Random Sample Consensus (RANSAC) Algorithm, A Generic Implementation
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Please use this identifier to cite or link to this publication: http://hdl.handle.net/10380/3223
The Random Sample Consensus (RANSAC) algorithm for robust parameter value estimation has been applied to a wide variety of parametric entities (e.g. plane, the fundamental matrix). In many implementations the algorithm is tightly integrated with code pertaining to a specific parametric object. In this paper we introduce a generic RANSAC implementation that is independent of the estimated object. Thus, the user is able to ignore outlying data elements potentially found in their input. To illustrate the use of the algorithm we implement the required components for estimating the parameter values of a hyperplane and hypersphere.
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minus Automatic Testing Results by Insight-Journal Dashboard on Mon Nov 22 09:37:24 2010 for revision #1
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minus Excellent addition! by David Doria on 2010-10-22 08:13:09 for revision #1
starstarstarstarstar expertise: 3 sensitivity: 3.5
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Summary:

The authors have implemented a templated, general version of the RANSAC algorithm.

Evidence:

The authors demonstrate their classes by performing plane and sphere parameter estimation.

Open Science:

The data from which the parameters is estimated is generated by the code. Everything is reproducible.

Reproducibility:

I downloaded the code and it compiled on the first try. I ran the planeEstimation executable and it worked as expected.

Use of Open Source Software:

No, this is not expected from a submission.

Open source Contributions:

Yes, it is very usable. The usage is clearly described as well as demonstrated. To use the code only took seconds, and it only took a few minutes to look through it to get an idea of how to expand it for other estimation tasks.

Code Quality :

The code was easy to read. A modern coding style (templates) was used throughout. I have only tested the code on Fedora 13, but I don't see anything obviously non-portable.

Quality of the data :

The data is generated by the code.

Interest:

The authors mentioned that RANSAC is often used for fundamental matrix operation for camera calibration. This would certainly be an excellent addition!

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Categories: Hypothesis Testing, Optimization
Keywords: RANSAC, robust estimation, hyperplane, hypersphere
Toolkits: ITK
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