Selective Local Image Registration Using Error Thresholds
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Solution Overview
Problem
Current image registration techniques face challenges in efficiently compensating for scene motion, which is difficult to model parametrically and time-consuming to process, especially when combined with camera motion.
Innovation Solution
A hybrid image registration method that selectively applies local registration based on registration error thresholds, using a pixel difference mask to identify areas requiring local correction after initial compensation for camera motion, thereby improving efficiency and accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If local registration is applied to the entire image to compensate for scene motion, then registration accuracy is improved, but computational time and resources increase significantly
Solution Approach 1:
The patent divides the image into multiple regions and applies local registration only to specific regions where scene motion is detected, rather than processing the entire image. This segmentation approach maintains registration accuracy in motion-affected areas while reducing overall computational time and resources.
Solution Approach 2:
The patent applies different registration strategies to different regions of the image based on local characteristics. Regions with significant scene motion receive local registration treatment, while regions without motion use simpler compensation methods, optimizing the balance between accuracy and computational efficiency.
2Reliability
If local registration is applied to the entire image, then compensation for scene motion is improved, but processing complexity increases
Solution Approach 1:
The patent segments the image processing task by identifying and isolating regions with scene motion from the rest of the image. This allows complex local registration to be applied only where necessary, reducing overall processing complexity while maintaining reliable scene motion compensation in affected areas.
Solution Approach 2:
The patent performs preliminary analysis to detect scene motion regions before applying local registration. This preliminary action identifies where complex processing is needed, allowing the system to prepare and apply registration only to those specific regions, thereby reducing overall processing complexity.
3Productivity
If selective local registration is applied only to regions with significant motion, then processing efficiency is improved, but registration accuracy may deteriorate in motion-affected areas
Solution Approach 1:
The patent applies high-precision local registration specifically to regions where scene motion is detected, ensuring that registration accuracy is maintained in motion-affected areas. In regions without motion, simpler and faster methods are used, thereby achieving both processing efficiency and accurate registration where needed.
Solution Approach 2:
The patent uses registration error thresholds as feedback to determine whether local registration should be applied to specific regions. By monitoring registration errors and comparing them against thresholds, the system can dynamically adjust which regions receive local registration treatment, ensuring accuracy in motion areas while maintaining processing efficiency.
Data Source
AI summary
For selective local registration based on registration error, a method determines a first registration error between a template image and a source image. The method further warps the source image to compensate for camera motion in response to the first registration error exceeding a registration error threshold. In addition, the method determines a second registration error between the template image and the warped source image. The method generates a pixel difference mask using the template image. The method further selectively performs local registration for the source image based on the pixel difference mask in response to the second registration error exceeding the registration error threshold.