Homography Estimation via Multi-Resolution Segmentation
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Solution Overview
Problem
Conventional methods for homography estimation in high-definition and ultra high-definition image processing are inefficient, leading to long feature point extraction and outlier removal times, which can result in inaccurate or failed homography calculations, especially for large images.
Innovation Solution
A homography estimation apparatus and method that down-samples reference and target images to reduce feature point extraction time, uses feature point matching and outlier removal algorithms like SIFT and RANSAC, and estimates homography using Direct Linear Transform and Singular Value Decomposition, repeating the process multiple times to select an optimal homography for the original image.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If feature point extraction is performed on high-definition and ultra high-definition images using conventional methods, then accurate homography estimation can be achieved, but the processing time becomes excessively long
Solution Approach 1:
The patent segments the homography estimation process into multiple iterations, each operating on down-sampled images of different resolution levels. By dividing the original high-resolution image processing into multiple smaller processing steps at reduced resolutions, the overall computation time is significantly reduced while maintaining accuracy through iterative refinement.
Solution Approach 2:
The patent applies partial action by performing feature point extraction and matching on down-sampled images rather than the full-resolution original images. This partial processing at reduced resolution levels allows the system to obtain sufficient homography information without the computational burden of processing complete high-definition images, thereby reducing processing time while maintaining acceptable accuracy.
2Reliability
If conventional homography estimation methods are applied to ultra high-definition images, then comprehensive feature matching can be achieved, but the outlier removal process becomes inefficient
Solution Approach 1:
The patent segments the outlier removal process into multiple iterations corresponding to different down-sampling levels. By removing outliers progressively at each resolution level rather than attempting to process all features from the original high-resolution image in a single step, the system improves computational efficiency while maintaining reliable feature matching through cumulative refinement.
Solution Approach 2:
The patent performs preliminary outlier removal on down-sampled images before final homography calculation on the original image. By eliminating obvious outliers at reduced resolution levels first, the system prepares cleaner feature sets for subsequent processing, which improves both the efficiency and reliability of the final homography estimation.
3Manufacturing precision
If full-resolution images are processed for homography estimation, then maximum image quality is preserved, but the overall processing speed becomes unacceptably slow
Solution Approach 1:
The patent segments the processing pipeline into multiple stages with different resolution requirements. Down-sampled images are used for computationally intensive operations (feature extraction, matching, outlier removal), while the original full-resolution image is used only for the final homography application. This segmentation allows speed optimization at processing stages without sacrificing final image quality.
Solution Approach 2:
The patent creates down-sampled copies of the original high-resolution images for processing. These smaller copy images are used for all intermediate computational steps, allowing fast processing while the original full-resolution images remain intact for the final output, thus preserving maximum image quality without the speed penalty of processing full-resolution images throughout the entire pipeline.
Data Source
AI summary
Disclosed are a homography estimation apparatus and method. A down-sampling unit reduces the reference image and the target image to the same size to generate down-sampled images. A feature point extraction unit extracts feature points from the reference image and the target image down-sampled to the same size, respectively. An outlier removal unit matches the feature points extracted from the reference image with the feature points extracted from the target image, and detects and removes outliers, which are feature points that do not match, from the matched feature points. A homography estimation unit estimates a homography using feature points from which outliers are removed. A reference image correction unit corrects the reference image using the estimated homography.