Stitched Image Pixel Density Mapping for Deformation Correction
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Solution Overview
Problem
Existing image stitching methods introduce deformation, leading to non-uniform pixel densities across stitched images, which affect image quality and alignment, and current manual measurement methods are labor-intensive and difficult to scale.
Innovation Solution
Automatically measure pixel density using synthetic images with fixed patterns, applying computer vision techniques to segment and analyze deformed features, generating a pixel density map to adjust stitching parameters for uniformity.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual measurement methods are used to determine pixel density, then measurement accuracy can be achieved, but labor intensity increases and scalability decreases
Solution Approach 1:
The patent uses synthetic images as copies or simulations of real captured images. These synthetic images contain known geometric patterns (such as grids or markers) with predetermined pixel densities. By processing these synthetic copies instead of real images, the system can automatically calculate pixel density measurements without manual intervention, thus maintaining measurement accuracy while dramatically improving efficiency and scalability.
Solution Approach 2:
The measurement system performs self-service by automatically detecting features in synthetic images and computing pixel density without human assistance. The synthetic images are designed with self-identifying features that enable automated algorithms to locate, measure, and calculate pixel density values independently, eliminating the need for manual measurement while maintaining precision.
2Adaptability or versatility
If image stitching is performed to combine multiple camera views, then a comprehensive environmental representation is achieved, but deformation is introduced that affects pixel density uniformity
Solution Approach 1:
The patent implements a feedback mechanism where pixel density measurements obtained from synthetic images are used to evaluate and adjust the stitching process. By measuring pixel density in controlled synthetic scenarios first, the system establishes reference values that can guide the stitching algorithm to maintain uniform pixel density across the composite image, thus preserving measurement precision while achieving comprehensive environmental coverage.
Solution Approach 2:
The system performs preliminary measurements on synthetic images before actual stitching operations. By pre-characterizing the pixel density properties of synthetic test images with known geometries, the system can anticipate and compensate for potential deformations during stitching, ensuring that the final stitched images maintain uniform pixel density across all regions.
Data Source
AI summary
A first synthetic image including a first pattern and a second synthetic image including a second pattern are processed to generated a stitched image comprising a modified version of the first synthetic image and a modified version of the second synthetic image. One or more features of the first pattern are deformed in the modified version of the first synthetic image and one or more features of the second pattern are deformed in the modified version of the second synthetic image. Pixel areas are determined for each feature of the modified version of the first synthetic image and for each feature of the modified version of the second synthetic image. Feature densities are determined for one or more regions of the stitched image based on the determined pixel areas.