Each image shows one distinct species on a uniform background. Every image is processed to recover the boundary contour, which is then represented by three global shape parameters and the maxima of the curvature zero-crossing contours in its 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. The features recovered from a CSS image for matching are the maxima of its zero-crossing contours. The matching of two CSS images consists of finding the optimal horizontal shift of the maxima in one of the CSS images that would yield the best possible overlap with the maxima of the other CSS image. Note that this technique was compared to moments and Fourier descriptors , and found to perform better. The top left figure above shows a sample image from the database.