Commit 2682c3d9 authored by Christian Tischer's avatar Christian Tischer

EMBL Course

parent 32a08fc2
......@@ -8,6 +8,7 @@
"binary image" -> "background value";
"binary image" -> "foreground value";
"intensity image" -> "machine learning";
"annotations" -> "machine learning";
"machine learning" -> "pixel class image";
"pixel class image" -> "class00 value";
"pixel class image" -> "class01 value";
......@@ -16,19 +17,51 @@
}
'/>
 
 
 
## Decision tree based image segmentation
<img src='https://g.gravizo.com/svg?
digraph G {
shift [fontcolor=white,color=white];
"intensity image" -> "filter00 image" -> "Decision tree(s)";
"intensity image" -> "filter01 image" -> "Decision tree(s)";
"intensity image" -> "filter02 image" -> "Decision tree(s)";
"intensity image" -> "filter.. image" -> "Decision tree(s)";
"intensity image" -> "filter F image" -> "Decision tree(s)";
"Intensity image" -> "filter00 image" -> "Decision tree(s)";
"Intensity image" -> "filter01 image" -> "Decision tree(s)";
"Intensity image" -> "filter02 image" -> "Decision tree(s)";
"Intensity image" -> "filter.. image" -> "Decision tree(s)";
"Intensity image" -> "filter F image" -> "Decision tree(s)";
"Annotations" -> "Decision trees(s)"
"Decision tree(s)" -> "class00 (probability) image";
"Decision tree(s)" -> "class01 (probability) image";
"Decision tree(s)" -> "class.. (probability) image";
"Decision tree(s)" -> "class C (probability) image";
}
'/>
## Activity: Semantic image segmentation
- Open image: xy_8bit__em_fly_eye.tif
- Segment three classes: background, eye, other
- Choose image filters
- Draw few labels in the blurry image background => class00
- Draw few labels on the eye => class01
- Draw few labels on other parts of the animal => class02
- While( not happy):
- Train the classifier
- Inspect the predictions
- Add more labels where the predictions are wrong
TODO: use multiple files to demo that a classifier can be applied on other images.
## Formative assessment
True or false? Discuss with your neighbour!
- In contrast to simple thresholding, using machine learning for pixel classification, one always has more than 2 classes.
- If one wants to learn 4 different classes one has to, at least, add 4 annotations on the training image.
- One cannot classify an image where one did not put any training annotations.
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