Unsupervised-Learning-Based Feature-Level Fusion Method for Mura Defect Recognition

Mura defect recognition has long been a challenging task in displays, such as the liquid-crystal display (LCD), organic light-emitting diode display and polymer light-emitting diode display. In this paper, we propose an unsupervised-learning-based feature-level fusion approach for mura defect recognition. The approach is known as a joint-feature-representation-based defect recognition framework method. This method concentrates on obtaining effective and sufficient features for mura defects by fusing handcrafted and unsupervised-learned features in a complementary manner. To demonstrate the performance, several experiments are carried out to compare this method with some widely used feature extraction approaches. Experimental results show that the proposed method is more robust and accurate. They also indicate that it is compatible with different unsupervised-learning-based algorithms and handcrafted feature descriptors. Finally, the proposed method is implemented in the vision inspection equipment for recognizing mura defects in thin-film-transistor-LCD panels. It exhibits high robustness and improves the recognition performance by nearly 20% compared with the traditional handcrafted feature descriptors.