Misaligned Camera Identification via Stereoscopic Distance Analysis
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Conventional methods fail to accurately and efficiently identify and recalibrate misaligned cameras in computing devices over time, leading to spurious distance measurements and potential camera damage, especially due to shocks or natural degradation.
Innovation Solution
The system employs multiple stereoscopic camera pairs to capture and evaluate 3D image data, identifying misaligned cameras by comparing distance measurements and recalibrating them using transformation matrices and optimization methods, with the option to label cameras as defective if recalibration fails.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If calibration procedure is used during manufacture or testing, then camera alignment is improved, but user recalibration over time becomes difficult
Solution Approach 1:
The system automatically detects camera misalignment and performs recalibration without requiring user intervention. The processor monitors disparity information over time and autonomously executes recalibration procedures when misalignment is detected, making the system self-maintaining and eliminating the need for difficult manual user recalibration.
2Measurement precision
If stereoscopic cameras are used to capture 3D images, then depth perception is improved, but camera misalignment due to shocks or degradation occurs over time
Solution Approach 1:
The system continuously monitors disparity information from stereoscopic image pairs and compares it against expected values. When misalignment is detected through feedback from the disparity analysis, the system automatically triggers recalibration to correct the alignment, thereby maintaining reliable depth perception over time despite shocks or degradation.
Solution Approach 2:
The system performs preliminary detection of misalignment conditions by analyzing disparity information before significant degradation affects image quality. By detecting early signs of misalignment and proactively recalibrating, the system prevents further degradation and maintains reliable stereoscopic functionality.
3Productivity
If multiple camera pairs are used for 3D imaging, then imaging redundancy is improved, but identification of misaligned cameras becomes complex
Solution Approach 1:
The system segments the analysis by examining each camera pair independently through separate stereoscopic image pairs. By processing and comparing disparity information from each pair separately, the system can identify which specific camera is misaligned without being overwhelmed by the complexity of analyzing all cameras simultaneously, thus maintaining ease of identification despite having multiple camera pairs.
Data Source
AI summary
An occurrence of an event triggering a dynamic recalibration of cameras in a computing device can be detected. In response, respective images of an object can be acquired using each of at least three cameras in the computing device at approximately a same time. Using at least three distinct pairs of the at least three cameras, a respective apparent distance between the computing device and the object can be determined. The respective apparent distances can be analyzed to isolate at least one misaligned camera.