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 ICA
(independent component analysis) 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.
¡@
|