针对相似细粒度背景下容易出现强对比度纹理误提取的问题，提出一种两通道纹理图像无监督提取算法。通过对图像水平、垂直方向梯度场的非线性扩散，在不改变空间目标边界位置的前提下获取主纹理的边缘结构与区域灰度特征；同时建立包含调整项与模糊因子的两通道纹理提取主动轮廓模型，以具有较大差异的特征为主导项驱动曲线演化，并采用水平集方法实现对强对比度纹理的无监督提取。实验结果表明，该算法对多种自然纹理的提取具有较高的准确性和计算效率。%To reduce common inaccuracies in the segmentation of high-contrast textures from fine-grained back-grounds, a two-channel unsupervised approach for texture extraction was proposed in this paper. Without chang-ing the locations of target boundaries, edges and regional gray-scale features of high-contrast texture were ac-quired through the nonlinear diffusion of pixel intensities, horizontal gradients and vertical gradients of the image. In addition, a two-channel active contour model that contains fuzzy information and adjustment factor was estab-lished. In this model, the feature with larger differences between textures was taken as the leading term to drive the evolution of segmentation curves. Moreover, the level-set method was employed to achieve unsupervised ex-traction. Experimental results on various textures demonstrated the improvements in terms of segmentation per-formance and computational efficiency.
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