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Fig. 11.3 The plane normal
vector by three vertices of a
triangle
Fig. 11.4 Evaluating the
planarity by two triangles
space based on selection of excessive samples in order to reduce the influence of
outliers for accurate plane detection. Compared to the portion of the surface with
outliers, the parameters of the plane are much more consistent. Many samplings
improve the accuracy but the processing time is also much increased. And when
the size of plane is small, it is difficult to find the plane peak in Hough parameter
space.
The normal vector of a triangle in PDC is inscribed in a small circular region
so that the normal vector passes through the circumcenter area of the triangle
(see Fig. 11.3 ).
The triangle is rotationally sampled with respect to the center position of the
circular region, and a series of inscribed triangles having different normal vectors
are generated. The direction vectors of these generated triangles are normalized
and the median direction of the normal vector is then used to test the planarity.
If the planarity of the circular region is lower than a given threshold, the point
won't be voted into the Hough parameter space. Therefore, this method can filter
noises and outliers instead of voting them into the Hough parameter space and it is
effective for detection of local regions. But it cannot detect global plane because
the method calculates the planarity at a single point. So we use the PDC as a
filtering method to reduce the excessive sampling of RHT.
We compare the normal direction of a few planes which are determined by
three vertices of a triangle and the others rotated with respect to this triangle (see
Fig. 11.4 ). If the angle between two normal directions is lower than a given
threshold it is voted into Hough parameter space. The local evaluation for the
planarity acts to filter noises and outliers that are not voted into Hough parameter
space. Therefore, there are only a few data samplings, resulting in less computing
time than PDC.
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