Processes in Semantic Segmentation
Semantic segmentation is based on a deep learning architecture called image segmentation convolutional neural network (CNN) which is built for image segmentation. Given below is a brief synopsis of its working (from preparation to output).
Prior to the starting of the segmentation process, images are pre-processed to ensure they are made suitable for analysis. To ensure the input is consistent and optimal, the images are resized, noise is reduced, and color is normalized.
The next step involves passing the pre-processed images via a neural network for extracting the pertinent features that are necessary for the the particular use case. The images are analyzed and distinct patterns, shapes and textures are identified to differentiate different objects or regions.
This phase starts after the extraction of relevant features. It involves classifying every pixel and assigning a label or class. It enables machine learning models to distinguish between various objects.
Visualization of semantic segmentation results involves overlaying a segmentation mask to the image, highlighting a particular class or object of interest to assist in distinguishing the identified objects from the remaining image.