CPC G06T 7/149 (2017.01) [G06N 3/04 (2013.01); G06N 3/08 (2013.01); G06T 7/11 (2017.01); G06T 2207/20081 (2013.01); G06T 2207/20084 (2013.01); G06T 2207/20116 (2013.01)] | 18 Claims |
1. A method for generating image segmentations from an input image, the method comprising:
receiving an input image;
providing the input image to a Convolutional Neural Network (CNN) backbone;
generating, using the CNN backbone, a set of parameter maps λ_1 and λ_2 from the input image;
generating, using the CNN backbone, an initialization map from the input image;
receiving, using an automatically differentiable Active Contour Model (ACM) comprising L layers, the parameter maps λ_1 and λ_2 and the initialization map;
generating, using the differentiable ACM, an image segmentation based on the set of parameter maps λ_1 and λ_2 and the initialization map;
comparing the image segmentation with a ground-truth label of the input image to compute a set of one or more losses; and
backpropagating the set of one or more losses to update the differentiable ACM and the CNN backbone.
|