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Segmentation partitions an image into regions (sets of pixels) to make it easier to analyze.
Image segmentation is the process of partitioning a digital image into multiple regions (segments or image objects, i.e., sets of pixels). Its purpose is to simplify or transform the image representation into something more meaningful and easier to analyze, commonly by locating objects and their boundaries (lines/curves) and producing either a full set of segments covering the image or extracted contours. A core definition of segmentation is assigning a label to every pixel such that pixels sharing the same label exhibit certain shared characteristics (e.g., similar color, intensity, or texture). Adjacent regions should differ significantly with respect to those same characteristics, and the segmentation result can be used for downstream tasks such as measurement, diagnosis, and 3D reconstruction in applications like medical imaging.
Segmentation partitions an image into regions (sets of pixels) to make it easier to analyze.
The segmentation goal is to assign a label to every pixel so that same-labeled pixels share characteristics, while neighboring regions differ.
The output is either a complete set of labeled segments or contours/boundaries useful for further processing (e.g., 3D reconstruction).
The process of partitioning a digital image into multiple segments (regions) by grouping pixels with similar properties.
Assigning a class/region label to every pixel so that pixels with the same label share specified characteristics.
A connected set of pixels in an image that are grouped together because they share certain characteristics.
Geometric outlines or extracted edges that represent the borders between segmented regions.
A segmentation approach that assigns a class label to every pixel without distinguishing between separate instances of the same class.
A segmentation approach that assigns a distinct instance identity to every pixel, separating different objects even if they share the same class.
A segmentation approach that combines semantic and instance segmentation by labeling each pixel’s class while also distinguishing different instances of the same class.
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