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Moreover, the number of matched points was adequate to compute quadratic model parameters.
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The matched points are constrained by two SREs.
It is a fast binary descriptor where good matched points are obtained by computing Hamming distance.
By iterating on each keypoint in X, a set of matched points is obtained.
After the matched feature points are found from the feature detecting and matching processes, the average distance of each matched points is now calculated.
3D information for matched points is obtained from data structure (Table 1) alluded to in the previous section.
Note that in each pair of images only several matched points are connected by lines for a clear vision.
When the sufficient number of matched points is found, particular obstacle is marked as recognized and it is not tested more.
As is apparent from the figure, the slope of the line indicated by the 152 matched points is 1.53544 and negligible differences between actual ground heights and those computed from the x-parallax disparity.
In the LCP, the objective is to find a mapping of the largest cardinality where the RMSD of the matched points is no more than a given threshold (Table 1).
The latter tends to penalize matched zones where every matched point is too distant from the road segment.
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