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Image Segmentation-Digital Image Processing-Lecture 18 Slides Slides-Electrical and Computer Engineering, Slides of Digital Image Processing

University of Alabama (UA)Digital Image Processing

Image Segmentation, Thresholding, Segmentation, Histogram, Global, Dynamic, Laplacian, Boundary, Thresholds, Gradient, Gray levels, Symmetric, Digital Image Processing, Lecture Slides, Dr D J Jackson, Department of Electrical and Computer Engineering, University of Alabama, United States of America.

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Dr. D. J. Jackson Lecture 18-1Electrical & Computer Engineering
Computer Vision &
Digital Image Processing
Image Segmentation: Thresholding
Dr. D. J. Jackson Lecture 18-2Electrical & Computer Engineering
Image segmentation: thresholding
•Suppose an image f(x,y) is composed of several light
objects on a dark background.
•The histogram for such an image may look like the
following: showing two dominate modes
•An obvious way to extract object information is to select a
threshold Tthat separates the two modes
T
pf3
pf4
pf5

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Download Image Segmentation-Digital Image Processing-Lecture 18 Slides Slides-Electrical and Computer Engineering and more Slides Digital Image Processing in PDF only on Docsity!

Electrical & Computer Engineering Dr. D. J. Jackson Lecture 18-

Computer Vision &

Digital Image Processing

Image Segmentation: Thresholding

Image segmentation: thresholding

  • Suppose an image f ( x,y ) is composed of several light objects on a dark background.
  • The histogram for such an image may look like the following: showing two dominate modes
  • An obvious way to extract object information is to select a threshold T that separates the two modes

T

Electrical & Computer Engineering Dr. D. J. Jackson Lecture 18-

Thresholding (continued)

  • Suppose several objects with differing gray levels (with a dark background) comprise the image
  • An object may be classified as belonging to one object class if T 1 < f ( x,y ) ≤ T 2 , to a second class if f ( x,y )> T 2 or to the background if f ( x,y ) ≤ T 1
  • This, however, is generally less reliable than single level thresholding

T 1 T 2

Thresholding (continued)

  • Thresholding may be viewed as an operation that tests against a given function of the form
  • where f ( x,y ) is the gray level of point ( x,y ) and p ( x,y ) is some local property of the point -- the average gray level of a neighborhood around ( x,y )
  • The thresholded image is given by
  • Pixels labeled 1 (or any other convenient gray level value) correspond to objects

T = T [ x , y , p ( x , y ), f ( x , y )]

⎩

⎨

⎧ ≤

>

0 iff(x,y) T

1 iff(x,y) T g ( x , y )

Electrical & Computer Engineering Dr. D. J. Jackson Lecture 18-

Boundary characteristic thresholds

(continued)

• An obvious advantage is that the histogram

becomes less dependent on the size of objects in

the image

• By choosing pixels on or near object boundaries (

assuming an equal probability of choosing a pixel

on the object or boundary) the histogram peaks

tend to be made more symmetric

• Using pixels that satisfy some simple measures

based on the gradient and Laplacian operators

tends to deepen the valleys between histogram

peaks

Boundary characteristic thresholds

(continued)

• Determining if a pixel lies on a boundary: compute

the gradient

• Determining what side, background (dark) or object

(light), a pixel lies on: compute the Laplacian

• Using the gradient and Laplacian, a three-level

image may be formed according to

• where 0, + and - are three distinct gray levels

⎪⎩

⎪ ⎨

⎧

− ∇ ≥ ∇ <

  • ∇ ≥ ∇ ≥

∇ <

if and 0

if and 0

0 if (, ) 2

2 f T f

f T f

f T sxy

Boundary characteristic thresholds

(continued)

• For a dark object on a light background, s(x,y) is

produced where

  • all pixels not on an edge are labeled 0
  • all pixels on the dark side of an edge are labeled +
  • all pixels on the light side of an edge are labeled -

• For a light object on a dark background, s(x,y) is

produced where

  • all pixels not on an edge are labeled 0
  • all pixels on the dark side of an edge are labeled -
  • all pixels on the light side of an edge are labeled +