David Eberly

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2 Oriented Bounding Boxes 5 2.1 Separation of OBBs . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5 2.2 Testing for Intersection of OBBs . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7 2.2.1 Stationary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7 2.2.2(More)
This is the usual introduction to least squares fit by a line when the data represents measurements where the y–component is assumed to be functionally dependent on the x–component. Given a set of samples {(xi, yi)}i=1, determine A and B so that the line y = Ax + B best fits the samples in the sense that the sum of the squared errors between the yi and the(More)
A classic problem in computer graphics is to decompose a simple polygon into a collection of triangles whose vertices are only those of the simple polygon. By definition, a simple polygon is an ordered sequence of n points, ~ V0 through ~ Vn−1. Consecutive vertices are connected by an edge 〈~ Vi, ~ Vi+1〉, 0 ≤ i ≤ n − 2, and an edge 〈~ Vn−1, ~ V0〉 connects(More)
5 Area of Intersecting Ellipses 5 5.1 No Intersection Points . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5 5.2 One Intersection Point . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7 5.3 Two Intersection Points . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7(More)
3 Quaternion Representation 5 3.1 Axis-Angle to Quaternion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5 3.2 Quaternion to Axis-Angle . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5 3.3 Quaternion to Matrix . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6 3.4(More)