Image segmentation is an essential step in computer vision and image processing. It involves dividing an image into multiple segments or regions, each representing a distinct object or part of an object. This technique is widely used in various applications such as medical image analysis, autonomous driving, and object detection.

Common Segmentation Techniques

  1. Thresholding

    • Thresholding is a simple and efficient method to segment images. It involves setting a threshold value and classifying pixel values above the threshold as foreground and below the threshold as background.
    • Thresholding

  2. Region Growing

    • Region growing is a bottom-up segmentation technique. It starts with an initial seed point and expands the region by adding neighboring pixels that are similar to the seed point.
    • Region Growing

  3. Edge Detection

    • Edge detection is used to identify the boundaries of objects in an image. It helps in segmenting the image by identifying the regions where the intensity changes abruptly.
    • Edge Detection

  4. Flood Fill

    • Flood fill is a simple segmentation technique that starts with an initial seed point and fills the entire connected region with a specific color.
    • Flood Fill

  5. Contour Detection

    • Contour detection is used to find the boundaries of objects in an image. It helps in segmenting the image by identifying the regions where the intensity changes abruptly.
    • Contour Detection

For more information on image segmentation techniques, you can visit our Image Processing Tutorial.