Scale-controlled area difference shape descriptor

10 pagesIn 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 enclosed area equal parameterized contour is convolved with a Gaussian kernel. The curvature is calculated to determine the inflexion points and 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 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