Segmentation: Edge-based

• Edge-based segmentation represents a large group of methods based on information about edges in the image
• Edge-based segmentations rely on edges found in an image by edge detecting operators -- these edges mark image locations of discontinuities in gray level, color, texture, etc.
• Image resulting from edge detection cannot be used as a segmentation result. ---> Supplementary processing steps must follow to combine edges into edge chains that correspond better with borders in the image.
• The final aim is to reach at least a partial segmentation -- that is, to group local edges into an image where only edge chains with a correspondence to existing objects or image parts are present.
• The more prior information that is available to the segmentation process, the better the segmentation results that can be obtained.
• Problems:
  • an edge presence in locations where there is no border
  • no edge presence where a real border exists

Edge Relaxation

Border Tracing

• If a region border is not known but regions have been defined in the image, borders can be uniquely detected.
• Whenever additional knowledge is available for boundary detection, it should be used - e.g., known approximate starting point and ending point of the border
• A more difficult situation is encountered if the borders are traced in grey level images where regions have not yet been defined.
  • The border is represented by a simple path of high-gradient pixels in the image.
  • Border tracing should be started in a pixel with a high probability of being a border element, and then border construction is based on the idea of adding the next elements which are in the most probable direction.
  • To find the following border elements, edge gradient magnitudes and directions are usually computed in pixels of probable border continuation.