I have trained a classifier on my training images and experimented with different features. I have then used the probability map from the classifier as input to the MorphoLibJ plugin to get morphometry data, using the Morphological segmentation option (and objects rather than borders option).
The goal is given an aerial image of many leaves, occluding one another, to count the number and area of each leaf in the image. I get very good edge identification results if I use features like Laplacian, variance, sobel, or variance. However, the problem I am seeing is that if I train the trainable weka segmentation on the raw RGB image with one or more of these features results in classification results that are very good but the downstream morphometry results are not so good, and involve too many parameter choices. This is independent of whether I use probability maps or classification results as input to the morpholibj.
As mentioned above, features like laplacian or sobel, or simple convolution, on raw input RGB results in very good edge detection. I tried to preprocess the image with (a laplacian say) filter and use the resulting image as input to the trainable weka segmentor, but as anticipated the preprocessing with filters loses the green color information that is important for the pixel level classifier. I have used FeatureJ to check which features results in good edge information on the raw data and then use just one of those features for the weka segmentor but the results are not that great.
Do you have a recommendation on how I can address this problem?
thank you in advance for your help