Orientation-Based Subject Matching in Images
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Solution Overview
Problem
Current subject-matching in image analysis techniques face challenges in accurately identifying individuals across varying orientations and image acquisition conditions, leading to reduced accuracy in matching subjects across different image sequences.
Innovation Solution
The method involves detecting the orientation of subjects in target and source images, extracting relevant features, and computing a visual similarity score by applying weights based on orientation parameters, which are determined using a training model to account for changes in orientation and image acquisition device pairs.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Stability of the object's composition
If color features along horizontal stripes are used for comparison, then robustness to orientation changes is improved, but matching accuracy for varied orientations deteriorates
Solution Approach 1:
The patent dynamically adjusts the feature extraction and weighting process based on detected orientation parameters. Instead of using fixed horizontal stripe comparisons, the system adapts feature extraction to the specific orientation of subjects in images, allowing optimal feature selection for each comparison scenario while maintaining computational efficiency.
Solution Approach 2:
The patent changes the parameters used for feature comparison based on orientation detection. By detecting orientation parameters and using them to select and weight features differently, the system transforms the static feature comparison approach into a dynamic one that adapts to orientation variations, resolving the contradiction between robustness and accuracy.
2Measurement precision
If orientation-based feature weighting is applied, then subject-matching accuracy across different orientations is improved, but computational complexity increases
Solution Approach 1:
The patent performs orientation detection and feature weighting preparation in advance before the actual subject-matching comparison. By pre-processing to determine orientation parameters and pre-weighting features based on these parameters, the system reduces the computational burden during the matching phase, achieving high accuracy without excessive real-time computational complexity.
Solution Approach 2:
The patent segments the subject-matching process into distinct stages: orientation detection, feature extraction with orientation-based weighting, and final comparison. This segmentation allows each stage to be optimized independently, managing computational complexity by breaking down the complex task into manageable steps with clear inputs and outputs.
3Adaptability or versatility
If multiple feature types are extracted and weighted according to orientation parameters, then adaptability to different image acquisition conditions is improved, but processing time increases
Solution Approach 1:
The patent extracts and weights multiple feature types but focuses computational resources on the most relevant features for each orientation scenario. By using orientation parameters to identify and prioritize key features rather than processing all features equally, the system achieves high adaptability to different acquisition conditions while avoiding the processing time penalty of exhaustive feature analysis.
Data Source
AI summary
A system and method for subject-matching in images comprises detecting a first orientation of a first subject in a target image; comparing the first orientation with a second orientation of a second subject in a source image to obtain at least one orientation parameter; extracting at least one features from the target image; computing a visual similarity score between the target image and the source image using the features, the visual similarity score being computed as a function of the at least one feature and the at least one orientation parameter; and determining a match between the first subject and the second subject in accordance with the visual similarity score.