An Efficient 2D Curve Matching Algorithm under Affine Transformations

Most of the existing works on partially occluded shape recognition are suited for Euclidean transformations. As a result, the performance would be degraded in the affine and perspective transformation. This paper presents a new estimation and matching method of the 2D partially occluded recognition under affine transformation including translation, rotation, scaling, and shearing. The proposed algorithm is designed to estimate the motion between two open 2D shapes based on an affine curve matching algorithms (ACMA). This ACMA considers the normalized affine arc length coordinated to the 2D contour. Then, it will correlate them in order to minimize the L2 distance according to any planar affine transformation by means of a method based upon a pseudo-inverse matrix. Experiments are carried on the Multiview Curve Dataset (MCD). They demonstrate that our algorithm outperforms other methods proposed in the state-of-the-art.

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