Image Alignment Using Saliency Region Segmentation
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Solution Overview
Problem
Existing image alignment algorithms require significant computational resources and time due to complex computations, especially in real-time applications like on-device video stabilization for video recording.
Innovation Solution
The method involves scaling images to lower resolution for a rough offset estimation and then using saliency regions for a fine offset estimation, combining these to determine a final offset for efficient image alignment, thereby reducing computational complexity.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional image alignment algorithms are used to align two images, then alignment accuracy can be achieved, but computation time and computational complexity increase significantly
Solution Approach 1:
The patent divides the image alignment process into multiple stages: first dividing the image into multiple regions, then further dividing selected regions into sub-regions for detailed analysis. This segmentation allows the algorithm to focus computational resources on critical areas rather than processing the entire image uniformly, thereby reducing overall computation time while maintaining alignment accuracy.
Solution Approach 2:
The patent applies different processing qualities to different parts of the image. Saliency regions and their sub-regions receive fine-grained analysis with higher computational effort, while other regions use coarser processing. This local quality approach ensures accurate alignment measurement in critical areas without unnecessarily processing less important regions, resolving the contradiction between accuracy and computation time.
2Measurement precision
If full-resolution images are processed for alignment, then alignment precision is improved, but computational complexity increases
Solution Approach 1:
The patent segments the full-resolution image into multiple regions and identifies saliency regions that contain the most important alignment information. By further dividing these saliency regions into sub-regions, the algorithm processes only necessary portions of the full-resolution image, reducing computational complexity while preserving alignment precision where it matters most.
Solution Approach 2:
The patent applies partial action by processing only specific sub-regions of the image at full resolution rather than the entire image. The algorithm identifies and focuses computational effort on saliency sub-regions that contribute most to alignment accuracy, avoiding excessive processing of redundant areas, thus reducing computational complexity while maintaining precision.
3Measurement precision
If detailed analysis of entire images is performed, then alignment accuracy is improved, but processing speed decreases
Solution Approach 1:
The patent divides the image into multiple regions and identifies saliency regions that contain critical alignment information. By further segmenting these into sub-regions, the algorithm performs detailed analysis only where necessary, maintaining alignment accuracy while significantly improving processing speed through selective detailed analysis.
Solution Approach 2:
The patent applies partial action by performing detailed analysis only on selected sub-regions rather than the entire image. This approach achieves sufficient alignment accuracy by focusing computational resources on critical areas, thereby improving processing speed without sacrificing necessary precision.
Data Source
AI summary
An image alignment method includes steps of receiving a first image and a second image; scaling the first image and the second image by a ratio to generate a first downsized image and a second downsized image respectively; determining a first offset between the first downsized image and the second downsized image; selecting a first saliency region and a second saliency region from the first downsized image and the second downsized image; determining a second offset between a first sub-region within the first image and a second sub-region within the second image, the first sub-region and the second sub-region corresponding to the first saliency region and the second saliency region respectively; determining a final offset according to the ratio, the first offset and the second offset; and aligning the first image and the second image by the final offset.