Point inclusion testing is to test the relationship of a fixed point and a polygon, an algorithm for this problem is presented.
A ray was drawn from the fixed point, the ray may have crossing point with the edge of the polygon, two vectors were
given from the fixed point to the endpoints of the edge which has crossing point with the ray, then calculate their vector
product. Two functions were defined according to the vector product, the decision for point inclusion testing was based
on this two functions. calculate the vector product for each edge that has crossing point with the ray from the fixed point,
If the amount of positive vector product and negative vector product are the same, the point is outside the polygon;
otherwise, the point is inside the polygon. This method can decrease computing time and can avoid some mistakes of
other algorithm, and the main part of algorithm only needs 5n times subtraction, 2n times multiplication, n times
*judgement, its algorithm complexity is 0(n). this algorithm is suitable for some other cases including self-intersection
polygon and polygon with hole.
Traditional camera lens calibrations need a 3D physical model with many control points on it, the coordination of these
points are measured high precision. These points should be evenly distributed in space for calibration precision, and it is
difficult to make the model especially big model. In this paper, a calibration plate was made and the plate was moved in
its normal direction, parallel, no rotation, it is implemented to provide large area 3D control points with variable Z values,
the images of the plate were made in different places. the moving plate is a 3D model, moreover, it could increase
control points significant, the plate is much easier to make than traditional 3D physical model, the plate could be big one,
the calibration parameters of camera lens were calculated by different control points group, get the mean value of these
parameters. This method is proved high precision, stabilization and robustness. Experiments show such an approach is
effective for reconstructing 3D objects in computer vision system.