Iterative method and system of identifying valid objects in a background of an image
First Claim
1. A method of identifying at least one valid object having at least one predetermined attribute value in a background, comprising the steps of:
- (a) generating an image of the object and the background;
(b) generating a gray level histogram of the image;
(c) selecting N global entropic threshold gray levels;
(d) subdividing the gray level histogram into N+1 sub-histograms using each of the entropically selected threshold gray levels;
(e) searching portions of the image corresponding to each sub-histogram using each global entropically selected threshold gray level for at least one candidate object, wherein the candidate object has at least one candidate object attribute value; and
(f) validating the candidate objects having the valid object predetermined attribute values for each of the sub-histograms to identify the valid object.
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Abstract
The present invention relates to image analysis methods and systems for identifying objects in a background by generating a description, which may be either a histogram or co-occurrence matrix, of the gray level space of the image by using an entropic kernel to recursively analyze the gray level space for candidate objects and validating the presence of valid objects by comparing the candidate object attribute values to a defined set of valid object attribute values contained in a driver. The present invention includes recursive, iterative and parallel processing methods. The methods may be used in a wide variety of industrial inspection techniques, including colony counting and the identification of discrete features in carpets and of pigment elements embedded in a polymer.
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Citations
8 Claims
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1. A method of identifying at least one valid object having at least one predetermined attribute value in a background, comprising the steps of:
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(a) generating an image of the object and the background; (b) generating a gray level histogram of the image; (c) selecting N global entropic threshold gray levels; (d) subdividing the gray level histogram into N+1 sub-histograms using each of the entropically selected threshold gray levels; (e) searching portions of the image corresponding to each sub-histogram using each global entropically selected threshold gray level for at least one candidate object, wherein the candidate object has at least one candidate object attribute value; and (f) validating the candidate objects having the valid object predetermined attribute values for each of the sub-histograms to identify the valid object. - View Dependent Claims (2, 3, 4, 5, 6, 7, 8)
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Specification