Single step coarse registration and inspection of circular objects
First Claim
1. A method of inspecting a circular object to effect angular displacement determination and defect detection, using a machine vision system capable of storing a train-time image of a standard sample of said circular object and capturing a run-time image of said circular object to compare against train-time pixel information, said method comprising the steps of:
- providing a geometric partition model of said circular object, said geometric partition model comprising spatial bins, each of said spatial bins comprising a plurality of pixels, each of said plurality of pixels being assigned one of a plurality of said labels corresponding to a respective spatial bin;
generating a train-time spatial histogram of said train-time image in accordance with said geometric partition model wherein each pixel in said train-time image is assigned a train-time spatial histogram label selected from one of said plurality of labels, said train-time spatial histogram comprising a plurality of train-time sums of grey level values of pixels of said train-time image in respective ones of said spatial bins;
generating a run-time spatial histogram of said run-time image in accordance with said geometric partition model wherein each pixel in said run-time image is assigned a run-time spatial histogram label selected from one of said plurality of labels designating said respective one of said spatial bins, said run-time spatial histogram comprising a plurality of run-time sums of grey level values of pixels of said run-time image in respective ones of said spatial bins;
determining an estimated angular displacement of said run-time image relative to said train-time image to provide an aligned run-time spatial histogram in accordance with said geometric partition model, that is aligned with said train-time spatial histogram in accordance with said estimated angular displacement; and
comparing said train-time spatial histogram and said aligned run-time spatial histogram to determine a difference therebetween to provide an estimate of defects in said circular object.
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Abstract
A very fast, memory efficient, single-step solution for coarse alignment with determination of angular displacement, and inspection for surface defects on circular objects with non-symmetric patterns and random orientation is described. A suitable geometric partition model of an image is constructed that includes a plurality of spatial bins that can be used for rotation estimation and defect detection. In one embodiment for inspection of circular objects, the geometric partitioning includes 256 spatial bins constituted by concentric circular rings divided into sectors. The number of sectors and thickness of the rings are such that the approximate number of pixels in each region is the same. A set of numbers that encode rotational position and gray level information on the plurality of spatial bins of a reference object are acquired during training and used during run-time wherein the same information relative to a circular object being inspected is computed, and the data are correlated to determine both the coarse rotation and gray level difference in pertinent ones of the plurality of spatial bins.
47 Citations
11 Claims
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1. A method of inspecting a circular object to effect angular displacement determination and defect detection, using a machine vision system capable of storing a train-time image of a standard sample of said circular object and capturing a run-time image of said circular object to compare against train-time pixel information, said method comprising the steps of:
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providing a geometric partition model of said circular object, said geometric partition model comprising spatial bins, each of said spatial bins comprising a plurality of pixels, each of said plurality of pixels being assigned one of a plurality of said labels corresponding to a respective spatial bin; generating a train-time spatial histogram of said train-time image in accordance with said geometric partition model wherein each pixel in said train-time image is assigned a train-time spatial histogram label selected from one of said plurality of labels, said train-time spatial histogram comprising a plurality of train-time sums of grey level values of pixels of said train-time image in respective ones of said spatial bins; generating a run-time spatial histogram of said run-time image in accordance with said geometric partition model wherein each pixel in said run-time image is assigned a run-time spatial histogram label selected from one of said plurality of labels designating said respective one of said spatial bins, said run-time spatial histogram comprising a plurality of run-time sums of grey level values of pixels of said run-time image in respective ones of said spatial bins; determining an estimated angular displacement of said run-time image relative to said train-time image to provide an aligned run-time spatial histogram in accordance with said geometric partition model, that is aligned with said train-time spatial histogram in accordance with said estimated angular displacement; and
comparing said train-time spatial histogram and said aligned run-time spatial histogram to determine a difference therebetween to provide an estimate of defects in said circular object. - View Dependent Claims (2, 3, 4, 5, 6, 7, 8, 9, 10, 11)
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Specification