Abstract
The basic MINPRAN (MINimize the Probability of RANdomness)
technique, introduced by C.V. Stewart (1994), is extended to handle
range data taken from complex scenes. Such data often includes: (1) a
large numbers of outliers, (2) points from multiple surfaces
interspersed over large image regions, and (3) extended regions
containing only bad data. The initial version of MINPRAN handles cases
(1) and (3). For (2), given an image region containing data from more
than one surface, the basic technique tends to favor a single fit that
“bridges” two surfaces. We analyze the extent of this
problem and introduce two modifications to solve it. The new version of
the algorithm, called MINPRAN2, produces extremely good results on
difficult range data
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