Image Layout Using Geometric Transformations and Overlap Analysis
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Solution Overview
Problem
Existing image stitching technologies face challenges in aligning multiple images without introducing unwanted distortions, particularly when capturing panoramic views, as they often require user input and struggle with projective distortions caused by varying viewpoints.
Innovation Solution
The method identifies geometric transformations to align images to a common reference frame, determines overlapping image regions, and applies additional transformations such as 2D translations, rotations, and scaling to reposition images seamlessly, using connectivity graphs and projective transformations to minimize distortions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If automatic image stitching is used to join multiple images, then productivity is improved, but manufacturing precision deteriorates due to projective distortions
Solution Approach 1:
The system performs preliminary actions by identifying geometric transformations and determining overlapping image regions before applying final transformations. This preliminary analysis of image overlaps and transformation relationships enables the system to pre-calculate the necessary adjustments, thereby maintaining high productivity while ensuring precise alignment in the final composite image.
Solution Approach 2:
The system changes transformation parameters by determining additional transformations of a specified type based on overlapping image regions. By adjusting transformation parameters (such as translation, rotation, and scaling) based on the actual overlap analysis, the system resolves projective distortions while maintaining efficient automated processing.
2Manufacturing precision
If geometric transformations are applied to align images, then manufacturing precision is improved, but device complexity increases
Solution Approach 1:
The system segments the image stitching process into distinct stages: identifying geometric transformations, determining overlapping regions, and applying additional transformations. This segmentation allows each stage to be processed independently and efficiently, reducing overall system complexity while maintaining high alignment precision through systematic step-by-step processing.
Solution Approach 2:
The system introduces intermediary elements (overlapping image regions and transformation parameters) that mediate between the input images and the final composite. These intermediaries simplify the complex transformation process by breaking it down into manageable steps, making the overall system more tractable while achieving precise alignment.
3Manufacturing precision
If additional transformations are determined based on overlapping regions, then manufacturing precision is improved, but loss of time increases
Solution Approach 1:
The system performs preliminary determination of overlapping image regions and transformation relationships before applying final transformations. By pre-calculating the overlap geometry and transformation parameters in advance, the system reduces the time required for final image layout while maintaining high precision through thorough preliminary analysis.
Data Source
AI summary
Systems, methods, and apparatuses, including computer program products, are provided for re-layout of composite images. In some implementations, a method includes identifying geometric transformations corresponding to multiple images from a collection of images, where a geometric transformation reorients a corresponding image in relation to a common reference frame when applied and identifying a reference image for the multiple images in the collection of images. The method also includes determining overlapping image regions for the multiple images starting from the reference image, the determining based on the identified geometric transformations, determining additional transformations of a specified type for the multiple images based on the overlapping image regions, where an additional transformation lays out a corresponding image in relation to the reference image when applied, and making the additional transformations available for further processing and output with respect to the collection of images.