On‐line print‐defect detecting in an incremental subspace learning framework

The purpose of this paper is to propose a novel on‐line print‐defect detecting approach.

The proposed method uses incremental principal component analysis (IPCA) to model a variety pattern with respect to the detected image itself.

The algorithm is constructed and deployed to a real‐time detecting print‐defect system, and the test results show that the system reduces false alarms dramatically.

The paper describes groundbreaking work which, for the first time in the printing industry, uses IPCA in relation to print‐defect detecting.