Range Image Segmentation Via Distance Slicing

Jia-Guu Leu; Ishwar K. Sethi

doi:10.1117/12.942764

19 February 1988 Range Image Segmentation Via Distance Slicing

Jia-Guu Leu, Ishwar K. Sethi

Proceedings Volume 0848, Intelligent Robots and Computer Vision VI; (1988) https://doi.org/10.1117/12.942764
Event: Advances in Intelligent Robotics Systems, 1987, Cambridge, CA, United States

Abstract

In this paper we present a method to segment a range image into regions which correspond to different object surfaces in the scene. We first obtain an equidistance contour map of the range image from slicing the range image at fixed increment distance values. Pixels along a contour are all at about the same distance from the sensor. We have observed that whenever a contour crosses an object surface edge, we would see direction discontinuity, curvature discontinuity, curvature zero-crossing, or termination of the contour. We call these places the critical points of the contour. We divide a contour into segments at its critical points. Next, we find the two corresponding contour segments on two consecutive slices. Every pair of corresponding contour segments defines a small region in the range image. Thus through registering contour segments in consecutive slices we have partitioned a range image into many small regions. Each region corresponds to a portion of an object surface. The last step is to merge these small regions into larger areas based on whether or not the corresponding scene surface segments of two adjacent regions have similar orientations in the 3-D space. The range image segmentation process is completed when the merging process is done. This approach is fast because it analyzes only the pixels along the equidistance contours and the entire process can be completed in just one pass.

Citation Download Citation

Jia-Guu Leu and Ishwar K. Sethi "Range Image Segmentation Via Distance Slicing", Proc. SPIE 0848, Intelligent Robots and Computer Vision VI, (19 February 1988); https://doi.org/10.1117/12.942764

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KEYWORDS

Image segmentation

Sensors

Image processing

Computer vision technology

Image sensors

Machine vision

Robot vision

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