AR Interface Screenshot Correlation for Conflation Error Detection
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Conflation errors in augmented reality interfaces are difficult to detect and mitigate, leading to confusion between physical and digital components, which can result in unintended interactions and are often not reported to developers until it's too late to correct the issue.
Innovation Solution
A method using a generative adversarial network (GAN) to generate a replica screenshot and a fractal-based convolutional neural network (CNN) to determine correlation between actual and replica screenshots, flagging potential conflation errors when the correlation falls below a threshold.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If augmented reality interfaces overlay digital components on real-world environments, then user experience and information delivery are enhanced, but conflation errors between physical and digital components become difficult to detect
Solution Approach 1:
The system creates a replica screenshot that copies the expected appearance of the augmented reality interface by rendering digital components on a blank image. This copy is then compared with the actual screenshot to detect conflation errors, where digital components are mistakenly identified as physical components or vice versa.
Solution Approach 2:
A generative adversarial network (GAN) serves as an intermediary to generate the replica screenshot, bridging the gap between the actual screenshot and the expected interface appearance. The GAN processes the actual screenshot to produce a replica that represents how the interface should appear without conflation errors.
2Reliability
If conflation errors occur in augmented reality interfaces, then interaction detection fails or unintended actions are triggered, but these errors are not reported to developers until too late to correct
Solution Approach 1:
The system implements automated feedback by comparing the actual screenshot with the replica screenshot generated by the GAN. When discrepancies are detected below a certain threshold, the system flags potential conflation errors, providing immediate feedback that enables timely correction before deployment or user reporting.
Solution Approach 2:
The system performs preliminary detection of conflation errors by analyzing screenshots before they reach production or user hands. By using the GAN to generate expected interface appearances and comparing them with actual captures, the system identifies issues in advance, enabling preventive correction rather than reactive troubleshooting.
Data Source
AI summary
A method for detecting conflation errors in an augmented reality interface is disclosed. In one embodiment, such a method includes receiving an actual screenshot of an augmented reality interface. The method generates, using a generative adversarial network (GAN), a replica screenshot of the augmented reality interface that indicates how the actual screenshot is expected to appear. The replica screenshot is non-identical to the actual screenshot. The method receives the actual screenshot and the replica screenshot into a fractal-based convolutional neural network (CNN) to determine a correlation between the actual screenshot and the replica screenshot. In the event the correlation is below a designated threshold, the method flags the actual screenshot as having a potential conflation error. This conflation error may involve conflating a digital component for a physical component or vice versa. A corresponding system and computer program product are also disclosed.