Image segmentation is a process in computer vision where a digital image is divided into multiple segments or sets of pixels. The goal of segmentation is to simplify or change the representation of an image into something more meaningful and easier to analyze. It aims to assign a label to every pixel in an image such that pixels with the same label share certain visual characteristics.
How Image Segmentation works
Image segmentation works by applying certain algorithms to clusters of pixels in an image, grouping together those with similar attributes. The basis for segmentation can vary, and could include color, intensity, texture, or other observable elements of the image data.
There are several approaches to image segmentation which include:
- Thresholding: It's the simplest method of image segmentation and often used in images with high contrast. Pixels are marked as "foreground"(object) or "background" based on pixel intensity.
- Clustering methods: This is a method where similar regions are grouped together. K-means is an example of a clustering method.
- Compression-based methods: This method involves the minimization of the cost of compression by creating more efficient and compact representation of the image.
- Edge detection: Edge detection involves identifying the edges of an object within an image. This is often used where the object to be segmented is in the foreground.
- Region-growing methods: This method involves selecting a seed, or starting point, and adding pixels or regions that are similar until the object of interest has been segmented.
These processes can make image analysis more efficient, aiding in tasks like object recognition, scene reconstruction, and even medical imaging diagnostics.
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