Mechanical parts range-image segmentation by locating critical points of one-dimensional curves

Ji-Chun Lee; Kuang-Chao Fan

doi:10.1117/12.232254

21 February 1996 Mechanical parts range-image segmentation by locating critical points of one-dimensional curves

Ji-Chun Lee, Kuang-Chao Fan

Proceedings Volume 2665, Machine Vision Applications in Industrial Inspection IV; (1996) https://doi.org/10.1117/12.232254
Event: Electronic Imaging: Science and Technology, 1996, San Jose, CA, United States

Abstract

A modified edge-based segmentation algorithm specially designed for mechanical parts is proposed. The technique used is based on a concept of 3-views of engineering drawings and is a partial parallel algorithm. Mechanical parts considered here are composed of planes, cylindrical and spherical surfaces. At first, a set of critical points is extracted from each row and column of range image by one dimensional curves' segmentation technique. Edge linking process is performed on the map of critical points by morphological dilation, thinning and edge tracking. After that, a connected component labeling procedure is done and all pixels belonging to the same 4-connected region are assigned a unique label. An efficient run-length implementation of local table method is used to do the connected components' analysis. Finally, a robust least squares surface fitting is employed for each label to accommodate the error of previous steps, and outliers are discarded according to errors. Experiments are presented for numerous scenes of both real and synthetic range images of mechanical parts including concave and convex surfaces, noiseless and noisy. The results show that the proposed one dimensional critical point locating method segments the range image of mechanical parts fast and accurately.

Citation Download Citation

Ji-Chun Lee and Kuang-Chao Fan "Mechanical parts range-image segmentation by locating critical points of one-dimensional curves", Proc. SPIE 2665, Machine Vision Applications in Industrial Inspection IV, (21 February 1996); https://doi.org/10.1117/12.232254

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KEYWORDS

Image segmentation

Image processing

Image processing algorithms and systems

3D image processing

Binary data

Pattern recognition

Signal to noise ratio

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