This paper introduces an imaging technique to enhance three-dimensional (3D) surface profiling of the machined part by using a feature-selective segmentation (FSS) and merging technique. Spatially-resolved 3D stereoscopic images were achieved compared with those of the conventional filtering-based imaging process. Two identical vision cameras simultaneously take images of the parts at different angles, and 3D images can be reconstructed by stereoscopy algorithm. High-pass and low-pass filtering of the images involves data loss and lowers the spatial resolution of the image. In this study, the 3D reconstructed image resolution was significantly improved by automatically classifying and selectively segmenting the features on the 2D 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 measurement system scanned the machined part with various shape and height information. The experimental results were compared with those of a conventional high-pass and low-pass filtering method 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. The proposed imaging method will be implemented with strobo-stereoscopy for in-process 3D surface imaging.

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