Are these all hyperparameters that we need to determine before the algorithm runs? How do we choose any one of these?
motoole2
How do we choose distance threshold?
It will depend on the problem. The slide recommends choosing a threshold such that, among all the inliers, 95% will be within the threshold. In practice though, this might be tricky to determine, because it's not always clear what are the inliers vs. outliers. It's something one often needs to empirically determine.
Are these all hyperparameters that we need to determine before the algorithm runs? How do we choose any one of these?
We typically choose them ahead of time, though there may be adaptive ways to choose them as well.
Number of sample points $s$: This will depend on your model.
Distance threshold: See above. When computing correspondences between two images, the distance threshold depends on the accuracy of the detector/descriptor/matching process.
Number of samples $N$: Choosing larger numbers of samples will always give you a better result. To ensure the code runs quickly, you want to choose a "reasonable" $N$ such that it produces a good result with some confidence (e.g., 99% confident that RANSAC will fit a model based on inliers). The equation/table on the slide will help you with choosing such a value.
how do we choose distance threshold?
Are these all hyperparameters that we need to determine before the algorithm runs? How do we choose any one of these?
How do we choose distance threshold?
It will depend on the problem. The slide recommends choosing a threshold such that, among all the inliers, 95% will be within the threshold. In practice though, this might be tricky to determine, because it's not always clear what are the inliers vs. outliers. It's something one often needs to empirically determine.
Are these all hyperparameters that we need to determine before the algorithm runs? How do we choose any one of these?
We typically choose them ahead of time, though there may be adaptive ways to choose them as well.
Number of sample points $s$: This will depend on your model.
Distance threshold: See above. When computing correspondences between two images, the distance threshold depends on the accuracy of the detector/descriptor/matching process.
Number of samples $N$: Choosing larger numbers of samples will always give you a better result. To ensure the code runs quickly, you want to choose a "reasonable" $N$ such that it produces a good result with some confidence (e.g., 99% confident that RANSAC will fit a model based on inliers). The equation/table on the slide will help you with choosing such a value.