ip12 segmentation

7/31/2019 Ip12 Segmentation

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mage Processing - Lesson 12

Segmentation

Threshold Segmentation

Local thresholding

Edge thresholding

Threshold using averaging

Gradient Detectors

Region Growing

Split & Merge

Shape Matching

Shape Representation


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Segmentation

Image Segmentation = divide image into (continuous)regions or sets of pixels.

1) Region Based

2) Boundary Based

3) Edge Based


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Thresholding

Global Thresholding = Choose threshold T that

separates object from background.

Background

Object

T

Graylevel

#

ofPixels

Image Histogram


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0 100 2000

500

1000

1500

Segmentation using Thresholding

Original Histogram

50 75

Threshold = 50 Threshold = 75


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0 100 2000

500

1000

1500

Original Histogram

21

Threshold = 21


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Thresholding a Grayscale Image

Original Image Thresholded Image

Threshold too low Threshold too high


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FMRI - Example

Original Image Threshold = 80

Threshold = 71 Threshold = 88


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Simple thresholding is not always possible:

Graylevel

#

ofPixels

Image Histogram

1) Many objects at different gray levels.

2) Variations in background gray level.

3) Noise in image.


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Thresholding Example

Original

0 100 2000

50

100

150

Histogram

Single Global Threshold

0 100 2000

50

100

150

T = 128


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Local Thresholding - 4 Thresholds

Divide image in to regions. Perform thresholding

independently in each region.

0 100 2000

50

00

0 100 2000

50

00

0 100 2000

50

100

0 100 2000

50

100

T = 80 T = 128 T = 188 T = 226


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Adaptive Thresholding

Every pixel in image is thresholded according to the

histogram of the pixel neighborhood.

0 50 1000

00

00

x-coordinate

Threshold

Level

T =


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Adaptive Thresholding - Example

Original

Global Threshold Adaptive Threshold


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Threshold Segmentation of Noisy Images

Noise inhibits localization of threshold.

Graylevel

#

ofPixels

Image Histogram

Smooth image and obtain a histogram for which

threshold is easily determined.

Graylevel

#ofPixels

Image Histogram

T


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Note: Smooth the image, not the histogram...

Graylevel

#

ofPixels

Image Histogram

Graylevel

#

ofPixels

Image Histogram


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Threshold using Average

Gray level

Gray level Histograms

nop

ixe

ls

0 50 100 150 200 2500

5000

10000


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Threshold using Average

Gray level

nopi

xels

0 50 100 150 200 2500

50

100

0 50 100 150 200 2500

200

400

Gray level

Gray level Histograms


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Edge Based Segmentation

Edge ImageOriginal

ObjectBackground


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Edge Based Thresholding

Original Edge Pixels Edge Neighbors

0 50 100 150 200 2500

50

100

150

200Edge Neighbors Histogram


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Thresholding Based on

Boundary Characteristics

Original Threshold (T=182)l

0 100 2000

1000

2000

3000

Global Histogram


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Thresholding Based on

Boundary Characteristics

Original Threshold (T=143)l

0 100 2000

5

10

15

20

Edge Neighborhood Histogram


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Region Growing

Define:

S = the set of pixels inside the region.

Q = queue of pixels to be checked.

(x0,y0) = a pixel inside the region.

Initialize: S =

Q = { (x0,y0) }

1) Extract pixel P from queue Q

2) Add P to S.

3) For each neighbor P' ofP:

ifP' is "similar" to P and P' S then

add P' to Q.

4) IfQ = then end, else return to 1.

Algorithm:

S = the extracted pixels of the region.

Define what "similar" means.

Problematic in small gradient regions.


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Region Growing - Example

Seed


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Region Growing - Examples

Color Segmentation

Texture Segmentation

Color + Texture Segmentation


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Watershed Threshold Algorithm

An Image can be viewed as a topographic map

T0

Tn

Threshold

Level

Image location


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Original T = 149 T = 150

Thre

shold

Level

T0

Tn

Image location

Initialize threshold at T0 that separates objects well.Determine connected components.

Raise threshold, and detect pixels that pass threshold

and belong to more than one connected component.

Do not let objects merge.

Set these pixels as object boundaries.


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Original Watershed Boundaries

Watershed Markers Watershed Boundaries

Watershed Markers may be chosen manually or

local global maximas (as above)


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Split & Merge Segmentation

2 Stage Algorithm:

Stage 1: Split

Split image into regions using a Quad Tree

representation.

Stage 2: Merge

Merge "leaves" of the Quad Tree which are

neighboring and "similar".

Original Split Split + Merge


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Quad Tree - Representation

Image Quad Tree

1 2 3 4

1 2

3 4

Demo
http://lectures/Ip4_GeomOper/Demos/AlexMorph/morphToky.htmlhttp://lectures/Ip4_GeomOper/Demos/AlexMorph/morphToky.htmlhttp://lectures/Ip4_GeomOper/Demos/AlexMorph/morphToky.html


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Quad Tree Representation

Original

Thresh = 0.20

Thresh = 0.40

Thresh = 0.55


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Split & Merge Example

Stage 1: Split

Stage 2: Merge


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Split & Merge Example


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Graph-Cut Segmentation

Wij

ji Wij

G = { V , E }

V = verticesE = Edges

V = image pixelsE = pixel similarity

Segmentation = Graph Partitioning


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Min-Cut Segmentation

cut

A

B

=BjAi

ijWBAcut,

),(

Segmentation by min-cut:

Find A,B such that cut(A,B) is minimal.

(Wu and Leahy 1993)


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Normalized-Cut Segmentation

+=

)(

1

)(

1),(

, BvolAvolWBANcutBjAi

ij

Segmentation by normalized-cut:

Find A,B such that Ncut(A,B) is minimal.

(Shi and Malik 2000)

cut

Min-cut segmentation favors small segments.

=AjAi

ijWAvol,

)(where


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Normalized-Cut Segmentation -

Examples

(from Cohen-Or 2005)


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Shape Matching / Object Recognition

Model #1 Model #2 Model #3 Model #4

? ?? ?

Which Model matches the Measurement?

Which Model

What is the transformation from Model to Measurement

(translation, rotation, scale,)


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ip12 segmentation

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