Defect detection system for quality assurance using automated visual inspection
First Claim
1. A quality assurance method for inspecting a surface of an object for defects against a texture background in uniform and textured web products using automated visual inspection comprising the steps of:
- imaging a web material to obtain an image;
detecting, automatically, at least one frequency component associated with the background texture of a reference image;
applying a pre-designed set of real Gabor function based filters associated with the at least one frequency component and a mask parameter to evaluate features of the image under inspection and generate a plurality of derived images corresponding to the pre-designed set of real Gabor function based filters;
estimating energy in a local region by computing a local energy function from at least one of the plurality of derived images to generate a blob-detecting image;
combining at least two blob-detecting images with the aid of an image fusion rule to obtain a resultant image; and
thresholding the resultant image to further segment any possible defect(s).
1 Assignment
0 Petitions
Accused Products
Abstract
A defect detection method and system for the automated visual inspection of web materials is provided. The invention utilize real Gabor function (RGF) filters with a non-linear function for estimating the local energy. An example method for quality assurance using automated visual inspection in accordance with the invention includes the steps of: automated design of a bank of RGF filters; using these RGF filters to sample the features of image under inspection; using a non-linear function to compute local energy estimate in the filtered images; combining all the filtered images using image fusion; and finally thresholding the resultant image to segment the defects in the inspection image. Possible embodiments of the described invention include detection of only a class of defects using a single tuned real Gabor filter or a bank of real Gabor functions.
37 Citations
36 Claims
-
1. A quality assurance method for inspecting a surface of an object for defects against a texture background in uniform and textured web products using automated visual inspection comprising the steps of:
-
imaging a web material to obtain an image;
detecting, automatically, at least one frequency component associated with the background texture of a reference image;
applying a pre-designed set of real Gabor function based filters associated with the at least one frequency component and a mask parameter to evaluate features of the image under inspection and generate a plurality of derived images corresponding to the pre-designed set of real Gabor function based filters;
estimating energy in a local region by computing a local energy function from at least one of the plurality of derived images to generate a blob-detecting image;
combining at least two blob-detecting images with the aid of an image fusion rule to obtain a resultant image; and
thresholding the resultant image to further segment any possible defect(s). - View Dependent Claims (2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15)
-
-
16. A method for quality assurance in uniform and textured web products with known yarn density using automated visual inspection comprising the steps of:
-
imaging a web material to obtain an image of a surface;
applying a pre-designed Gabor filter to the image to generate a filtered image;
applying a nonlinear function to a pixel in the filtered image to generate a resultant image; and
thresholding the resultant image to segment at least one defect. - View Dependent Claims (17, 18, 19, 20, 21, 22, 23)
-
-
24. A method for quality assurance in uniform and textured web products with known yarn density using automated visual inspection comprising the steps of:
-
imaging a defect-free web material by generating a set of pixels corrected for illumination irregularities to obtain an image;
selecting at least one frequency component associated with the background texture of the image;
designing at least one real Gabor function based filter using the at least one frequency component associated with the background texture of the image; and
computing, automatically, a threshold associated with the at least one real Gabor function based filter using the image. - View Dependent Claims (25, 26, 27, 28, 29, 30)
-
-
31. A method for quality assurance in the uniform and textured web products using automated visual inspection comprising the steps of:
-
imaging a defect-free web material by generating a set of pixels corrected for illumination irregularities to obtain an image;
detecting, automatically, a first frequency component associated with the background texture of the image from a set of pixels in the warp direction and a second frequency component associated with the background texture of the image from a set of pixels in the weft direction;
selecting a highest frequency component following comparison of at least the first and the second frequency components;
designing at least one real Gabor function based filter using the selected frequency component associated with the background texture of the image; and
computing, automatically, a threshold associated with the at least one real Gabor function based filter using the image. - View Dependent Claims (32, 33, 34, 35)
-
-
36. A system for automatically detecting defects against a textured background in a surface of a material, the system comprising:
-
image acquisition means;
at least one processor configured to execute the steps for detecting, automatically, at least one frequency component associated with the background texture of a reference image, applying a pre-designed set of real Gabor function based filters associated with the at least one frequency component and a mask parameter to evaluate features of the image under inspection and generate a plurality of derived images corresponding to the pre-designed set of real Gabor function based filters, estimating energy in a local region by computing a local energy function from at least one of the plurality of derived images to generate a blob-detecting image, combining at least two blob-detecting images with the aid of an image fusion rule to obtain a resultant image, and thresholding the resultant image to further segment any possible defect(s); and
a memory for storing at least one mean and standard deviation value for a blob detecting image, and at least one threshold based on a defect-free image.
-
Specification