基于局部二值拟合模型的板材表面节子与虫眼的图像分割

为了更好地对板材表面的节子和虫眼进行快速有效的分割，对局部二值拟合（local binary fitting，LBF）模型进行了深入研究，从而提出一个改进的 LBF模型，即在LBF模型的基础上，添加一个新的水平集线性正则化项，与此同时引入一个以高斯函数为核函数的局部二值拟合能量。改进算法能够克服LBF模型的分割缺点，使得分割过程对初始轮廓的大小和位置不敏感，同时增强算法的抗噪性，能够分割出灰度不均匀的图像。经实验验证，该算法可以比较完整地提取出单一目标和多目标的板材节子和虫眼的图像，以及对应得出与缺陷图像相对应的水平集演化图像。图21表1参15

Segmentation of wood surface knots and wormholes based on an improved LBF Model

To make wood surface defect segmentation faster and more effective, research was conducted to put forward an improved LBF （local binary fitting） Model with image segmentation based on the Chan-Vese （CV） Model. The improved LBF Model added a new level set formulation with a linear regularization term, and at the same time formed a Gaussian function as the kernel function with two local values for fitting energy. Results showed that the improved algorithm could overcome the segmentation shortcomings in the LBF Model. Also, the segmentation process was not sensitive to the size or the position of the initial contour. However, the anti noise of the algorithm was enhanced, and the image could be segmented in non-uniform gray. The experiment showed that the algorithm completely extracted the wood surface defect images with single and multi objectives, and level set evolution corresponding to the defect image could be obtained. ［Ch, 21 fig, 1 tab. 15 ref.］