In this paper, we propose a shape representation and description well adapted to pattern recognition, particularly in the
context of affine shape transformations. The proposed approach operates from a single closed contour. The
parameterized contour is convolved with a Gaussian kernel. The curvature is calculated to determine the inflexion
points and the main significant ones are kept by using a threshold defined by observing a segment-length between two
curvature zero-crossing points. Then this filtered and simplified shape is registered with the original one. Finally, we
separately calculate the areas between the two segments corresponding to these two scale-space representations. The
proposed descriptor is a vector with components issued for each segment and the corresponding area. This article
develops the new concepts: 1) compares the same segment under different scales representation; 2) chooses the
appropriate scales by applying a threshold to the shape shortest-segment; 3) then proposes the algorithm and the
conditions of merging and removing the short-segments. An experimental evaluation of robustness under affine
transformations is presented on a shape database.
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