Image Obstruction Detection via Perspective Shift Analysis
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Handheld imaging devices often capture images with unintended obstructions, such as fingers or clothing, which can interfere with the field of view and are difficult to detect and correct, especially in situations where the images hold sentimental value.
Innovation Solution
A method that captures multiple frames of an image and analyzes the perspective shifts to identify edge objects that do not move in sync with the rest of the image, signaling potential obstructions and allowing for corrective action, such as notification or automatic cropping.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If multiple frames are captured and analyzed to detect obstructions, then detection accuracy is improved, but processing time and complexity increase
Solution Approach 1:
The system performs preliminary actions by capturing multiple frames before the actual photo is taken, analyzing perspective shifts in advance to detect obstructions. This allows the detection process to be completed prior to image processing, reducing the time required for post-capture analysis while maintaining high detection accuracy through multi-frame comparison.
2Measurement precision
If multiple frames are captured and analyzed to detect obstructions, then detection accuracy is improved, but device complexity increases
Solution Approach 1:
The patent replaces complex mechanical or manual inspection methods with computational analysis of perspective shifts between frames. By using automated image processing algorithms to detect relative movements of edge objects compared to central objects, the system achieves high detection accuracy through software-based analysis rather than complex hardware modifications or manual review processes.
3Difficulty of detecting and measuring
If perspective shifting is used to detect obstructions, then detection capability is improved, but reliability decreases when device movement is minimal
Solution Approach 1:
The system applies preliminary anti-action by pre-defining threshold values for perspective shift detection and implementing validation logic that accounts for minimal device movement scenarios. The method includes checks to ensure that detected edge objects truly represent obstructions rather than artifacts of negligible camera motion, thereby maintaining reliable detection even when perspective shifting is minimal.
Data Source
AI summary
Detection of an image obstruction is facilitated by, in part, obtaining multiple frames of an image being captured via an imaging device, and confirming movement of the imaging device between the multiple frames. The movement causes perspective shifting of the image between frames and the perspective shifting results in one or more objects of the image shifting between frames. The detection process determines whether an edge object within the multiple frames does not shift in a manner corresponding to the one or more objects between the multiple frames, and based, at least in part, on determining that the edge object does not shift in the corresponding manner to the one or more objects, identifying the edge object as an image obstruction. Presence of the identified image obstruction is signaled to facilitate corrective action being taken.