Segmentation of geometric signals using robust fitting

The problem of segmenting an image provided by a geometric sensor into geometric primitives is addressed by a two-step iterative process. In the first step, the largest connected region bounded by edge pixels is hypothesized as containing a geometric primitive. In the second step, the resulting set of pixels is sent to a robust fitter based on the least median of squares algorithm, to verify whether this hypothesis holds. If the fit is successful, then the geometric primitive is removed from the data; otherwise, a different hypothesis is obtained by decreasing the edge threshold. The process is repeated until the entire image is segmented. The fact that the robust fitter is tolerant of outliers means that the correct segmentation is produced from many different initial hypotheses. The algorithm is demonstrated on a number of range images which are known to contain particular geometric primitives