Segmentation
- one of the most important steps leading to the analysis of processed image
data
- Goal: divide an image into parts that have a strong correlation
with objects or areas of the real world contained in the image
- complete segmentation - set of disjoint regions uniquely
corresponding with objects in the input image
- cooperation with higher processing levels which use specific knowledge
of the problem domain is necessary
- partial segmentation - regions do not correspond directly with image
objects
- image is divided into separate regions that are homogeneous with respect
to a chosen property such as brightness, color, reflectivity, texture, etc.
- in a complex scene, a set of possibly overlapping homogeneous regions
may result. The partially segmented image must then be subjected to further
processing, and the final image segmentation may be found with the help of
higher level information.
- Problem: Totally correct and complete segmentation of complex
scenes usually cannot be achieved in this processing phase
- Resolution: A reasonable aim is to use partial segmentation as an
input to higher level processing.
Simple segmentation problems
- contrasted objects on a uniform background
- simple assembly tasks, blood cells, printed characters, etc.
Segmentation Difficulties
- image data ambiguity
- information noise
Segmentation methods
Characteristics used in segmentation:
- brightness
- texture
- velocity field
- etc.
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