automatic seeded region growing for color image segmentation

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Automatic seeded region growing for color image segmentation

Authors: Frank Y. Shih, Shouxian ChengSource: Image and Vision Computing, vol. 23, pp.877-886, 2005.Speaker: Shu-Fen Chiou( 邱淑芬 )Date: 04/19/23

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Outline

Introduction Proposed method Experimental results Conclusion

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Introduction

Color image segmentation polices: Threshold Boundary-based Region-based Hybrid techniques

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Hybrid techniques

Seeding region growing (SRG) Different merging order possibility

SRG

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Proposed method

1. Transform the color image from RGB to YCbCr

2. Automatic seed selection algorithm3. Region growing4. Region merging

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Transform the color image from RGB to YCbCr

Why ? YCbCr color space is widely used in video compression st

andards. (e.g. MPEG and JPEG) The color difference of human perception can be directly

expressed by Euclidean distance in the color space. The intensity and chromatic components can be easily a

nd independently controlled. How ?

128

128

16

18.214-93.786-112

11274.203-39.797-

24.966128.55365.481

B

G

R

C

C

Y

r

b

The Cb (Cr, respectively)is the difference between the blue (red, respectively) component and a reference value

The Cb (Cr, respectively)is the difference between the blue (red, respectively) component and a reference value

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Automatic seed selection algorithm

Condition 1: A seed pixel candidate must have the

similarity higher than a threshold values.

Condition 2: A seed pixel candidate must have the

maximum relative Euclidean distance to its eight neighbors less than a threshold value.

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Automatic seed selection algorithm

The similarity of a pixel to its neighbors is defined as :

Considering neighborhood, the standard deviations :

High similarity Otsu’s method

NH 1

max

rb CCYN

33 2

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1xxx

Condition 1:A seed pixel candidate must have the similarity higher than a threshold values.

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Automatic seed selection algorithm

Calculate the maximum distance to its neighbors as :

of the pixel, of its neighbors Not on the boundary 0.05

iidd

8

1max max

8...1,

222

222

i

CCY

CCCCYYd

rb

rrbbi

iii

rbCYCii rbi CCY

Condition 2:A seed pixel candidate must have the maximum relative Euclidean distance to its eight neighbors less than a threshold value.

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Automatic seed selection algorithm

Connected seeds are considered as one seed.

the detected seeds are shown in red colorOriginal color image

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Region growing

The pixels that are unclassified and neighbors of at least one region, calculate the distance:

222

222

ii

ii

rbi

rrbbi

iCCY

CCCCYYd

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Region growing

1. red pixels are the seeds and the green pixels are the pixels in the sorted list T in a decreasing order of distances.

2. the white pixel is the pixel with the minimum minimum distance to the seed regions

3. check its 4-neighbors

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Region growing

1. If all labeled neighbors of p have a same label, set p to this label.2. If the labeled neighbors of p have different labels, calculate the

distances between p and all neighboring regions and classify p to the nearest region.

3. Then update the mean of this region, and add 4 neighbors of p, which are neither classified yet nor in T, to T in a decreasing order of distances.

4. Until the T is empty.

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Region merging

Consider the color different and size of regions : Color different between two adjacent region Ri a

nd Rj is defined as:

Size select of the total number of pixels in an image as

the threshold.150

1

222222

222

,min,

ijii

jiji

rbjrbi

rrbbji

CCYCCY

CCCCYYRjRid

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Region merging

We first examine the two regions having the smallest color different among others. If < threshold, merge the two

regions and re-compute the mean of the new region.

We repeat the process until no region has the distance less than the threshold.

Threshold=0.1

RjRid ,

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Region merging

If the size with number of pixels in a region is smaller than a threshold, the region is merged into its neighboring region with the smallest color difference. This procedure is repeated until no region

has size less than the threshold.

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Experimental results

JSEG algorithm.

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Experimental results

JSEG algorithm.

JSEG algorithm.

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Conclusion

We have presented an efficient segmentation algorithm for color image with automatic seed selection.

Experimental results show that we have a better results than JSPG.