Bilinear Image Transformation via Surface Segmentation
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Solution Overview
Problem
Image processing techniques such as perspective transformation and equirectangular transformation require significant computational resources and time, making them inefficient.
Innovation Solution
The method involves dividing images into smaller surfaces and applying a bilinear transformation to each surface, which are then grouped together, allowing for a simpler and more resource-efficient transformation process with minimal error, and includes adaptive error checking to refine surface dimensions based on processing complexity.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If complex image processing (perspective transformation, equirectangular transformation) is applied, then transformation accuracy is improved, but computational resource consumption increases
Solution Approach 1:
The image is divided into multiple smaller surfaces (quadrilateral regions) before transformation. Each surface is processed independently using a simpler bilinear transformation, which reduces the computational complexity per region while maintaining overall accuracy through the cumulative effect of many small transformations.
Solution Approach 2:
The patent changes the transformation parameter from complex projective transformations to simpler bilinear transformations. This parameter change reduces computational resource consumption while maintaining sufficient accuracy for the application, as the bilinear transformation approximates the complex transformation adequately when applied to small surface regions.
2Measurement precision
If complex image processing is applied, then transformation accuracy is improved, but processing time increases
Solution Approach 1:
By segmenting the image into smaller surfaces, the patent reduces the processing time for each individual transformation operation. The simpler bilinear transformation applied to each small surface completes faster than a complex transformation applied to the entire image, while the cumulative accuracy is maintained through proper surface segmentation.
Solution Approach 2:
The patent applies a simpler bilinear transformation (partial action) to each small surface region rather than applying a complex transformation to the entire image. This partial application of a simpler operation achieves the same overall accuracy as a complex full-image transformation but with reduced processing time.
3Use of energy by moving object
If bilinear transformation is applied to each surface, then computational resource consumption is reduced, but transformation accuracy may deteriorate
Solution Approach 1:
The image is segmented into multiple small surfaces, and a bilinear transformation is applied to each. The segmentation ensures that the approximation error of the bilinear transformation remains small for each region, while the cumulative effect across all regions achieves the desired overall accuracy.
Solution Approach 2:
The patent applies different transformation qualities to different regions. Each small surface receives a bilinear transformation tailored to its local characteristics, which optimizes the balance between computational simplicity and local accuracy. This local quality approach ensures that each region is transformed accurately enough for the overall image.
4Measurement precision
If image is divided into smaller surfaces, then bilinear transformation accuracy is improved, but device complexity increases
Solution Approach 1:
The image is divided into a grid of quadrilateral surfaces, which provides a systematic and regular segmentation pattern. This regular grid structure simplifies the implementation complexity compared to irregular segmentation, as the same algorithm can be applied uniformly to all regions without requiring complex adaptive division logic.
Data Source
AI summary
Geometric method of transforming a first two-dimensional image into a second two-dimensional image through an image processing applied to the first images or to the second image. In this method, one of said first and second images is divided into several surfaces, each of the surfaces of the divided image is transformed by a bilinear transformation specific to each surface, and the transformed surfaces are grouped together.