基于L1L0层分解模型的家具死节缺陷图像分割

Dead Knot Defect Image Segmentation in Finished Furniture Surface Based on L1L0 Decomposition Model and Active Contour Model

针对成品家具中的表面死节缺陷,提出一种基于L1L0层分解模型与主动轮廓模型的死节缺陷图像分割算法。将RGB彩色图像转换成Lab图像,对Lab各通道灰度图进行L1L0两层分解,得到分解后的低频灰度图与高频系数图;并用主动轮廓模型对各低频灰度图进行分割得到Lab多通道的二值图。最后对多通道的二值图进行比较,选择b通道二值图作为图像最终分割结果,得到死节缺陷目标。试验结果表明,提出的算法能降低复杂背景噪声,分割效果好,能很好地提取家具表面死节缺陷,性能优于传统的Otsu方法。

Aiming at the surface defect of dead knot in finished furniture,an image segmentation algorithm based on L1L0 decomposition model and active contour model was proposed.The RGB color image was transformed into Lab image,and the Lab channel gray images were decomposed by L1L0 two-layer decomposition,hence the decomposed low-frequency gray images and high-frequency coefficients were obtained.Finally,the multi-channel binary image obtained by segmenting these gray images with active contour model was compared.The b-channel binary image was selected as the final segmentation result and the dead-knot defect target was attained.The experimental results show that the proposed algorithm can reduce the noise of complex background,and has good segmentation effect,which can extract the dead knot defects of furniture surface well,and have better performance than the traditional Otsu method.