Wearable Camera Image Enhancement for User-Caused Occlusions
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Solution Overview
Problem
Wearable devices often capture images with poor quality due to issues like blind framing and unintended occlusions, which existing methodologies fail to adequately address.
Innovation Solution
Implementing intelligent image enhancement techniques that detect and mitigate occlusions and blurring in wearable device images using artificial intelligence, including notifications and automatic image correction.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If wearable devices capture images automatically, then image capture speed is improved, but image quality deteriorates due to blind framing and occlusions
Solution Approach 1:
The system analyzes captured images to detect occlusions and framing issues, then provides feedback to the user through notifications. This feedback loop enables users to adjust their positioning or re-capture images, thereby improving image quality while maintaining automatic capture functionality
Solution Approach 2:
The system performs preliminary analysis of the captured image to detect potential quality issues such as occlusions and blind framing before the user finalizes the image. This preliminary detection allows for early intervention through user notification and potential re-capture
2Manufacturing precision
If the system notifies users of all occlusions, then image quality control is improved, but user experience deteriorates due to excessive notifications
Solution Approach 1:
The system changes the notification parameter based on the occlusion threshold. When occlusion exceeds a predetermined threshold, a notification is triggered; otherwise, no notification is provided. This parameter-based approach balances quality control with user experience by filtering out minor issues
3Manufacturing precision
If automatic image correction is implemented, then image quality is improved, but device complexity increases
Solution Approach 1:
The system extracts only the essential image quality assessment functionality from a full image editing suite. It focuses specifically on detecting occlusions and framing issues rather than implementing comprehensive automatic correction, thereby limiting complexity while maintaining quality improvement
Data Source
AI summary
A method is provided for detecting whether image data captured by a head-worn device is blocked by an occlusion. The method includes receiving image data captured by a camera of a head-worn device. The method includes determining the image data indicates an occlusion caused by the user is present in the camera's field of view. The method includes determining that the image data indicates that an occlusion caused by the user is present in a portion of a field of view of the camera. The method includes, when determining the occlusion satisfies a first occlusion threshold, notifying the user there is an occlusion to the field of view of the camera. And the method includes, when the occlusion satisfies a second occlusion threshold, (i) forgoing notifying the user there is an occlusion to the field of view, and (ii) modifying the image data to remove or minimize the occlusion.


