By Andrew Blake
Active Contours offers with the research of relocating photographs - a subject of growing to be value in the special effects undefined. specifically it truly is eager about realizing, specifying and studying earlier types of various power and making use of them to dynamic contours. Its target is to enhance and examine those modelling instruments extensive and inside of a constant framework.
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Extra resources for Active Contours: The Application of Techniques from Graphics, Vision, Control Theory and Statistics to Visual Tracking of Shapes in Motion
However, if the curve momentarily loses lock on the outline it rapidly becomes too tangled to be able to recover. (Figure by courtesy of R. } somewhat freely over time, the tracked curve can rapidly tie itself into unrecoverable knots, as the figure shows. This is a prime example of the sort of insight that can be gained from real-time experimentation. A regular snake, with suitably chosen internal energy may succeed in tracking several dozen frames off-line. However, once tracking is seen as a continuous process, and this is the viewpoint that real-time experiments enforce, the required standards of robustness are altogether more stringent.
BNB-l of NB B-spline functions or basis functions. If the interval [0, L] is taken to be periodic the resulting parametric curve will be closed. 6. 11). 17) n=Q a weighted sum of basis functions with weights q~. 6) on page 45 and, as before, NB = L so that the number of control points of the curve is equal to the number of its spans. 6 Curves with vertices It is often necessary to introduce a vertex or hinge at a certain point along a parametric curve, to fit around sharp corners on an object outline.
The mask is a prototype image, typically of small size, of the feature being sought: for a valley feature, for instance, the mask would be a V-shaped intensity function. The output of the correlation process is a measure of goodness of fit of the prototype to the image, in each of the image locations evaluated. However, feature maps are only the beginning. They enhance features of the desired type but do not unambiguously detect them. Detection requires a decision to be made at each pixel, the simplest decision rule being that a feature is marked wherever feature strength exceeds some preset threshold.
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