Download Active Contours: The Application of Techniques from by Andrew Blake PDF

By Andrew Blake

Active Contours bargains with the research of relocating photographs - an issue of transforming into value in the special effects undefined. specifically it really is fascinated with realizing, specifying and studying previous versions of various energy and using them to dynamic contours. Its goal is to increase and examine those modelling instruments extensive and inside of a constant framework.

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Additional resources for Active Contours: The Application of Techniques from Graphics, Vision, Control Theory and Statistics to Visual Tracking of Shapes in Motion

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Knots" at s =,0,1,2,3,4 mark transitions between polynomial segments of the function. (bottom) In the regular case which has evenly spaced knots (at integral values of s), each B-spline basis function is a translated copy of the previous one. 3. 5 '. ". 3: B-spline basis functions 2 3 4 5 s Bn (s) are weighted by coefficients Xn (top) and combined linearly to form a spline function x(s) (bottom). Note that the spline function follows the "control polygon" closely. Spline curves 45 By convention, B-spline basis functions are constructed in such a way that they sum to 1 at all points: NB-I L Bn(s) = 1 for all s.

Hence a quadratic function a+bx+cx 2• has order d = 3. Its degree - the highest power of x - is 2. A. 1: Image edges represented as parametric spline curves. 1 B-spline functions B-splines are a particular, computationally convenient representation for spline functions. In the B-spline form, a spline function x( s) is constructed as a weighted sum of NB basis functions (hence 'B'-splines) Bn(s), n = 0, ... ,NB - 1. In the simplest ("regular") case, each basis function consists of d polynomials each defined over a span of the s-axis.

13) and would be heptadiagonal for cubic splines. Armed with the inner product, some approximation problems become straightforward. The simplest tutorial example is the problem of approximating a spline function represented as QX in terms of two other spline functions Qf, Q~ . 9: Spline approximation: QX is the least-squares approximation of a spline QX in terms of two other splines Qf, Q~ . The solution can be concisely expressed in terms of inner products - see text. 9. A development of this method appears later when, in the interests of economy and of stability over time, it is required to express spline curves in terms of a relatively small number of parameters.

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