From 36af191d23b4917a46dc1f0757b04ad9c8c8145d Mon Sep 17 00:00:00 2001 From: Mathieu Gravey Date: Tue, 3 Oct 2023 14:32:51 +0200 Subject: [PATCH] Update QuickSampling.md --- algorithms/QuickSampling.md | 4 ++++ 1 file changed, 4 insertions(+) diff --git a/algorithms/QuickSampling.md b/algorithms/QuickSampling.md index 6eca026..8a4d677 100644 --- a/algorithms/QuickSampling.md +++ b/algorithms/QuickSampling.md @@ -35,12 +35,16 @@ Outputs: sim = simulation, index = index of the simulated values in the flattene | Flag | Description | Mandatory | | ---- | ----------- | --------- | | `-ii` | Array that specifies for each pixel which training image to sample from. Default: all training images are searched for the best match. | | +| `-ni` | Array that specifies for each pixel which the number of neighbors. | | +| `-kvi` | Array that specifies for each pixel which the number of best candidates to consider. | | | `-far` | Fast and risky 😄, like -ii but with a random input (experimental). | | | `-cti` | With this flag QS will treat the training image(s) as periodic (aka circular or cyclic) over each dimension. | | | `-csim` | With this flag QS will make sure to create a periodic (aka circular or cyclic) simulation over each dimension. | | | `-adsim` | Augmented dimentionality simulation: allows for 3D simulation using 2D training image, only for categories (Coming maybe some day!). | | | `-fs` | Full simulation: follows a different simulation path for each variable (as opposed to vector simulation, where the same simulation path is used for all variables). | | | `-nV` | No Verbatim, i.e. prohibits neighbors in the training image to be neighbors in the simulation. (experimental). | | +| `--forceSimulation` | Restimulate a value even if already existing. | | + ## Examples Below are several examples showcasing different applications of QS. For these examples the G2S server should be installed and running, either on your own machine or remotely. A Google Colab notebook with more examples and an automatic installation of G2S can be found [here](https://github.com/GAIA-UNIL/Short-course-MPS/blob/main/MPS_SC_with_QS_Online.ipynb).