Geometry-Based Image Orientation for Panoramic Stitching
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Solution Overview
Problem
Existing image stitching methods are time-consuming and impractical for manual processing, especially in medical imaging, and struggle with generating panoramic images when there is insufficient overlap between images or when features change, such as in dynamic scenes like human skin or fast-moving objects.
Innovation Solution
A method using geometry-based image orientation that identifies boundary features and points within images, determines parameters for a geometrical shape, and positions images accordingly to create a panoramic image, even when images do not share common features or have insufficient overlap.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If traditional feature-based image stitching methods are used, then image stitching can be performed, but the process is time-consuming and impractical for manual processing
Solution Approach 1:
The patent extracts and removes the time-consuming feature matching and manual adjustment steps from the traditional image stitching process. By using pre-identified boundary features and geometric shape parameters, the method eliminates the need for time-intensive feature detection and matching operations, achieving automated rapid stitching.
Solution Approach 2:
The patent performs preliminary actions by pre-identifying boundary features and determining geometric shape parameters before the actual stitching operation. The boundary features and geometric parameters are established in advance, allowing the stitching process to proceed quickly without real-time feature matching.
2Reliability
If traditional image stitching methods are used, then panoramic images can be generated, but they struggle when there is insufficient overlap between images or when features change
Solution Approach 1:
Instead of relying on common features between overlapping images, the patent inverts the approach by using boundary features and geometric shape parameters that remain stable even when images are discontinuous or features change. This inverted methodology makes the stitching process independent of image overlap and feature consistency.
Solution Approach 2:
The patent changes the fundamental parameters used for stitching from feature-based parameters to geometry-based parameters. By using geometric shape parameters (such as circle, ellipse, or parabola parameters) that describe the boundary features, the method adapts to discontinuous scenes and changing features where traditional feature matching fails.
3Ease of operation
If manual image stitching is performed, then some control is maintained, but the process is time-consuming and impractical
Solution Approach 1:
The patent implements self-service by enabling the system to automatically identify boundary features, determine geometric parameters, and perform stitching without manual intervention. The automated geometric parameter-based method eliminates the need for manual feature matching and image alignment, achieving both ease of operation and high productivity.
Data Source
AI summary
Within a first image in a set of images comprising a panoramic view of a scene, a first boundary feature is identified using a model. Within the first boundary feature, a first set of points is selected. Within a second image in the set of images, a second boundary feature is identified using the model. Within the second boundary feature, a second set of points is selected. A set of parameters of a geometrical shape including the first set of points and the second set of points is determined. From the first image and the second image, a panoramic image is formed, the first image placed within the panoramic image according to the position of the first image on the geometrical shape, the second image placed in the panoramic image according to the position of the second image on the geometrical shape.