The correct answer is: D. Fully Convolutional Network (FCN).
A fully convolutional network (FCN) is a type of deep learning model that is commonly used for image segmentation. FCNs are made up of a series of convolutional layers, which are able to extract features from images in a way that is similar to how the human visual cortex works. FCNs can be trained to classify each pixel in an image into a specific category or class, and they have been shown to be very effective for a variety of image segmentation tasks.
K-means clustering is a type of unsupervised learning algorithm that can be used to cluster data points into groups. K-means clustering is not typically used for image segmentation, as it does not take into account the spatial relationships between pixels.
A decision tree is a type of supervised learning algorithm that can be used to classify data points. Decision trees are not typically used for image segmentation, as they are not able to capture the spatial relationships between pixels.
A support vector machine (SVM) is a type of supervised learning algorithm that can be used to classify or regress data points. SVMs are not typically used for image segmentation, as they are not able to capture the spatial relationships between pixels.
In conclusion, the correct answer is: D. Fully Convolutional Network (FCN). FCNs are a type of deep learning model that is commonly used for image segmentation. FCNs are made up of a series of convolutional layers, which are able to extract features from images in a way that is similar to how the human visual cortex works. FCNs can be trained to classify each pixel in an image into a specific category or class, and they have been shown to be very effective for a variety of image segmentation tasks.