Chapter 10. Image Segmentation. Preview. Segmentation subdivides an image into its constituent regions or objects. Level of division depends on the problem being solved.
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Segmentation subdivides an image into its constituent regions or objects.
Level of division depends on the problem being solved.
Image segmentation algorithms generally are based on one of two basic properties of intensity values: discontinuity (e.g. edges) and similarity (e.g., thresholding, region growing, region splitting and merging)
Detection of discontinuities
Edge linking and boundary detection
Motion in segmentation
Detection of Discontinuities
Define the response of the mask:
Point Detection Example
Masks that extract lines of different directions.
An ideal edge has the properties of the model shown to the right:
A set of connected pixels, each of which is located at an orthogonal step transition ingray level.
Edge: local concept
Region Boundary: global idea
Ramp Digital Edge
In practice, optics, sampling and other image acquisition imperfections yield edges that area blurred.
Slope of the ramp determined by the degree of blurring.
Zero-Crossings of 2nd Derivative
Noisy Edges: Illustration
We define a point in an image as being an edge point if its 2-D 1st order derivative is greater than a specified threshold.
A set of such points that are connected according to a predefined criterion of connectedness is by definition an edge.
Diagonal Edge Masks
Generally not used in its original form due to sensitivity to noise.
Role of Laplacian in segmentation:
Tell whether a pixel is on the dark or light side of an edge.
Laplacian of Gaussian
Edge Linking: Local Processing
Link edges points with similar gradient magnitude and direction.
Global Processing: Hough Transform
Representation of lines in parametric space: Cartesian coordinate
Representation in parametric space: polar coordinate
Foundation: background point vs. object point
The role of illumination: f(x,y)=i(x,y)*r(x,y)
Basic global thresholding
Optimal global and adaptive thresholding
Use of boundary characteristics for histogram improvement and local thresholding
Thresholds based on several variables
The Role of Illumination
Basic Global Thresholding
Basic Adaptive Thresholding
Basic Adaptive Thresholding (cont’d)
Optimal Global and Adaptive Thresholding
Refer to Chapter 2 of the “Pattern Classification” textbook by Duda, Hart and Stork.
Thresholds Based on Several Variables
Let R represent the entire image region. We may view segmentation as a process that partitions R into n sub-regions R1, R2, …, Rn such that: