Image segmentation is a fundamental computer vision technique that involves dividing an image into distinct, meaningful regions or segments. These segments are typically based on shared characteristics, such as color, texture, or object boundaries, making it easier to analyze and interpret the contents of an image.
The process of image segmentation serves as a critical pre-processing step in various computer vision applications, allowing machines to isolate and identify objects or regions of interest within an image. For instance, in medical imaging, image segmentation can be used to detect and delineate tumors, while in autonomous driving systems, it helps identify pedestrians and road signs.
5 Image Segmentation Techniques:
- Threshold-Based Segmentation: dividing pixels based on their intensity relative to a given value or threshold.
- Watershed Segmentation: detects lines forming ridges and basins and divides images into regions based on pixel height.
- Edge-based Segmentation: identify edges based on contrast, texture, color, and saturation to remove redundant information.
- Cluster-based Segmentation: divides images into clusters of pixels with similar characteristics to group them into clusters.
- Region-based Segmentation: By dividing an image into regions with similar characteristics, the algorithm can then grow these regions by adding more pixels or shrinking/merging them with other points.
Image segmentation is important in image annotation, where it creates labeled datasets for training machine learning models. Accurate segmentation ensures that objects of interest are precisely outlined and labeled.
Image segmentation facilitates object detection, recognition, and analysis, ultimately powering a wide range of applications across industries.