Possible use of machine learning for blob comparison
I currently work on a project in which I want to check images of blobs and tell if blob is OK, or NOK.
- ~200 8bit grayscale images with same size, unlabelled
- Each picture contains one black blob shape on white background.
Features that I currently have:
- contour shape
- contour volume
Classify if blob picture is OK, or NOK. Dataset is unlabelled, but I am able to manually label total OK and total NOK. But most of the blobs are somewhere close between NOK/OK. Labeling this ones will bring subjective error.
Also I want to be able to change threshold value for OK/NOK classification.
- Anomaly detection:
I did an Andrew Ng ML course on coursera. And I tried to implement anomaly detection with multivariate gaussian distribution. With change of epsilon threshold I was able to change number of classified OK/NOK blobs.
- Use convolutional NN (didn't try yet):
Train it on total OK/NOK blobs and implement possible threshold changer in classification layer. Is there any way how I can reuse trained NN, and only change classification layer? Do you know about any trained model, that can be used for this? Can you pinpoint some good material on this topic?
- Other possible approach?
What is other possible approach without convolutional NNs. I think I can get more features from data.