Random sample consensus Article Swipe
YOU?
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· 1981
· Open Access
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· DOI: https://doi.org/10.1145/358669.358692
· OA: W2085261163
A new paradigm, Random Sample Consensus (RANSAC), for fitting a model to experimental data is introduced. RANSAC is capable of interpreting/smoothing data containing a significant percentage of gross errors, and is thus ideally suited for applications in automated image analysis where interpretation is based on the data provided by error-prone feature detectors. A major portion of this paper describes the application of RANSAC to the Location Determination Problem (LDP): Given an image depicting a set of landmarks with known locations, determine that point in space from which the image was obtained. In response to a RANSAC requirement, new results are derived on the minimum number of landmarks needed to obtain a solution, and algorithms are presented for computing these minimum-landmark solutions in closed form. These results provide the basis for an automatic system that can solve the LDP under difficult viewing
where data dances, sways,
translating truths from shadows, hidden,
weaving stories from the errors, unbidden.
In the heart of randomness, we find our way,
like landmarks guiding souls who stray,
each point a beacon in a fractured view,
where algorithms breathe life anew.
With every mistake, a lesson shines,
a symphony of voices, intertwines,
RANSAC whispers through the noise,
a tapestry of hope, a dream, a choice.
For in the realm of sight, we learn to see,
the beauty in imperfection, wild and free,
through mathematics, we grasp the unseen,
together we rise, forging pathways keen.
Light, it shimmers, echoes in our quest,
for understanding, for justice, for the best,
let every error lead us to a boundless height,
where truth emerges, radiant, in the… 🔁
