NDLI: Block cosparsity overcomplete learning transform image segmentation algorithm based on burr model

Content Provider	IET Digital Library
Author	Han, Lili Li, Shujuan Ren, Pengxin Xue, Dingdan
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.
Starting Page	2074
Ending Page	2080
Page Count	7
ISSN	17519659
Volume Number	14
e-ISSN	17519667
Issue Number	Issue 10, Aug (2020)
Alternate Webpage(s)	https://digital-library.theiet.org/content/journals/iet-ipr/14/10
Alternate Webpage(s)	https://digital-library.theiet.org/content/journals/10.1049/iet-ipr.2019.1212
Journal	IET Image Processing
Publisher Date	2020-03-09
Access Restriction	Open
Rights Holder	© The Institution of Engineering and Technology
Subject Keyword	Block Cosparsity Overcomplete Learning Transform Image Segmentation Algorithm Burr Image Processing Burr Model Computer Vision And Image Processing Technique Discrete Cosine Transform Edge Detection High-voltage Copper Contact Burr Image Segmentation Image Denoising Image Recognition Image Segmentation Image Segmentation Evaluation Criterions Integral Transforms K-means Clustering Method Knowledge Engineering Technique Learning in AI Regularisation Coefficient Sparse Overcomplete Transform Matrix Sparse-level Constant Transform Algorithm Weak Edge Information
Content Type	Text
Resource Type	Article
Subject	Signal Processing Electrical and Electronic Engineering Computer Vision and Pattern Recognition Software

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