Marker Generation Device for Accurate Feature Point Matching
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Solution Overview
Problem
Existing marker generation technologies fail to detect identifiers as markers due to mismatched feature points, preventing accurate identification and distinction of products with different manufacturers.
Innovation Solution
A marker generation device and system that arranges feature points in a predetermined space, counts matching points, and selects identifiers with the highest count as detection markers by converting their configuration to match unique features.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If identifiers are used as markers without configuration conversion, then the original identifier structure is preserved, but the feature points do not match with unique features preventing accurate detection
Solution Approach 1:
The patent applies parameter changes by converting the configuration of identifiers to match unique features. Specifically, the system extracts feature points from identifiers, compares them with unique features extracted from background images, and transforms the identifier configuration (such as scaling, rotating, or repositioning) to maximize the number of matching feature points with unique features, thereby enabling reliable detection while maintaining identifier functionality
Solution Approach 2:
The patent implements preliminary action by pre-extracting unique features from background images before identifier detection occurs. These unique features are stored and used as reference patterns for subsequent identifier detection, allowing the system to quickly determine whether detected feature points correspond to actual markers or background elements without performing complex analysis during the detection phase
2Measurement precision
If feature points are extracted and arranged in predetermined space to identify unique features, then accurate marker detection is enabled, but the process becomes complex when handling multiple identifier configurations
Solution Approach 1:
The patent applies local quality by focusing the comparison process on specific local features rather than analyzing the entire identifier image globally. The system extracts discrete feature points from identifiers and compares their local characteristics (such as position, orientation, and distance relationships) with locally extracted unique features from background images, enabling precise matching while reducing overall computational complexity
Solution Approach 2:
The patent implements segmentation by dividing the identifier image into discrete feature points rather than treating it as a continuous image. Each feature point is independently extracted and its coordinates are arranged in a predetermined space for comparison with unique features. This segmentation approach simplifies the complexity of handling multiple identifier configurations by reducing them to a set of comparable discrete points
3Measurement precision
If identifier configuration is converted to maximize feature point matching, then detection accuracy improves, but the original identifier information may be altered
Solution Approach 1:
The patent applies copying by creating a transformed copy of the identifier configuration for detection purposes while preserving the original identifier information. The system extracts feature points from the original identifier, generates a converted configuration that maximizes matching with unique features, and uses this copy for detection without modifying the source identifier data, thus maintaining both detection accuracy and information integrity
Data Source
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AI summary
Provided is a marker generation device which has a feature comparison means and a marker pattern generation means. The feature comparison means disposes feature points extracted from an image in a predetermined space, sets parts in which the number of feature points in the predetermined space is equal to or less than a predetermined number as singular features, disposes feature points extracted from an identifier in the predetermined space, and counts the number of feature points that coincide with the singular features. The marker pattern generation means converts the structure of the identifier and extracts the feature points from the converted identifier. The feature comparison means disposes the feature points extracted from the converted identifier in the predetermined space, counts the number of feature points that coincide with the singular features, and selects, as a marker for detection, an identifier having the most counts from among identifiers before conversion and one or two or more identifiers that have been converted.