Segmentation of wood surface defect image based on improved C-V model.
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摘要: 木材表面缺陷会严重影响木材的质量、性能和使用价值,对木材表面缺陷分割检测有利于提高木材的利用率,节约现有木材资源,缓解森林资源短缺的压力。针对传统的C-V(Chan-Vese)模型算法不能分割灰度不均匀图像的缺点,本文采用C-V模型与形态学结合的方法与传统的C-V模型算法进行对比试验。与此同时,根据C-V模型和C-V模型结合形态学方法的不足之处,在C-V模型基础上,引入局部拟合函数和高斯核函数,提出了一种基于C-V模型的改进算法,能够有效地克服C-V模型的不足。通过对木材表面缺陷图像分别采用传统C-V模型算法、C-V模型与形态学结合的方法和改进的C-V模型算法进行多组针对单一目标的木材表面缺陷图像的对比试验。结果表明:C-V模型能够将虫眼和活节缺陷图像分割出来,但是对纹理干扰强烈的死节缺陷图像分割困难;运用C-V模型与形态学结合的方法,可以有效地消除分割结果中的细小空洞和噪声,但是仍无法抵抗死节缺陷图像中木材自身纹理的干扰,难以将死节缺陷完整地分割出来;改进的C-V模型算法对木材表面缺陷图像的分割能够减少迭代次数,缩短分割时间,使分割轮廓线更加光滑和完整。通过采用改进C-V模型算法对多目标木材表面缺陷图像进行试验,能够更好地验证改进算法的优越性、有效性和可行性。Abstract: Wood surface defects can seriously affect the quality, performance and use value of wood; therefore, the detection of wood surface defects is beneficial to improving the utilization of wood, saving the existing wood resources and easing the shortage of forest resources. So it is important to study the method of image segmentation of wood surface defects. Aiming at the shortcomings of the traditional C-V (Chan-Vese) model which cannot segment gray images, we used a combination of C-V model and the morphological method for a comparison with sole C-V model algorithm. Given the weakness of the traditional C-V model or combined with the morphological method, the local fitting function and the Gauss kernel function which are based on the C-V model are introduced. Thus, an improved algorithm based on C-V model is proposed which overcomes the shortcomings of the C-V model. Targeting a single wood surface defect, the segmentation of images has been performed by three algorithms, i.e., the C-V model algorithm, combination of C-V model and morphological method, and our improved algorithm, for a contrast test. The test shows that the C-V model is capable of segmenting the images of wormholes and slipknots, but it is difficult to segment the images of encased knots. With the morphological method, the small holes and noise after segmentation can be effectively eliminated, but it is difficult to split out the defect image segmentation of encased knots completely, and still cannot resist interference of dead knot images of wood texture itself. The segmentation of defect image based on our improved C-V model algorithm is quicker and more accurate, and can reduce the number of iterations, shorten segmentation time and make the segmentation contour more smooth and complete. Our study shows that the improved algorithm is able to finish the multi target experiment, and therefore it is feasible, superior and practicable.
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Keywords:
- wood surface defect /
- wood image segmentation /
- C-V model /
- morphology
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