# Chapter 10 - PowerPoint PPT Presentation

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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.

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Chapter 10

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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.

• Definition:

### Global Processing: Hough Transform

• Representation of lines in parametric space: Cartesian coordinate

### Hough Transform

• Representation in parametric space: polar coordinate

### Graphic-Theoretic Techniques

• Minimal-cost path

### Thresholding

• 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

### Optimal Global and Adaptive Thresholding

• Refer to Chapter 2 of the “Pattern Classification” textbook by Duda, Hart and Stork.

### Region-Based Segmentation

• 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:

• (a)

• (b) Ri is a connected region

• (c)

• (d) P(Ri)= TRUE for i=1,2,…n

• (e) P(Ri U Rj)= FALSE for i != j