1 February 1992 Heuristic segmentation of range images
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Noise and digitization effects cause real concern in segmentation of 3-D images. There is a need for a technique which takes care of these effects without increasing the computational effort to a great extent. In this paper we present a new computationally efficient 3-D object segmentation technique suitable for noisy images. The technique is based on detection of edges in the image. The edges can be classified as belonging to one of the four categories: fold edges; semi-step edges; boundary edges; and smooth edges. The 3-D image is sliced to create equidepth contours (EDCs). Four types of critical points are extracted from the EDCs which indicate edge regions in the 3-D image. Sparse reliable edge pixels are extracted first using the critical points obtained from the EDCs. The edges are grown from these reliable edge pixels through the application of some proposed masks. The constraints of the masks can be adjusted depending on the noise present in the image. The total computational effort stays at a reduced level as the masks are applied only in the small neighborhood of critical points (edge regions), rather than on all the pixels in the scene. Further, the algorithm can be run in parallel as edge growing from different edge regions can be carried out independently of each other.
© (1992) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
M. Arif Wani, M. Arif Wani, Bruce G. Batchelor, Bruce G. Batchelor, "Heuristic segmentation of range images", Proc. SPIE 1607, Intelligent Robots and Computer Vision X: Algorithms and Techniques, (1 February 1992); doi: 10.1117/12.57066; https://doi.org/10.1117/12.57066


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