Image Segmentation
This category contains algorithms that isolate relevant information from an image into a binary or label image.
- Binarization: Algorithms which allow transforming gray level or color images into a binary image.
- Labeling: Algorithms that create images where pixels belonging to a same entity are set to a same intensity (label).
- Feature Selection: Algorithms that improve segmentation based on topological criteria.
- Separating And Filling: Algorithms that improve segmentation by filling some area or separating connected objects.
- Region Growing: Algorithms that segment an image by propagating some seeds.
- Computational Geometry: Algorithms transforming images into geometric objects.
Segmentation is a critical step of image processing that consists of partitioning an image into multiple segments
(sets of pixels). The goal of segmentation is to simplify the representation of an image into something that is
easier to analyze.
The final goal of segmentation is generally to assign a label to every pixel of the input image such that pixels with the same label share certain visual characteristics (objects or regions). This segmented image can be then processed by analysis tools to make measurements on its components (see section Image Analysis).
The final goal of segmentation is generally to assign a label to every pixel of the input image such that pixels with the same label share certain visual characteristics (objects or regions). This segmented image can be then processed by analysis tools to make measurements on its components (see section Image Analysis).