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RANSAC (Random Sample Consensus)

RANSAC, or Random Sample Consensus, is a robust method used to identify patterns in data that may contain outliers—incorrect or noisy data points. It works by repeatedly selecting a small random subset of points to create a model and then testing how well this model fits the rest of the data. By determining which points fit well (inliers) and which do not (outliers), RANSAC effectively improves the accuracy of the model. This approach is widely used in applications like computer vision and machine learning, where data can be unreliable or messy.

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  • Image for RANSAC (Random Sample Consensus)

    RANSAC, or RANdom SAmple Consensus, is a robust algorithm used in data analysis to identify a reliable trend or model in a set of data that may contain outliers. It works by randomly selecting a subset of data points to create a model, then checking how many other data points fit this model well. This process is repeated multiple times to find the best-fitting model with the most inliers—data points that conform to the model. RANSAC is widely used in computer vision, robotics, and other fields for tasks like fitting lines or shapes to noisy data.