MATLAB Implementation of RANSAC Algorithm for Image Matching

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Implementation of RANSAC algorithm for robust image matching by eliminating incorrect correspondence points through iterative model fitting and outlier rejection.

Detailed Documentation

To implement the RANSAC (Random Sample Consensus) algorithm in MATLAB, we utilize an image matching approach that improves matching accuracy by systematically removing incorrect correspondence points. The algorithm operates through iterative random sampling of minimal point sets to estimate transformation models (such as homography or fundamental matrix), followed by consensus evaluation to identify inliers. Key implementation steps include: randomly selecting minimal point subsets, computing candidate models, evaluating model support using distance thresholds, and retaining the model with maximum inliers. This process effectively handles outliers through probabilistic sampling and model validation cycles, making it particularly suitable for feature-based image stitching and computer vision applications where robust correspondence filtering is essential.

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