I am learning image segmentation,but I can not understand neural network about image segmentation,such as unet,fcn.How can I understand the principle thoroughly.
Hello Dr Li:
How does a UNet work?
UNet uses a rather novel loss weighting scheme for each pixel such that there is a higher weight at the border of segmented objects. ... First of all pixel-wise softmax applied on the resultant image which is followed by cross-entropy loss function. So we are classifying each pixel into one of the classes.
https://towardsdatascience.com/u-net-b229b32b4a71
What is FCN network?
FCN is a network that does not contain any “Dense” layers (as in traditional CNNs) instead it contains 1x1 convolutions that perform the task of fully connected layers (Dense layers). ... Building a fully convolutional network (FCN) in TensorFlow using Keras.
https://towardsdatascience.com/implementing-a-fully-convolutional-network-fcn-in-tensorflow-2-3c46fb61de3b
Regards-
thank you,but I can not understand how the machine learning or neural network works on the image segmentation area.I can not understand it vividly,can you tell it popularly?thanks
Dear Shoucong Li ,
Look the link, maybe useful.
https://towardsdatascience.com/understanding-semantic-segmentation-with-unet-6be4f42d4b47
Regards,
Shafagat
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