In our previous work, we presented a segmentation method that combines useful properties of edge and region-based segmentation. In the region-based approach, pixels are classified into 10 surface types according to the spatial properties in the neighborhood of each pixel. Surface differential properties are approximated using least squares estimation. Geometrically coherent regions are formed by grouping connected pixels of the same surface type. A two stage method which detects both step and roof edges is used for edge detection. Preliminary edge and region-based segmentation results are overlaid to achieve the final segmentation. This paper presents our recent results which improve the robustness of the segmentation method. Accurate estimation of the differential properties of the surfaces is essential if one is to gain good segmentation. The least squares estimation with constant coefficient window operators gives good results when only white Gaussian distributed noise occurs, and pixels in the neighborhood are from one statistical population. In order to decrease the influence of very deviant pixel values that occur near region boundaries or due to noise, we implemented two robust estimation methods. One is iterative reweighting least squares method that uses a variable order model and the other is a least trimmed square method. The robust and least squares approaches are compared and their effects on surface classification are reported. Also the validity of the assumptions on the data, model and estimation methods used are considered. Both synthetic and real range images are used for test images.

Date Published: 1 March 1992
PDF: 12 pages
Proc. SPIE 1608, Intelligent Robots and Computer Vision X: Neural, Biological, and 3-D Methods, (1 March 1992); doi: 10.1117/12.135095

Published in SPIE Proceedings Vol. 1608:
Intelligent Robots and Computer Vision X: Neural, Biological, and 3-D Methods
David P. Casasent, Editor(s)