Can we start with the circle equation as decision boundary?

Since you are asking about CV (Computer Vision).
Usually here you have a different paradigm: Deeplearning basically takes care of Feature Engineering on its own to learn more abstract and complex patterns, e.g. with convolutional filters and hierarchical pooling.

In this thread some more thoughts are explained in a more detailed way: Do traditional algorithms perform better than CNN? - #2 by Christian_Simonis

So since a well-trained CV model learned so much already with DL, probably a polynomial feature will not have much impact (at least I do not see it yet currently). Still, transforming pictures or getting additional dimensions (e.g. with LiDAR / Radar / …) with sensing or data fusion can definitely help by enabling the DL model to learn better due to better data.

Hope that answers your question, @mehmet_baki_deniz.

Best regards