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Jul 3, 2021 · In this post I showed how to implement well-known RANSAC algorithm, with non-linear curve fitting as an usage example. In my implementation, I tried to isolate the core RANSAC algorithm, so...
Random sample consensus (RANSAC) is an iterative method to estimate parameters of a mathematical model from a set of observed data that contains outliers, when outliers are to be accorded no influence on the values of the estimates.
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Jan 31, 2020 · Introduction. In this article we will explore the Random Sample Consensus algorithm — more popularly known by the acronym RANSAC. This is an iterative and a non-deterministic algorithm that...
- RANSAC [Fischler & Bolles 81]
- RANSAC Parameters
- Shortcomings of ‘Plain’ RANSAC
- Noisy Inliers
- Shortcomings of ‘Plain’ RANSAC
- Shortcomings of ‘Plain’ RANSAC
- Shortcomings of ‘Plain’ RANSAC
- Hypothesis Generation
- PROSAC
- Shortcomings of ‘Plain’ RANSAC
- Shortcomings of ‘Plain’ RANSAC
- RANSAC Conclusion
Hypothesize-and-verify framework Sample hypothesis and verify with data Assumptions Outliers provide inconsistent (ie. random) votes for models There are sufficiently many inliers to detect a correct model Hypothesis generation Sample subset of data points and fit model parameters to this subset Plain RANSAC: sample points uniformly at random Verif...
How to find inlier ratio? Provide lower bound for initialization and recompute when new best hypothesis has been found Scale of inlier noise Confidence for having sampled at least one all-inlier sample
Scale of inlier noise (for inlier-outlier threshold) needs to be specified Correct model is not generated with user-defined confidence Estimated model might be inaccurate Degenerate cases not handled Can be sped up considerably Better hypothesis generation Faster verification schemes Multiple models Model selection Interesting problem, but not cove...
Problem: not every all-inlier-sample provides a good solution Sampling more than one all-inlier-set might be necessary! In practice, solution often found only after roughly iterations Simple calculation is inaccurate
Scale of inlier noise (for inlier-outlier threshold) needs to be specified Correct model is not generated with user-defined confidence Estimated model might be inaccurate Degenerate cases not handled Can be sped up considerably Better hypothesis generation Faster verification schemes Multiple models Model selection Interesting problem, but not cove...
Scale of inlier noise (for inlier-outlier threshold) needs to be specified Correct model is not generated with user-defined confidence Estimated model might be inaccurate Degenerate cases not handled Can be sped up considerably Better hypothesis generation Faster verification schemes Multiple models Model selection Interesting problem, but not cove...
Scale of inlier noise (for inlier-outlier threshold) needs to be specified Correct model is not generated with user-defined confidence Estimated model might be inaccurate Degenerate cases not handled Can be sped up considerably Better hypothesis generation Faster verification schemes Multiple models Model selection Interesting problem, but not cove...
Trade-off between exploration and exploitation Previously verified hypothesis tell us something about inlier set Still, we should avoid narrowing our search too quickly Especially important for multi-model case Eg. estimation of multiple planes in a scene Points on other planes act as outliers to plane under consideration
“Matching with PROSAC – progressive sample consensus” Chum & Matas [CVPR05] Use of a-priori knowledge Confidence of a matching pair (eg. based on descriptor matching distance) PROSAC: Favor high-quality matches while sampling points for minimal sample Sort correspondences according to matching score Consider progressively larger subsets of putative...
Scale of inlier noise (for inlier-outlier threshold) needs to be specified Correct model is not generated with user-defined confidence Estimated model might be inaccurate Degenerate cases not handled Can be sped up considerably Better hypothesis generation Faster verification schemes Multiple models Model selection Interesting problem, but not cove...
Scale of inlier noise (for inlier-outlier threshold) needs to be specified Correct model is not generated with user-defined confidence Estimated model might be inaccurate Degenerate cases not handled Can be sped up considerably Better hypothesis generation Faster verification schemes Multiple models Model selection Interesting problem, but not cove...
Many different ‘flavours’ Still an active research area
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RANSAC: Pros and Cons. Pros: General method suited for a wide range of model fitting problems. Easy to implement and easy to calculate its failure rate. Cons: Only handles a moderate percentage of outliers without cost blowing up.
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Apr 14, 2023 · Ransack needs ActsAsTaggableOn::Tag attributes explicitly allowlisted as searchable. Define a ransackable_attributes class method in your ActsAsTaggableOn::Tag
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activerecord-hackery.github.io · ransack · gettingSimple mode | Ransack documentation - GitHub Pages
Simple Mode. Ransack can be used in one of two modes, simple or advanced. For searching/filtering not requiring complex boolean logic, Ransack's simple mode should meet your needs. In your controller. def index. @q = Person.ransack(params[:q]) @people = @q.result(distinct: true) end.
