Image Segmentation Learning Path

Purpose and pixel labeling definition

Image segmentation is the process of partitioning a digital image into multiple regions (segments/objects). Its purpose is to simplify or transform an image into a representation that is more meaningful and easier to analyze, commonly by locating objects and their boundaries (e.g., lines and curves). In practice, segmentation produces either a full set of regions covering the image or contours extracted from the image (related to edge detection). A core definition of segmentation is pixel labeling: it assigns a label to every pixel such that pixels sharing the same label exhibit certain shared characteristics (such as color, intensity, or texture). Adjacent regions are expected to differ significantly with respect to those same characteristics. The output can be used for downstream tasks such as measurement, diagnosis, and 3D reconstruction in medical imaging, and it can also support different segmentation paradigms like semantic, instance, and panoptic segmentation.

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Segmentation outputs: regions and contours

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