Colour Segmentation
  • Introduction
  • WHAT IS BEHIND?
    • Fuzzy Logic-based methods
      • Amante-Fonseca
      • Chamorro-Martínez
      • Liu-Wang
      • Shamir
  • USAGE
    • Segmentator object
    • Segmentation using Fuzzy Logic-based methods
  • Examples
    • Examples of Fuzzy Logic-based methods
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  • Proposal of fuzzy sets
  • Partition of the chromatic space
  • Classification of achromatic colours
  • References

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  1. WHAT IS BEHIND?
  2. Fuzzy Logic-based methods

Amante-Fonseca

This page contains a brief explanation of the fuzzy sets proposed in the study of Amante et. al.

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Last updated 3 years ago

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Proposal of fuzzy sets

Nine categories are defined: red, brown, orange, yellow, green, cyan, blue, purple and pink. By default, each of the classes is represented by the following colour:

Colour label

Index

RGB value

HEX value

Red

0

[255, 33, 36]

#FF2125

Brown

1

[170, 121, 66]

#AA7942

Orange

2

[255, 146, 0]

#FF9200

Yellow

3

[255, 251, 0]

#FFFB00

Green

4

[0, 255, 0]

#00FF00

Cyan

5

[0, 253, 255]

#00FFFF

Blue

6

[0, 0, 255]

#0000FF

Purple

7

[147, 33, 146]

#932191

Pink

9

[255, 64, 255]

#FF40FF

The colour associated with each label can be changed when calling the function that performs the segmentation. By default, they are the colours shown in the table above.

The indices of the colours must be positive integers. Negative integers are reserved for the internal calculation of the methods.

Partition of the chromatic space

The H channel, whose values must be in range [0,360][0, 360][0,360], is partitioned using trapezoidal membership functions. For the sake of clarity, the parameters aaa, bbb, mmm, nnn which uniquely determine the expression of the membership function are given. For more information, see section .

Colour label

Red

335

20

350

10

Brown

10

35

20

30

Orange

30

50

34

42

Yellow

44

100

50

70

Green

70

160

100

140

Cyan

140

220

160

200

Blue

200

290

220

260

Purple

260

320

290

310

Pink

310

350

315

335

In the definition of the red colour, it can be seen that it is not verified that a<m<n<ba<m<n<ba<m<n<b. This is because the chromatic component H of the HSV colour space is circular, and the red colour is the one located on the boundary.

Classification of achromatic colours

The study of Amante et al. proposes the classification of achromatic colours. This is because, depending on the values of the V and S channels, the colour can be more or less saturated. In this case, the achromatic colours are black, grey and white. For this classification, the S and V channels must be in range [0,1][0, 1][0,1]. If p=(h,s,v)p=(h,s,v)p=(h,s,v)is an HSV-encoded pixel, the criterion is as follows:

  • If v≤V1v \leq V_1v≤V1​, pixel is classified as black. Black colour is represented by index -1.

  • If v>V2v>V_2v>V2​ and s≤S1s\leq S_1s≤S1​, pixel is classified as white. White colour is represented by index -2.

  • If V1<v≤V2V_1 < v \leq V_2V1​<v≤V2​ and s≤S1s\leq S_1s≤S1​, pixel is classified as gray. Gray colour is represented by index -3.

The thresholds that appear have been experimentally fixed by Amante et al., and they are V1=0.19V_1=0.19V1​=0.19, V2=0.81V_2=0.81V2​=0.81 and S1=0.14S_1=0.14S1​=0.14.

References

Amante, José; Fonseca, Manuel J.. (2012). Fuzzy Color Space Segmentation to Identify the Same Dominant Colors as Users. Proceedings: DMS 2012 - 18th International Conference on Distributed Multimedia Systems. 48-53.

aaa
bbb
mmm
nnn
Fuzzy Logic-based methods