A fast fixed-point algorithm for independent component analysis
Neural Computation
Independent component analysis: theory and applications
Independent component analysis: theory and applications
Independent component analysis: algorithms and applications
Neural Networks
Edge Detection and Texture Segmentation Based on Independent Component Analysis
ICPR '02 Proceedings of the 16 th International Conference on Pattern Recognition (ICPR'02) Volume 3 - Volume 3
Independent Component Analysis of Textures
ICCV '99 Proceedings of the International Conference on Computer Vision-Volume 2 - Volume 2
Non-negative Matrix Factorization with Sparseness Constraints
The Journal of Machine Learning Research
Pattern Recognition
Robust Face Recognition via Sparse Representation
IEEE Transactions on Pattern Analysis and Machine Intelligence
The particle swarm - explosion, stability, and convergence in amultidimensional complex space
IEEE Transactions on Evolutionary Computation
Fast and robust fixed-point algorithms for independent component analysis
IEEE Transactions on Neural Networks
Face recognition by independent component analysis
IEEE Transactions on Neural Networks
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In this paper, we propose a machine vision approach for detecting local irregular brightness in low-contrast surface images and, especially, focus on mura (brightness non-uniformity) defects in liquid crystal display (LCD) panels. A mura defect embedded in a low-contrast surface image shows no distinct intensity from its surrounding region, and even worse, the sensed image may also present uneven illumination on the surface. All these make the mura defect detection in low-contrast surface images extremely difficult. A set of basis images derived from defect-free surface images are used to represent the general appearance of a clear surface. An image to be inspected is then constructed as a linear combination of the basis images, and the coefficients of the combination form the feature vector for discriminating mura defects from clear surfaces. In order to find minimum number of basis images for efficient and effective representation, the basis images are designed such that they are both statistically independent and spatially exclusive. An independent component analysis-based model that finds both the maximum negentropy for statistical independency and minimum spatial correlation for spatial redundancy is proposed to extract the representative basis images. Experimental results have shown that the proposed method can effectively detect various mura defects in low-contrast LCD panel images.