Homography Matrix Generation from Minimal Key-Point Pairs
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Solution Overview
Problem
Existing techniques for generating a homography matrix require detecting four or more pairs of key-points, which can be inefficient and computationally complex.
Innovation Solution
A method that detects two or more key-point pairs and derived point pairs using scale-invariant features to generate a homography matrix, reducing the minimum number of key-point pairs needed to four, and employs techniques like RANSAC to enhance accuracy and reduce computational complexity.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If four or more key-point pairs are detected to generate a homography matrix, then the accuracy of homography matrix generation is ensured, but the processing time and computational complexity increase
Solution Approach 1:
The patent applies preliminary action by pre-defining multiple candidate directions around each keypoint based on feature information (such as gradient directions or dominant orientations) before the matching process. These candidate directions are prepared in advance, allowing the system to quickly select appropriate corresponding points without extensive real-time computation, thus reducing processing time while maintaining accuracy
Solution Approach 2:
The patent implements local quality by determining directions and distances for deriving corresponding points based on local feature characteristics at each keypoint. Instead of using a uniform approach for all keypoints, the system adapts the derivation parameters to the specific local structure and orientation of each keypoint, improving matching accuracy while reducing the need for exhaustive search
2Reliability
If four or more key-point pairs are detected to generate a homography matrix, then the reliability of the homography matrix is ensured, but the device complexity and computational load increase
Solution Approach 1:
The patent applies parameter changes by dynamically adjusting the number of candidate directions and their angular intervals based on feature information. For keypoints with strong directional characteristics, fewer candidate directions are needed, while keypoints with weaker directional information use more candidates. This adaptive parameter adjustment maintains reliability while reducing overall computational complexity
Solution Approach 2:
The patent segments the keypoint matching process into two stages: first detecting initial keypoint pairs, then deriving additional corresponding point pairs using feature-based directions and distances. This segmentation allows the system to start with a minimal set of keypoints and incrementally build up to the required number of pairs, reducing the complexity of the initial detection phase
Data Source
AI summary
A homography matrix generation apparatus (2000) detects two or more key-point pairs from a first image (10) and a second image (20). The homography matrix generation apparatus (2000) detects, for each of the key-point pairs, a derived point pair, which is a pair of a point separated by a first distance in a first direction from a point on the first image (10) included in the key-point pair and a point separated by a second distance in a second direction from a point on the second image (20) included in the key-point pair. The homography matrix generation apparatus (2000) uses each of the detected key-point pairs and the derived point pairs to generate a homography matrix (40) representing a homography from a point on the first image (10) to a point on the second image (20).