Block cosparsity overcomplete learning transform image segmentation algorithm based on burr model. Issue 10 (8th July 2020)

• Record Type: Journal Article
• Authors: Han, Lili; Li, Shujuan; Ren, Pengxin; Xue, Dingdan
• Is Part Of: IET image processing. Volume 14:Issue 10(2020)
• Page Start: 2074
• Page End: 2080
• Publication Date: 2020-07-08
• Subjects: image denoising -- image segmentation -- discrete cosine transforms -- edge detection -- learning (artificial intelligence); burr model -- high‐voltage copper contact burr image segmentation -- burr image processing -- sparse overcomplete transform matrix -- transform algorithm -- weak edge information -- regularisation coefficient -- sparse‐level constant -- discrete cosine transform -- k‐means clustering method -- block cosparsity overcomplete learning transform image segmentation algorithm -- image segmentation evaluation criterions; Image processing -- Periodicals; 621.36705
• DOI: 10.1049/iet-ipr.2019.1212
• Languages: English
• ISSNs: 1751-9659

Abstract

Abstract : 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.