Homographic Image Processing with Local Segmentation
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Solution Overview
Problem
Existing image processing techniques using global homography matrices fail to accurately model correspondence relationships between images captured from different viewpoints, especially when conditions such as rotational movement, objects on the same plane, and sufficient distance from the observer are not met, leading to compromises in plane parallax problems.
Innovation Solution
The method involves segmenting images into local regions, generating segmentation mask pairs, and using a neural network to predict local homography matrices for each region, which are then used to align images, considering plane parallax and improving accuracy across different planes.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If a global homography matrix is used to model correspondence relationships between images, then the processing is simple and fast, but the alignment accuracy deteriorates when plane parallax exists
Solution Approach 1:
The patent divides the image into multiple local regions based on segmentation masks, and estimates separate homography matrices for each region instead of using a single global homography matrix. This segmentation approach allows each region to be aligned independently, handling plane parallax effects while maintaining computational efficiency through region-based processing
Solution Approach 2:
The patent applies different homography matrices to different local regions of the image, allowing each region to have its own alignment parameters optimized for its specific content and depth. This local quality approach enables accurate alignment across varying planes without the constraints of a single global transformation
2Reliability
If a global homography matrix is used for image alignment, then the method is simple to implement, but it fails to handle plane parallax problems accurately
Solution Approach 1:
The patent segments the image into multiple regions based on depth information and plane parallax characteristics, then estimates homography matrices separately for each segment. This segmentation enables reliable correspondence modeling across different planes while keeping the complexity manageable through automated region-based processing
Solution Approach 2:
The patent dynamically adjusts the number and configuration of local homography matrices based on the detected plane parallax and image content. When plane parallax is significant, more local matrices are applied; when the scene is relatively flat, fewer matrices are needed, making the system adaptable to varying complexity levels
3Measurement precision
If existing techniques use approximate global homography estimation, then the computation is fast, but the alignment accuracy in various planes deteriorates
Solution Approach 1:
The patent segments the image into local regions and applies homography estimation only to regions where plane parallax is detected, rather than processing the entire image uniformly. This selective segmentation reduces processing time while maintaining high alignment accuracy in affected areas
Solution Approach 2:
The patent applies homography transformation only to the extent necessary for correcting plane parallax in specific regions, rather than applying a comprehensive global transformation. This partial action approach achieves sufficient alignment accuracy without the computational burden of full global processing
Data Source
AI summary
An apparatus and method for processing an image are disclosed. The method includes: segmenting both a first image and a second image and generating segmentation mask pairs, each segmentation mask pair having a segmentation mask of the first image and a segmentation mask of the second image; generating local homography matrices of the first image with respect to the second image, based on the segmentation mask pairs, the first image, and the second image; and generating a synthetic image obtained by aligning the first image with the second image, wherein the aligning is performed based on the local homography matrices, the segmentation mask pairs, the first image, and the second image.