However, this approach is misguided since on a free-form contour, features usually exist at different scales. The segmentation technique described here is a multi-scale one and is based on the Curvature Scale Space image. Intuitively, curvature is a local measure of how fast a planar contour is turning. Contour evolution is achieved by first parametrizing using arclength. This involves sampling the contour at equal intervals and recording the 2-D coordinates of each sampled point.
Next step is to compute curvature on each smoothed contour.
As a result, curvature zero-crossing points can be recovered and mapped to the CSS image in which the horizontal axis represents the arclength parameter on the original contour, and the vertical axis represents the standard deviation of the Gaussian filter. In doing so, movement of curvature zero-crossings across scales must be accomodated. This step is repeated until the contour becomes simple and convex. This section describes a robust system for object recognition with occlusion based on the multi-scale segmentation technique described in the previous section. No constraints were imposed on object shapes.