Augmented Reality Marker Suitability Scoring and Real-Time Feedback
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Solution Overview
Problem
Current AR client applications lack a method to assess the suitability of user-suggested sub-images for use as markers and provide real-time feedback on how to enhance their suitability for image recognition, leading to inefficient trial-and-error processes in finding suitable markers.
Innovation Solution
An AR content creation system computes a suitability score for the sub-image based on feature richness and uniqueness, and if below a threshold, it searches for a sub-image that completely contains the original, offering real-time advice to use the new sub-image as the marker to improve feature richness or uniqueness.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If a user-suggested sub-image is used as an AR marker without suitability assessment, then the marker creation process is simple and fast, but the image recognition reliability and accuracy deteriorate
Solution Approach 1:
The system performs preliminary assessment of the sub-image's suitability for AR marker usage before finalizing the marker creation. This includes evaluating feature richness, uniqueness against existing markers, and computing suitability scores in advance to ensure reliable image recognition while maintaining a streamlined user workflow.
Solution Approach 2:
The system provides real-time feedback to users about the suitability of their selected sub-images for AR markers. By computing and displaying suitability scores, feature richness evaluations, and uniqueness assessments, users can immediately understand whether their selection is appropriate or needs adjustment, thereby improving recognition reliability without adding significant complexity to the creation process.
2Productivity
If trial-and-error method is used to find suitable markers, then the user can eventually find appropriate markers, but the time consumption and efficiency worsen
Solution Approach 1:
The system performs preliminary evaluation of candidate markers by computing suitability scores based on feature richness and uniqueness before the user commits to a selection. This pre-assessment eliminates the need for repeated trial-and-error attempts, as users can immediately identify suitable markers through the provided feedback, dramatically improving creation efficiency and reducing time loss.
Solution Approach 2:
The system enables users to independently assess marker suitability through automated feedback mechanisms. By providing real-time suitability scores, feature richness metrics, and uniqueness evaluations, users can make informed decisions without requiring iterative attempts or external assistance, thereby optimizing both productivity and time utilization.
3Measurement precision
If the sub-image has insufficient feature richness or uniqueness, then the marker creation is easier, but the image recognition accuracy and reliability deteriorate
Solution Approach 1:
The system provides detailed feedback to users about the feature richness and uniqueness of their selected sub-images. By computing suitability scores that specifically evaluate these attributes and presenting them to users in real-time, the system enables informed decisions about whether the selected image meets the necessary criteria for accurate recognition, or if modifications are needed.
Solution Approach 2:
The system performs preliminary analysis of the sub-image's feature richness and uniqueness before final marker creation. This pre-evaluation identifies potential issues with recognition accuracy early in the process, allowing users to make necessary adjustments before committing to the marker, thereby ensuring high recognition precision without unnecessary complexity.
Data Source
AI summary
For an augmented reality (AR) content creation system having a marker database, when a user requests this system to use a first sub-image of an image to update the marker database, this system computes a suitability score of the first sub-image for rating feature richness of the first sub-image and uniqueness thereof against existing markers in the marker database. When the suitability score is less than a threshold value, a second sub-image of the image having a suitability score not less than the threshold value and completely containing the first sub-image is searched. Then the second sub-image, the suitability score thereof and the suitability score of the first sub-image are substantially-immediately presented to the user for real-time suggesting the user to use the second sub-image instead of the first sub-image as a new marker in updating the marker database to increase feature richness or uniqueness of the new marker.