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
Independent component analysis for noisy data: MEG data analysis
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
Face recognition using independent component analysis and support vector machines
Pattern Recognition Letters - Special issue: Audio- and video-based biometric person authentication (AVBPA 2001)
Adaptive Noise Smoothing Filter for Images with Signal-Dependent Noise
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
Expert Systems with Applications: An International Journal
Financial time series forecasting using independent component analysis and support vector regression
Decision Support Systems
Fast support vector data descriptions for novelty detection
IEEE Transactions on Neural Networks
Expert Systems with Applications: An International Journal
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In this paper, we propose a convolution filtering scheme for detecting small defects in low-contrast uniform surface images and, especially, focus on the applications for backlight panels and glass substrates found in liquid crystal display (LCD) manufacturing. A defect embedded in a low-contrast surface image shows no distinct intensity from its surrounding region, and even worse, the sensed image may present uneven brightness on the surface. All these make the defect detection in low-contrast surface images extremely difficult. In this study, a constrained independent component analysis (ICA) model is proposed to design an optimal filter with the objective that the convolution filter will generate the most representative source intensity of the background surface without noise. The prior constraint incorporated in the ICA model confines the source values of all training image patches of a defect-free image within a small interval of control limits. In the inspection process, the same control parameter used in the constraint is also applied to set up the thresholds that make impulse responses of all pixels in faultless regions within the control limits, and those in defective regions outside the control limits. A stochastic evolutionary computation algorithm, particle swarm optimization (PSO), is applied to solve for the constrained ICA model. Experimental results have shown that the proposed method can effectively detect small defects in low-contrast backlight panels and LCD glass substrate images.