Image Verification Using Feature Point Mapping and Geometric Constraints
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Solution Overview
Problem
Existing image verification methods have low accuracy in distinguishing between images copied from different shooting angles, leading to misjudgment and incorrect identification of copied images.
Innovation Solution
A method that acquires feature points from source and target images, determines mapped points, calculates distances between feature points, and counts reference feature point pairs within a threshold to determine if the target image is a copy of the source image, using algorithms like ORB and SIFT for feature extraction and affine or homography transformations for mapping.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional local feature matching is used to verify images, then the verification process is simple and fast, but the accuracy is low and misjudgment occurs for images from different shooting angles
Solution Approach 1:
The patent segments the image verification process into multiple stages: traditional local feature matching is performed first to identify candidate feature point pairs, then geometric constraints (distance ratios, angle relationships) are applied to filter and verify these pairs. This segmentation allows the system to maintain simplicity in the initial matching phase while adding complexity only where needed for accuracy improvement.
Solution Approach 2:
The patent introduces additional verification parameters beyond simple local feature matching, including distance ratios between feature points, angular relationships, and geometric constraint satisfaction. By changing the parameter set used for verification from basic feature similarity to multi-parameter geometric validation, the system achieves higher accuracy while managing complexity through hierarchical processing.
2Reliability
If geometric constraints are added to feature point verification, then the accuracy for distinguishing different shooting angles improves, but the computational complexity increases
Solution Approach 1:
The patent performs preliminary local feature matching to identify candidate feature point pairs before applying geometric constraint verification. This preliminary action filters out obviously mismatched pairs early, reducing the number of pairs that require complex geometric analysis. The system prepares and pre-processes feature matches in advance, then applies geometric constraints only to promising candidates, thereby improving reliability while controlling computational complexity.
Data Source
AI summary
A method for verifying an image can include: acquiring a first feature point set of a source image and a second feature point set of a target image; determining a target local feature point pair based on the first feature point set and the second feature point set; determining a mapped point of the first feature point on the target image; determining a distance between a second feature point and the mapped point; acquiring a quantity of reference local feature point pairs; and determining that the target image is an image acquired by copying the source image based on the quantity being greater than a target quantity.