Image Stitching Alignment Verification for High-Quality Panorama Generation
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Solution Overview
Problem
Existing image stitching algorithms for generating panoramas face challenges in ensuring the alignment of image centers, leading to deteriorated quality due to uncertainties in the imaging device's pose and shooting environment, making it difficult to verify if the image centers are within a tolerance range.
Innovation Solution
A method that determines the alignment of images by calculating matrices and rotational relationships between adjacent images, optimizing image centers, and projecting image points to assess alignment based on distances and statistical conditions, ensuring that the image centers meet preset criteria for generating high-quality panoramas.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of manufacture
If image stitching is performed based on graphic information alone, then the processing is simple, but the panorama quality deteriorates when image centers are misaligned
Solution Approach 1:
The system performs preliminary verification of image center alignment before executing the image stitching process. By calculating matrices and checking alignment conditions in advance, the system prevents quality deterioration from misaligned images while maintaining simple stitching processing for properly aligned images.
Solution Approach 2:
The system implements a feedback mechanism where the alignment verification results directly control whether stitching should proceed. The alignment check provides feedback about image quality conditions, and the stitching process is conditionally executed based on this feedback, ensuring quality while maintaining simplicity.
2Measurement precision
If precision instruments are used to control imaging device pose, then the pose control is improved, but it remains difficult to verify whether image center displacement is within tolerance
Solution Approach 1:
The system introduces an intermediary verification process that uses matrix calculations derived from graphic information in the images themselves to assess alignment. This intermediary method bridges the gap between precise pose control and actual image center alignment verification, providing a practical check without requiring additional precision instruments.
3Manufacturing precision
If alignment verification is performed using multiple images and optimization, then the alignment accuracy is improved, but the processing complexity increases
Solution Approach 1:
The system segments the alignment verification process into distinct steps: matrix calculation from graphic information, parameter determination, alignment condition checking, and conditional stitching. This segmentation makes the complex process more manageable and systematic, improving alignment accuracy through structured processing.
Data Source
AI summary
A method for image processing is disclosed herein. A computer system obtains a first image and a second image acquired by an imaging device. The computer system determines a matrix relating a first set of image points in the first image to a second set of image points in the second image. Based on the matrix, the computer system determines a set of parameters of the imaging device and a rotational relationship between the first image and the second image corresponding to poses of the imaging device. In response to determining that the first image and the second image are aligned based on the set of parameters of the imaging device and the rotational relationship between the first image and the second image, the computer system generates a composite image of the first image and the second image and causes display of the composite image.