CPC G06T 7/12 (2017.01) [G06F 18/2413 (2023.01); G06T 7/0002 (2013.01); G06T 7/10 (2017.01); G06T 7/11 (2017.01); G06T 7/194 (2017.01); G06V 10/454 (2022.01); G06V 10/56 (2022.01); G06V 10/764 (2022.01); G06V 10/82 (2022.01); G06V 10/993 (2022.01); G06V 40/193 (2022.01); G06V 40/197 (2022.01); G06T 2207/10024 (2013.01); G06T 2207/20076 (2013.01); G06T 2207/20081 (2013.01); G06T 2207/20084 (2013.01); G06T 2207/30041 (2013.01); G06T 2207/30168 (2013.01); G06T 2207/30196 (2013.01)] | 20 Claims |
1. A method for training a convolutional neural network for eye image segmentation and image quality estimation, the method being performed by a system of one or more processors, and the method comprising:
obtaining a training set of eye images;
providing a convolutional neural network with the training set of eye images;
and training the convolutional neural network with the training set of eye images, wherein the convolution neural network comprises a segmentation tower and a quality estimation tower, wherein the segmentation tower comprises segmentation layers and shared layers,
wherein the quality estimation tower comprises quality estimation layers and the shared layers,
wherein an output layer of the shared layers is connected to a first input layer of the segmentation tower and a second input layer of the segmentation tower,
and wherein the output layer of the shared layers is connected to an input layer of the quality estimation layer.
|