Spatially resolved stereoscopic surface profiling by using a feature-selective segmentation and merging technique
Spatially resolved stereoscopic surface profiling by using a feature-selective segmentation and merging technique
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基于特征选择性分割和合并技术的空间分辨立体表面轮廓测量
DOI：
10.1088/2051-672x/ac5998
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发表时间：
2022-03
期刊：
影响因子：
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通讯作者：
Chabum Lee;Xiangyu Guo
中科院分区：
文献类型：
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作者：
Chabum Lee;Xiangyu Guo
我们提出了一个功能选择性的分割和合并技术，以实现空间分辨的零件表面轮廓的三维立体和频闪立体。一对视觉相机从不同角度捕捉零件的图像，并且可以重建3D立体图像。3D图像的常规滤波过程涉及数据丢失并且降低图像的空间分辨率。在这项研究中，三维重建图像的空间分辨率通过自动识别和分割的原始图像上的特征，局部和自适应地应用超分辨率算法的分割图像的基础上分类的特征，然后合并这些过滤段。在这里，特征被转换成掩模，掩模选择性地分离特征和背景图像以进行分割。实验结果与传统的滤波方法，通过使用高斯滤波器和带通滤波器的空间频率和轮廓精度进行了比较。因此，选择性特征分割技术能够在保留成像特征的同时进行空间分辨的3D立体成像。
We present a feature-selective segmentation and merging technique to achieve spatially resolved surface profiles of the parts by 3D stereoscopy and strobo-stereoscopy. A pair of vision cameras capture images of the parts at different angles, and 3D stereoscopic images can be reconstructed. Conventional filtering processes of the 3D images involve data loss and lower the spatial resolution of the image. In this study, the 3D reconstructed image was spatially resolved by automatically recognizing and segmenting the features on the raw images, locally and adaptively applying super-resolution algorithm to the segmented images based on the classified features, and then merging those filtered segments. Here, the features are transformed into masks that selectively separate the features and background images for segmentation. The experimental results were compared with those of conventional filtering methods by using Gaussian filters and bandpass filters in terms of spatial frequency and profile accuracy. As a result, the selective feature segmentation technique was capable of spatially resolved 3D stereoscopic imaging while preserving imaging features.