Chapter 10

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# Chapter 10 - PowerPoint PPT Presentation

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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### Chapter 10

Image Segmentation

Preview
• 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)
Chapter Outline
• Detection of discontinuities
• Edge linking and boundary detection
• Thresholding
• Region-based segmentation
• Morphological watersheds
• Motion in segmentation
Detection of Discontinuities
• Define the response of the mask:
• Point detection:
Line Detection
• Masks that extract lines of different directions.
Edge Detection
• 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.
Edge Point
• 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.
• Magnitude:
• Direction:
The Laplacian
• Definition:
• Generally not used in its original form due to sensitivity to noise.
• Role of Laplacian in segmentation:
• Zero-crossings
• Tell whether a pixel is on the dark or light side of an edge.
Global Processing: Hough Transform
• Representation of lines in parametric space: Cartesian coordinate
Hough Transform
• Representation in parametric space: polar coordinate
Thresholding
• Foundation: background point vs. object point
• The role of illumination: f(x,y)=i(x,y)*r(x,y)
• Basic global thresholding