Skip to Main Content
In this paper, we propose a Hough transform-based method to identify low-contrast defects in unevenly illuminated images, and especially focus on the inspection of mura defects in liquid crystal display (LCD) panels. The proposed method works on 1-D gray-level profiles in the horizontal and vertical directions of the surface image. A point distinctly deviated from the ideal line of a profile can be identified as a defect one. A 1-D gray-level profile in the unevenly illuminated image results in a nonstationary line signal. The most commonly used technique for straight line detection in a noisy image is Hough transform (HT). The standard HT requires a sufficient number of points lie exactly on the same straight line at a given parameter resolution so that the accumulator will show a distinct peak in the parameter space. It fails to detect a line in a nonstationary signal. In the proposed HT scheme, the points that contribute to the vote do not have to lie on a line. Instead, a distance tolerance to the line sought is first given. Any point with the distance to the line falls within the tolerance will be accumulated by taking the distance as the voting weight. A fast search procedure to tighten the possible ranges of line parameters is also proposed for mura detection in LCD images.