Affine invariant shape recognition with particle swarmoptimization algorithm

Focusing on the problem that affine transformation will exist among the contour images due to variation of the viewpoints, a new approach to extract affine invariant features and matching strategy is proposed for shape recognition. First, the centroid distance and azimuth angle of each boundary point are computed. Then, with a prior defined angle interval, all the points in the neighbor region of the sample point are considered to calculate the average distance for eliminating noise. After that, the centroid distance ratios(CDRs) of any two contour points with angle difference of 180° are achieved as the representation of the shape, which would be invariant to affine transformation. Since the angles of contour points changed non linearly among affine related images, the CDRs should be resampled to build corresponding relationship. It could be regarded as an optimization problem of path planning. In our method, a PSO based path planning model is presented to address this problem. The experimental results demonstrate the efficiency of the proposed method in shape recognition with translation, scaling, rotation, distortion and noise interference.