粒子群优化算法实现仿射不变性形状识别

Affine invariant shape recognition with particle swarm optimization algorithm

针对形状识别过程中,轮廓图像会因为视点改变发生仿射变换的问题,提出了一种新的仿射不变特征提取方法及匹配策略。首先,对所有边界点计算其与质心的距离及方向角,并在给定的角度邻域内进行平均以消除噪声干扰;然后,计算方向角相差180°的边界点质心距离之比作为形状特征,该特征具有仿射不变性。由于仿射变换图像间各轮廓点的方向角具有非线性变化特征,质心距离比需要重采样以建立对应关系,笔者将其转化为一个路径规划问题,并用粒子群优化算法得以解决。实验表明该方法对形状识别中的平移、旋转、缩放、拉伸和噪声干扰具有良好的效果。

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.