Multi-Camera Image Stitching via Localized Feature Alignment
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Solution Overview
Problem
The process of stitching images captured by a multi-camera array often results in stitching artifacts due to differences in object movement, parallax error, image feature complexity, and texture variations, which can lead to disfigurement of facial features and noticeable visual disruptions in the final stitched image.
Innovation Solution
The solution involves optimizing the camera configuration to minimize parallax error and using advanced stitching algorithms that analyze image features, depth, and motion information to align and warp image data, with the quality of stitching operations selected based on the proximity of the view window to the overlap region and the importance of image features, thereby reducing artifacts.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Area of stationary object
If images are stitched together using a multi-camera array, then the field of view and image data are expanded, but stitching artifacts appear at or near the stitch lines
Solution Approach 1:
The patent applies different stitching operations to different regions of the image based on local characteristics. High-quality stitching operations are applied to regions containing important features (such as faces) or regions close to the view window, while lower-quality operations are applied to other areas. This localized approach maintains image quality in critical regions while reducing overall processing complexity and artifact visibility.
Solution Approach 2:
The patent dynamically adjusts stitching parameters based on the importance of image features and proximity to the view window. The stitching operation quality is modulated by parameters such as feature importance scores and distance to the view window, allowing the system to adapt the stitching process to local requirements and minimize artifacts in visually significant areas.
2Object-affected harmful factors
If advanced stitching algorithms are used to reduce artifacts, then image quality is improved, but processing complexity and time increase
Solution Approach 1:
The patent divides the stitching process into multiple stages and operations. It segments the image into different regions based on feature importance and view window proximity, applying different stitching algorithms to each segment. This segmentation allows the system to use computationally intensive algorithms only where necessary, reducing overall processing complexity while maintaining quality in critical areas.
Solution Approach 2:
The patent applies high-quality stitching operations selectively to only the most important regions of the image rather than uniformly across the entire image. By performing partial action on critical areas (faces, features near the view window) and using simpler operations elsewhere, the system achieves artifact reduction where it matters most without incurring the full processing cost across the entire image.
3Ease of operation
If stitching operations are applied uniformly across all regions, then processing is simplified, but important features like faces may be disfigured
Solution Approach 1:
The patent implements local quality by selecting and applying different stitching operations to different regions based on the presence and importance of image features. Regions containing important features such as faces receive high-quality stitching operations with careful alignment and blending, while other regions use simpler operations. This approach maintains processing simplicity overall while ensuring precision in critical areas.
Solution Approach 2:
The patent uses feedback mechanisms to identify important features in the image and adjust the stitching operation accordingly. The system analyzes the image content, determines feature importance, and uses this information to select appropriate stitching algorithms for different regions. This feedback-driven approach ensures that feature alignment accuracy is maintained in critical areas without requiring complex uniform processing across the entire image.
Data Source
AI summary
Images captured by multi-camera arrays with overlap regions can be stitched together using image stitching operations. An image stitching operation can be selected for use in stitching images based on a number of factors. An image stitching operation can be selected based on a view window location of a user viewing the images to be stitched together. An image stitching operation can also be selected based on a type, priority, or depth of image features located within an overlap region. Finally, an image stitching operation can be selected based on a likelihood that a particular image stitching operation will produce visible artifacts. Once a stitching operation is selected, the images corresponding to the overlap region can be stitched using the stitching operation, and the stitched image can be stored for subsequent access.