To improve the performance of the high-voltage copper contact burr image segmentation, a block cosparsity overcomplete learning transform image segmentation algorithm based on burr model is proposed in this study. In this study, k -means clustering method is used to initialise the clustering results; the authors found the algorithm is very effective for burr image processing in production process and the sparse overcomplete transform matrix is initialised by discrete cosine transform. The algorithm is expressed by a set of transforms. When the set of transforms is fixed, the penalty is corresponding to the condition number. A new burr model is proposed in this study. The parameters of the burr are the factors on infection of the sparse-level constant and the regularisation coefficient of the block cosparsity overcomplete learning transform algorithm. The algorithm divides all pixels into several groups. To evaluate the performance of the model, a large number of experiments have been carried out, and three image segmentation evaluation criterions have been used to evaluate the effectiveness of the algorithm. Experimental results show that this method is excellent in retaining weak edge information and avoiding the influence of three-dimensional structure compared with other algorithms.

中文翻译：

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基于毛刺模型的块稀疏超完备学习变换图像分割算法

为了提高高压铜接触毛刺图像分割的性能，提出了一种基于毛刺模型的块稀疏超完备学习变换图像分割算法。在这个研究中，ķ -均值聚类方法用于初始化聚类结果；作者发现该算法对于生产过程中的毛刺图像处理非常有效，并且稀疏的不完全变换矩阵是通过离散余弦变换初始化的。该算法由一组变换表示。当变换集固定时，罚则对应于条件编号。在这项研究中提出了一种新的毛刺模型。毛刺的参数是影响稀疏级常数的影响因素和块稀疏过完全学习变换算法的正则化系数。该算法将所有像素分为几组。为了评估模型的性能，已进行了大量实验，并使用三个图像分割评估标准来评估算法的有效性。实验结果表明，与其他算法相比，该方法在保留弱边缘信息和避免三维结构影响方面表现优异。