Xm. Yu et al., ROBUST ESTIMATION FOR RANGE IMAGE SEGMENTATION AND RECONSTRUCTION, IEEE transactions on pattern analysis and machine intelligence, 16(5), 1994, pp. 530-538
This correspondence presents a segmentation and fitting method using a
new robust estimation technique. We present a robust estimation metho
d with high breakdown point which can tolerate more than 80% of outlie
rs. The method randomly samples appropriate range image points in the
current processing region and solves equations determined by these poi
nts for parameters of selected primitive type. From K samples, we choo
se one set of sample points that determines a best-fit equation for th
e largest homogeneous surface patch in the region. This choice is made
by measuring a RESidual Consensus (RESC), using a compressed histogra
m method which is effective at various noise levels. After we get the
best-fit surface parameters, the surface patch can be segmented from t
he region and the process is repeated until no pixel left. The method
segments the range image into planar and quadratic surfaces. The RESC
method is a substantial improvement over the least median squares meth
od by using histogram approach to inferring residual consensus. A gene
tic algorithm is also incorporated to accelerate the random search.