重构的图像预处理算法。通过对所采集的图像进行高斯滤波、伽马变换以及直方图均衡化处理,去除图像噪点,对复杂背景图像进行抑制,最大程度地强化目标细节信息。融合Grab cut算法与Deeplab算法,解决Grab cut耗时而Deeplab边缘模糊的问题,有效地实现了目标与复杂背景的分离。搭建了针对小汽车模型的实验平台,共获取16组目标图像,验证了该算法的可行性。以2组目标为例,对比了所提算法与传统三维重构图像预处理算法的效果:所提算法的分割准确性为0.9986,灵敏度为0.9889,特异性为0.9991,均高于传统方法;与传统算法点云噪点率为22.7%相比,所提算法的点云噪点率降低为1.15%;所提算法的平均重构耗时为2.245s,是传统算法耗时的60.6%。由此证明了所提图像预处理方法在复杂背景下的三维重构中具有更好的效果。
For three-dimensional (3D) reconstruction of targets, existing image-processing algorithms require a single background, and they significantly depend on the experimental environment. Therefore, an image- preprocessing algorithm for 3D reconstruction of targets in complex backgrounds is proposed. First, to maximize the target detail information, Gaussian filtering, Gamma conversion, and histogram-equalization processing are performed on the acquired images to remove image noise and suppress complex backgrounds. Then, the Grab cut and Deeplab algorithms are combined to solve the problems of long time consumed on Grab cut and edges blurred on Deeplab, effectively separating the target from complex backgrounds. A test platform for the car model is built and sixteen sets of target images are obtained to verify the algorithm. Considering two sets of targets as examples, the effects of the proposed algorithm and the traditional 3D-reconstruction image-preprocessing algorithm are compared. The segmentation accuracy of the proposed algorithm is 0. 9986, the sensitivity is 0.9889, and the specificity is 0.9991,which are higher than those of the traditional algorithm. The point-cloud noise rate of the traditional algorithm is 22.7%, which is reduced to 1. 15% by the proposed algorithm. The average reconstruction time of the proposed algorithm is 2.245 s, which is 60. 6 % of the time of the traditional algorithm. These results prove that the proposed image-preprocessing algorithm offers superior 3D image reconstruction under complex backgrounds.
作者
方雅媚
王红军
黄矿裕
周伟亮
刘磊
邹湘军
Fang Yamei;Wang Hongjun;Huang Kuangyu;Zhou Weiliang;Liu Lei;Zou Xiangjun(College of Engineering, South China Agricultural University,Guangzhou, Guangdong 510642,China)
出处
《激光与光电子学进展》
CSCD
北大核心
2019年第13期110-118,共9页
Laser & Optoelectronics Progress
基金
广东省公益与能力建设项目(2016A010102013)
