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The study of mean shift
smoothing for defect detection
in low-contrast surfaces
and heterogeneous surfaces
ABSTRACT
The mean shift
technique has been an attractive alternative for noise removal, region segmentation
and object tracking in image processing. In this thesis, the feasibility of
mean shift smoothing for defect detection in complicated surfaces is studied.
The proposed methods especially focus on low-contrast non-textured surfaces
such as mura defects (uneven brightness) in TFT-LCD panels, and the
heterogeneous surfaces such as polycrystalline solar wafers.
Mean shift
smoothing involves an iterative procedure that shifts each data point to the
mode of the data points based on a kernel estimator of density. For
non-textured surfaces, two mean shift-based methods are proposed. The first
method shifts each pixel to the mode in the image, and the distance between
the original pixel location and its converged position is used as the
discrimination measure. A defect-free pixel will converge fast in its
neighborhood and results in a small shift, while a defective pixel will need
a larger shift to converge. In order to speed up the computation, a weight
measure that uses the kernel function to calculate the gray-level variation
in the spatial window in one single mean-shift iteration is also proposed for
detecting low-contrast defects. For heterogeneous solar wafers, the
fingerprint and contamination defects are studied. Since the grain edges in
the polycrystalline wafer in a small spatial window show more consistent edge
directions and a defect region presents high variation of edge directions,
the entropy of gradient directions of each pixel in a small neighborhood
window is first calculated to convert the gray-level image into an entropy
image. The mean-shift smoothing procedure is then performed to remove
defect-free grain edges in the entropy image. The preserved edge points in
the resulting image are declared as defective ones. Experimental results have
shown that mean-shift technique can be an effective tool for low-contrast
defect detection in non-textured surfaces. It also performs well for defect
detection in heterogeneous surfaces if the defect features can be adequately
extracted.
Keywords: Machine vision; defect
detection; surface inspection;
mean-shift smoothing; Solar wafer
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