基于相位信息和主成分分析的对称性检测方法

对称性检测在图像分析与模式识别中具有重要意义。该文提出一种基于相位信息和主成分分析的对称性检测方法。首先在不同尺度、不同方向上计算相位对称性，其次在每个方向上将所有尺度的相位对称性进行融合，再次利用主成分分析提取各个方向的主要特征，最后利用非极大值抑制和自适应滞后阈值处理得到最终的对称性检测结果。实验证明:该文方法可直接对原始图像进行处理，不需要图像的任何先验知识，对于多目标图像不需分割等任何预处理；可以同时检测亮目标和暗目标的镜像对称、旋转对称、曲线对称；对图像亮度和对比度不敏感。

Symmetry Detection Based on Phase Information and Principal Component Analysis

Symmetry detection plays an important role in image analysis and pattern recognition. Based on phase symmetry and principal component analysis, a new image symmetry detection method is proposed. Firstly, phase symmetry is computed on different scales and orientations. On each orientation, the phase symmetry values of different scales are merged together. And then the main feature of different orientations is extracted by using principal component analysis. Finally, the results of symmetry detection can be obtained by using non-maximal suppression and adaptive hysteresis thresholding. The experiments show that the proposed method can be applied directly to original images and it does not need segmentation or any preprocessing. And it is insensitive to rotation, brightness and contrast. It also can detect mirror symmetry, rotational symmetry and curve symmetry at the same time, whether bright or dark objects.