Image segmentation is the process of dividing an image into multiple segments or regions, each of which corresponds to a different object or background in the image. The goal of image segmentation is to partition an image into coherent parts that correspond to different objects or parts of objects.
There are several different approaches to image segmentation, including:
Thresholding: This involves dividing an image into two or more regions based on the intensity values of the pixels. For example, pixels with intensity values above a certain threshold could be assigned to one region, while pixels with intensity values below the threshold could be assigned to another region.
Clustering: This involves grouping pixels together based on their similarity in terms of features such as color, texture, and intensity.
Edge detection: This involves finding the boundaries between different objects in an image by detecting changes in pixel intensity.
Region growing: This involves starting with a seed point and expanding a region to include nearby pixels that have similar features to the seed point.
Image segmentation is an important step in many images processing tasks, as it allows for more accurate analysis and interpretation of images. It is widely used in applications such as object recognition, image editing, and medical imaging.
