thank you for the response. I have tried that strategy, with the difference that I only had 2 classes ('leaf' and 'other'), and borders were member of
Other class. The results were not that good. May I ask,
1- which features did you use for the above and what sigmas?
2- as you saw the laplacian/sobel filters do a good job finding the borders. So rather than going through expensive edge labeling exercise, i wrote a utility to post process a probability map given the binary mask of the edges detected by a (gaussian blur+sobel) filter from the original image. If pixel value at binary mask xi,yi == 0 then we keep the prob map value, else we set it to zero. does this make sense to you?
3- do you know if there is an implementation of a Conditional Random Field for postprocessing images in imagej?
Liang-Chieh, C., Papandreou, G., Kokkinos, I., Murphy, K., Yuille, A.: Semantic image
segmentation with deep convolutional nets and fully connected crfs. In: International Conference
on Learning Representations (ICLR). (2015)
Zheng, S., Jayasumana, S., Romera-Paredes, B., Vineet, V., Su, Z., Du, D., Huang, C., Torr,
P.H.: Conditional random fields as recurrent neural networks. IEEE International Conference
on Computer Vision (ICCV) (2015)
Parameter Learning and Convergent Inference for Dense Random Fields
thank you ignacio