7th Sea Game Masters' Guide by David Leri, Therese Nieson, William O'Connor

By David Leri, Therese Nieson, William O'Connor

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12: MGVF force field on the synthetic circle image. The direction of motion (vx , v y ) is from right to left here (ii). we select the multiplicative inverse of the time-step as λ ≥ 1 + 8µ. 53) Proof. See Appendix F. Let us illustrate the efficacy of MGVF through a couple of examples. 12 shows MGVF field ∇w for the circle image of Fig. 1(a). , the circle is moving in the negative x-direction). The vector field MGVF is quite different from the GVF vector field for the circle shown in Fig. 9(a). A lagging active contour can now be attracted to the circle edges.

2 with the general sequential Bayesian filter computations shown in Fig. 1. 4 CASE STUDY: THE ALPHA–BETA FILTER For the reader who is not an expert in control theory, linear algebra, and stochastic processes, the typical question at this point is How the heck do I use the Kalman filter in an actual tracking application? The typical text leaves the hungry reader unsatisfied at this point. Perhaps, the authors hypothesize, the reason is simple job security. If any bubba could implement the Kalman filter, we’d lose all those lucrative consulting deals .

61). In this case, DP leads to the following optimal value functions: D0 (X0 , Y0 , X2 , Y2 ) = min[E0 (X0 , Y0 , X1 , Y1 ) + E1 (X1 , Y1 , X2 , Y2 )], X1 ,Y1 D1 (X0 , Y0 , X3 , Y3 ) = min[D0 (X0 , Y0 , X2 , Y2 ) + E2 (X2 , Y2 , X3 , Y3 )], .. cls 42 T1: IML December 26, 2005 20:39 BIOMEDICAL IMAGE ANALYSIS: TRACKING Dn−2 (X0 , Y0 , Xn , Yn ) = min [Dn−3 (X0 , Y0 , Xn−1 , Yn−1 ) Xn−1 ,Yn−1 +En−1 (Xn−1 , Yn−1 , Xn , Yn )], Dn−1 = min X0 ,Y0 ,Xn ,Yn [Dn−2 (X0 , Y0 , Xn , Yn ) +En (Xn , Yn , X0 , Y0 )].

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