Method and system for automating an image rejection process
First Claim
1. A method for automating an image rejection process, said method comprising:
- extracting features from at least one image among a batch of images utilizing an LBP (Local Binary Pattern) operator to train a classifier, said features comprising texture, spatial structure, and image quality characteristics, wherein said LBP operator extracts several different orientations and types of edge features in said at least one image, giving equal priority for all patterns found;
calculating said features with respect to a test image for submission of said features to said classifier, given an operating point corresponding to a desired false positive rate;
generating at least one output from said classifier as a confidence value corresponding to a likelihood of at least one of the following;
a license plate being absent in said image, said license plate being unreadable, or said license plate being obstructed; and
comparing said confidence value against a threshold to determine if said at least one image should be removed from a human review pipeline, thereby reducing images requiring human review.
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Abstract
Systems and methods for automating an image rejection process. Features including texture, spatial structure, and image quality characteristics can be extracted from one or more images to train a classifier. Features can be calculated with respect to a test image for submission of the features to the classifier, given an operating point corresponding to a desired false positive rate. One or more inputs can be generated from the classifier as a confidence value corresponding to a likelihood of, for example: a license plate being absent in the image, the license plate being unreadable, or the license plate being obstructed. The confidence value can be compared against a threshold to determine if the image(s) should be removed from a human review pipeline, thereby reducing images requiring human review.
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Citations
20 Claims
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1. A method for automating an image rejection process, said method comprising:
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extracting features from at least one image among a batch of images utilizing an LBP (Local Binary Pattern) operator to train a classifier, said features comprising texture, spatial structure, and image quality characteristics, wherein said LBP operator extracts several different orientations and types of edge features in said at least one image, giving equal priority for all patterns found; calculating said features with respect to a test image for submission of said features to said classifier, given an operating point corresponding to a desired false positive rate; generating at least one output from said classifier as a confidence value corresponding to a likelihood of at least one of the following;
a license plate being absent in said image, said license plate being unreadable, or said license plate being obstructed; andcomparing said confidence value against a threshold to determine if said at least one image should be removed from a human review pipeline, thereby reducing images requiring human review. - View Dependent Claims (2, 3, 4, 5, 6, 7, 8, 9)
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10. A system for automating an image rejection process, said system comprising:
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at least one processor; and a memory comprising instructions stored therein, which when executed by said at least one processor, cause said at least one processor to perform operations comprising; extracting features from at least one image among a batch of images utilizing an LBP (Local Binary Pattern) operator to train a classifier, said features comprising texture, spatial structure, and image quality characteristics, wherein said LBP operator extracts several different orientations and types of edge features in said at least one image, giving equal priority for all patterns found; calculating said features with respect to a test image for submission of said features to said classifier, given an operating point corresponding to a desired false positive rate; generating at least one output from said classifier as a confidence value corresponding to a likelihood of at least one of the following;
a license plate being absent in said at least one image, said license plate being unreadable, or said license plate being obstructed; andcomparing said confidence value against a threshold to determine if said at least one image should be removed from a human review pipeline, thereby reducing images requiring human review. - View Dependent Claims (11, 12, 13, 14, 15, 16, 17)
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18. A system for automating an image rejection process, said system comprising:
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at least one image-capturing unit; at least one processor that communicates electronically with said at least one image-capturing unit; and a memory comprising instructions stored therein, which when executed by said at least one processor, cause said at least one processor to perform operations comprising; extracting features from at least one image among a batch of images utilizing an (LBP Local Binary Pattern) operator to train a classifier, said features comprising texture, spatial structure, and image quality characteristics, wherein said LBP operator extracts several different orientations and types of edge features in said at least one image, giving equal priority for all patterns found; calculating said features with respect to a test image for submission of said features to said classifier, given an operating point corresponding to a desired false positive rate; generating at least one output from said classifier as a confidence value corresponding to a likelihood of at least one of the following;
a license plate being absent in said at least one image, said license plate being unreadable, or said license plate being obstructed; andcomparing said confidence value against a threshold to determine if said at least one image should be removed from a human review pipeline, thereby reducing images requiring human review. - View Dependent Claims (19, 20)
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Specification