Semantic BRIEF Point Matching for Image Alignment
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Solution Overview
Problem
Conventional point description and matching techniques, such as SIFT and SURF, are computationally expensive and inaccurate due to their reliance on low-level features, while simpler methods like ORB introduce ambiguity, leading to misalignments, especially in complex images with large viewpoint changes.
Innovation Solution
A method that combines low-level intensity features with high-level semantic features using a semantic feature descriptor, which assigns semantic labels to key points and matches them based on shared labels and Hamming distance measures, reducing mismatching and improving accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional point description and matching techniques (SIFT, SURF) are used, then matching accuracy is improved, but computational cost increases significantly
Solution Approach 1:
The patent segments the feature description into two independent parts: low-level intensity features (captured by ORB) and high-level semantic features (captured by semantic segmentation masks). This segmentation allows each part to be processed separately and combined, achieving both speed and accuracy. The semantic features provide discriminative power for accurate matching, while the simplified intensity features reduce computational burden.
Solution Approach 2:
The patent creates a composite feature descriptor that combines ORB intensity descriptors with semantic segmentation labels. This composite approach integrates the advantages of both low-level and high-level features: the speed and simplicity of ORB with the accuracy and robustness of semantic features, resolving the contradiction between computational efficiency and matching precision.
2Productivity
If simpler point description methods (ORB) are used, then computational time is reduced, but matching accuracy deteriorates due to feature ambiguity
Solution Approach 1:
The patent introduces semantic segmentation masks as an intermediary layer between the image and the feature matching process. These masks provide high-level semantic information that disambiguates the simple ORB descriptors, allowing the system to maintain computational speed while significantly improving matching accuracy by filtering out ambiguous matches.
Solution Approach 2:
The patent adds a new dimension of semantic information to the traditional 2D image space by incorporating semantic segmentation labels. This dimensional enrichment transforms the feature space from purely intensity-based to a multi-dimensional space that includes semantic categories, enabling more accurate discrimination between similar-looking features.
3Device complexity
If low-level features only are used for matching, then computational complexity is reduced, but reliability decreases in complex images with large viewpoint changes
Solution Approach 1:
The patent changes the parameter space by incorporating semantic labels as an additional matching criterion. Instead of relying solely on intensity and gradient parameters, the system now matches features based on both low-level descriptors and high-level semantic categories. This parameter expansion maintains computational efficiency while significantly improving reliability in challenging conditions.
Data Source
AI summary
Methods and apparatus to match images using semantic features are disclosed. An example apparatus includes a semantic labeler to determine a semantic label for each of a first set of points of a first image and each of a second set of points of a second image; a binary robust independent element features (BRIEF) determiner to determine semantic BRIEF descriptors for a first subset of the first set of points and a second subset of the second set of points based on the semantic labels; and a point matcher to match first points of the first subset of points to second points of the second subset of points based on the semantic BRIEF descriptors.