Image segmentation

Image Segmentation

Image segmentation is a computer vision technique that divides an image into multiple segments or regions to simplify its representation and make it easier to analyze. In simpler terms, it's like coloring different parts of a picture so that a computer can understand and process each part separately.

  • Face Recognition: Segmenting facial images to extract and analyze facial features for biometric identification and authentication.
  • Tumor Detection: Segmenting medical images such as MRI or CT scans to identify and delineate tumors or other abnormalities.
  • Defect Detection: Segmenting images of manufactured products to identify and classify defects such as scratches, cracks, or misalignments for quality control and inspection.
                                                              


1. Semantic image segmentation:
  • Divide the image into meaningful segments or regions and assign a class label to each pixel based on what object or category it belongs to.
  • For example, pixels representing people may be labeled as "person," pixels representing cars may be labeled as "car," and so on.

2. Instance image segmentation:

  • For each detected object, further refine the segmentation by segmenting it into individual pixels and distinguishing between separate instances of the same object class.
  • Unlike semantic segmentation, which groups pixels into broad categories like "car" or "person," instance segmentation differentiates between distinct objects of the same class, such as different cars or people.
3. Panoptic image segmentation:

  • The results of semantic segmentation and instance segmentation are combined to create a unified segmentation map that provides both semantic category labels and instance-specific identifiers for every pixel in the image.








 


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