Feature-based detection and context discriminate classification for known image structures
First Claim
1. A method of detecting and classifying targets in a digital image of a structure having known characteristics, comprising the steps of:
- generating a feature set for each of a plurality of overlapping windowed portions of said image, each feature in said feature set defined by a value indicative of a mathematical measure of a corresponding one of said plurality of overlapping windowed portions;
forming a weighted sum for each of said plurality of overlapping windowed portions using said feature set corresponding thereto;
normalizing each feature in said feature set and said weighted sum for each of said plurality of overlapping windowed portions across said plurality of overlapping windowed portions, wherein a context matrix is defined by a normalized feature set and a normalized weighted sum for each of said plurality of overlapping windowed portions;
forming a score using said context matrix for each of said plurality of overlapping windowed portions;
normalizing said score for each of said plurality of overlapping windowed portions across said plurality of overlapping windowed portions, wherein a normalized score is defined for each of said plurality of overlapping windowed portions;
evaluating each of said plurality of overlapping windowed portions in terms of location in said image and the known characteristics of the structure in said image, wherein a plurality of relevant windowed portions are identified;
comparing a threshold criteria to a maximum score defined as the maximum of said normalized weighted sum and said normalized score for each of said plurality of relevant windowed portions, wherein each of said plurality of overlapping windowed portions having said maximum score satisfying said threshold criteria is classified as a possible target window and wherein said maximum score is indicative of a target classification;
assigning each said possible target window to a group based on location of said possible target window in said image and said maximum score associated with said possible target window;
forming a group score for each said group using said maximum score associated with each said possible target window in said group; and
comparing each said group score to a group threshold criteria, wherein each said group having its corresponding said group score satisfying said group threshold criteria is classified as a target and wherein said group score is indicative of a target classification.
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Abstract
A method is provided for the detection and classification of targets in a digital image of a structure having known characteristics. In general, windowed portions of the image are evaluated in context with the entire image and in terms of their location in the image. More specifically, a scoring scheme is used to identify relevant windows with the relevance of each window being evaluated in terms of location in the image and the known characteristics of the structure being imaged. Relevant windows satisfying a threshold criteria are grouped based on their relative location in the image. A group scoring scheme is applied to each group to identify and classify targets.
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Citations
21 Claims
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1. A method of detecting and classifying targets in a digital image of a structure having known characteristics, comprising the steps of:
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generating a feature set for each of a plurality of overlapping windowed portions of said image, each feature in said feature set defined by a value indicative of a mathematical measure of a corresponding one of said plurality of overlapping windowed portions; forming a weighted sum for each of said plurality of overlapping windowed portions using said feature set corresponding thereto; normalizing each feature in said feature set and said weighted sum for each of said plurality of overlapping windowed portions across said plurality of overlapping windowed portions, wherein a context matrix is defined by a normalized feature set and a normalized weighted sum for each of said plurality of overlapping windowed portions; forming a score using said context matrix for each of said plurality of overlapping windowed portions; normalizing said score for each of said plurality of overlapping windowed portions across said plurality of overlapping windowed portions, wherein a normalized score is defined for each of said plurality of overlapping windowed portions; evaluating each of said plurality of overlapping windowed portions in terms of location in said image and the known characteristics of the structure in said image, wherein a plurality of relevant windowed portions are identified; comparing a threshold criteria to a maximum score defined as the maximum of said normalized weighted sum and said normalized score for each of said plurality of relevant windowed portions, wherein each of said plurality of overlapping windowed portions having said maximum score satisfying said threshold criteria is classified as a possible target window and wherein said maximum score is indicative of a target classification; assigning each said possible target window to a group based on location of said possible target window in said image and said maximum score associated with said possible target window; forming a group score for each said group using said maximum score associated with each said possible target window in said group; and comparing each said group score to a group threshold criteria, wherein each said group having its corresponding said group score satisfying said group threshold criteria is classified as a target and wherein said group score is indicative of a target classification. - View Dependent Claims (2, 3, 4, 5, 6, 7, 8, 9, 10)
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11. A system for detecting and classifying targets in a digital image of a structure having known characteristics, comprising:
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means for generating a feature set for each of a plurality of overlapping windowed portions of said image, each feature in said feature set being defined by a value indicative of a mathematical measure of a corresponding one of said plurality of overlapping windowed portions; a processor for i) forming a weighted sum for each of said plurality of overlapping windowed portions using said feature set corresponding thereto, ii) normalizing each feature in said feature set and said weighted sum for each of said plurality of overlapping windowed portions across said plurality of overlapping windowed portions, wherein a context matrix is defined by a normalized feature set and a normalized weighted sum for each of said plurality of overlapping windowed portions, iii) forming a score using said context matrix for each of said plurality of overlapping windowed portions, iv) normalizing said score for each of said plurality of overlapping windowed portions across said plurality of overlapping windowed portions, wherein a normalized score is defined for each of said plurality of overlapping windowed portions, v) evaluating each of said plurality of overlapping windowed portions in terms of location in said image and the known characteristics of the structure in said image, wherein a plurality of relevant windowed portions are identified; vi) comparing a threshold criteria to a maximum score defined as the maximum of said normalized weighted sum and said normalized score for each of said plurality of relevant windowed portions, wherein each of said plurality of overlapping windowed portions having said maximum score satisfying said threshold criteria is classified as a possible target window and wherein said maximum score is indicative of a target classification, vii) assigning each said possible target window to a group based on location of said possible target window in said image and said maximum score associated with said possible target window, viii) forming a group score for each said group using said maximum score associated with each said possible target window in said group, and ix) comparing each said group score to a group threshold criteria, wherein each said group having its corresponding said group score satisfying said group threshold criteria is classified as a target and wherein said group score is indicative of a target classification; and at least one output device coupled to said processor for providing an indication that said group score satisfies said group threshold criteria. - View Dependent Claims (12, 13, 14, 15, 16, 17, 18, 19, 20, 21)
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Specification