中圖分類號(hào)： TP393
文獻(xiàn)標(biāo)識(shí)碼： A
DOI： 10.19358/j.issn.2096-5133.2021.02.012
引用格式： 李鋒. 基于熵率聚類的超像素機(jī)器視覺與缺陷檢測(cè)算法[J].信息技術(shù)與網(wǎng)絡(luò)安全，2021，40(2)：70-73.

Super pixel machine vision and defect detection algorithm based on entropy rate clustering

Abstract： In intelligent manufacturing, traditional imaging technology can no longer meet the needs of high-precision industry. In this paper, a target segmentation algorithm combining entropy rate clustering was proposed, and the objective function of entropy rate and equilibrium term was established based on the adjacent edge set of hyper pixel. Finally, the optimal hyper pixel set was obtained by optimizing and solving the objective function through greedy heuristic algorithm. A similarity experiment based on Gaussian function was designed to measure the similarity of adjacent pixels, and the relevant parameters were set to test the actual process of industrial manufacturing. The final experimental result shows that the algorithm has a good detection and recognition effect, is obvious in contour and internal fringe recognition, and the overall result is good, which is applicable to the field of industrial manufacturing.

Key words : machine vision；entropy clustering；super pixel；greedy heuristic algorithm