By Goro Obinata and Ashish Dutta
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Extra resources for Vision Systems - Segmentation and Pattern Recognition
Range image segmentation by curve grouping, In K. Dobrovodský, editor, Proceedings of the 7th International Workshop on Robotics in Alpe-Adria-Danube Region, pages 339--344, Bratislava, June 1998. ASCO Art. Haralick R. , Shapiro L. , Survey: Image segmentation techniques, Computer Vision, Graphics, and Image Processing, vol. 29, pp. 100–132, 1985. Hijjatoleslami S. , Region growing: A new approach, IEEE Transaction on Image Processing, vol. 7, pp. 1079–1084, 1998. Hoffman R. , Jain A. , IEEE Trans.
7. Conclusions We proposed novel fast and accurate range segmentation method based on the combination of range & intensity profile modelling and curve-based region growing. A range profile is modelled using an adaptive simultaneous regression model. The recursive adaptive predictor uses spatial correlation from neighbouring data what results in improved robustness of the algorithm over rigid schemes, which are affected with outliers often present at the boundary of distinct shapes. A parallel implementation of the algorithm is straightforward, every image row and column can be processed independently by its dedicated processor.
Variable Order Surface Fitting (BJ) Algorithm Besl and Jain developed a segmentation algorithm (Besl & Jain, 1988) which uses signs of surface curvature to obtain a coarse segmentation and iteratively refines it by fitting bivariate polynomials to the surfaces. The algorithm begins by estimating the mean and Gaussian surface curvature at every pixel and uses the signs of the curvatures to classify each pixel as belonging to one of eight surface types. The resultant coarse segmentation is enhanced by an iterative region growing procedure.