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I have input RGB images as follows:

enter image description here

I have a dataset of manually annotated images highlighting the outline(edges) from the input images I am attaching an example.

enter image description here

My aim is to train a ML algorithm which learns how these outlines are mapped. And when given a new image it should produce similar output. Is there any way that this can be done?

Ive tried canny-edge detector,sobel and other type of open cv transforms. But they do not distinct object from their background properly. So I am looking into ML.

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  • $\begingroup$ It seems you have tried basic edge detection techniques, can you detail why you are not satisfied with them ? $\endgroup$
    – lcrmorin
    Jan 24, 2020 at 12:48

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What You are seeking is called 'Edge detection' in CVML fields. In my knowledge, DexiNed is one of best models. See papers in paperswithcode.

https://paperswithcode.com/task/edge-detection

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