融合局部特征的图像过渡区提取与阈值化

Image transition region extraction and thresholding based on local feature fusion

针对图像过渡区提取与阈值化问题,提出了一种融合局部灰度复杂度和局部灰度差异度的方法。首先生成图像的局部复杂度和局部差异度等局部灰度特征;其次融合这些局部灰度特征构造新的特征矩阵;然后设计了与特征矩阵的均值和标准差相关的自动特征阈值,并提取图像过渡区;最后将过渡区像素的灰度均值作为最优灰度阈值完成图像二值化。实验结果表明,所提方法的过渡区提取质量高,分割效果好,具有合理性和有效性,可作为经典方法的有效补充。

To select the optimal threshold for image segmentation, a new method based on local complexity and local difference was proposed. Firstly, the local grayscale features of a given image were generated, including local complexity and local difference. Next, the new feature matrix was constructed using local feature fusion. Then, an automatic threshold was defined based on the mean and standard deviation of feature matrix, and the image transition region was extracted. Finally, the optimal grayscale threshold was obtained by calculating the grayscale mean of transition pixels, and the binary result was yielded. The experimental results show that, the proposed method performs well in transition region extraction and thresholding, and it is reasonable and effective. It can be an alternative to traditional methods.