140515 andrew kuo

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1) The document summarizes research on classifying rice grains using digital image analysis of morphological and color features. 2) It outlines procedures for image acquisition of rice seeds, quantifying features like Fourier descriptors, geometry, and color. 3) Future work is to acquire images of 360 rice varieties, analyze contour and color features, and cluster varieties based on morphological and color similarities.

Classiﬁcation of rice grain in
digital images using morphological
and color features
Presenter: Andrew Kuo
Advisor: Yan-Fu Kuo
Date: 2014.05.15
Department of Bio-industrial Mechatronics Engineering, National Taiwan University, Taiwan, R.O.C.
1

Outline
Introduction
• rice grain structure
• research objective
• procedure
Materials & Methods
• image acquisition
• feature quantiﬁcation
Future work
3

Rice grain structure
Rice seed
• rice grain with hull
Brown rice
• rice grain without hull
White rice
• rice without hull, bran and
germ
germ
white rice
bran
hull
4

Objective
To quantify the morphological and color
features of rice seed
To gather different kinds of rice seed that
have similar features in clusters based on
morphological and color features
5

Procedure
image acquisition
rice seed preparation
feature quantiﬁcation
clustering
analysis & illustrate
6

Image acquisition
-Equipment
Canon 450D
Adaptor
Microscope
8

Canon 450D
adaptor
microscope
Image acquisition
-Equipment
9

Canon 450D
- CMOS APS-C
- 4272 * 2848 pixel
- EF-s lens mount
-ISO 100 to 1600
adaptor
microscope
Image acquisition
-Equipment
10

Canon 450D
Adaptor
Microscope
Image acquisition
-Equipment
11

Canon 450D
Adaptor
Microscope
Image acquisition
-Equipment
12

Canon 450D
Adaptor
Microscope
- objective len 2X
Image acquisition
-Equipment
13

Canon 450D
Adaptor
Microscope
Image acquisition
-Equipment
14

Feature quantiﬁcation
Coefﬁcient of Fourier descriptor
Geometrical features
Color feature
16

Chain code
Feature quantiﬁcation
-coefﬁcient of Fourier descriptor
17
04
2
13
5
6
7

Chain code
Feature quantiﬁcation
-coefﬁcient of Fourier descriptor
18
0005676644422123

Assuming that the chain code is followed at
constant speed, the time needed to traverse a
particular link ai, is
Feature quantiﬁcation
-coefﬁcient of Fourier descriptor
19

The changes in the x, y projections of the
chain as the link ai are traversed
Feature quantiﬁcation
-coefﬁcient of Fourier descriptor
20

The projections on x and y of the ﬁrst p links
of the chain are
Feature quantiﬁcation
-coefﬁcient of Fourier descriptor
21

The Fourier series expansion for the x and y
projection of the chain code of the complete
contour are deﬁned as
Feature quantiﬁcation
-coefﬁcient of Fourier descriptor
22

Area
Perimeter
Major diameter
Aspect ratio
Feature quantiﬁcation
-geometrical features
23

Area
Perimeter
Major diameter
Aspect ratio
Feature quantiﬁcation
-geometrical features
24

Area
Perimeter
Major diameter
Aspect ratio
Feature quantiﬁcation
-geometrical features
25

Area
Perimeter
Major diameter
Aspect ratio
Feature quantiﬁcation
-geometrical features
26
major diameter
minordiameter

Area
Perimeter
Major diameter
Aspect ratio
Feature quantiﬁcation
-geometrical features
27
major diameter
minordiameter

Feature quantiﬁcation
-color feature
28
mapping
function

Future work
Acquire images of 360 kinds of rice grain
Describe the contour of the grain in Fourier
descriptor
Extract the color information from the raw ﬁles
Gather different kinds of rice seed in clusters
based on morphological and color features
29

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Randomised Optimisation Algorithms in DAPHNE

University of Maribor

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Randomised Optimisation Algorithms in DAPHNE

140515 andrew kuo

1. Classiﬁcation of rice grain in digital images using morphological and color features Presenter: Andrew Kuo Advisor: Yan-Fu Kuo Date: 2014.05.15 Department of Bio-industrial Mechatronics Engineering, National Taiwan University, Taiwan, R.O.C. 1

2. 2

3. Outline Introduction • rice grain structure • research objective • procedure Materials & Methods • image acquisition • feature quantiﬁcation Future work 3

4. Rice grain structure Rice seed • rice grain with hull Brown rice • rice grain without hull White rice • rice without hull, bran and germ germ white rice bran hull 4

5. Objective To quantify the morphological and color features of rice seed To gather different kinds of rice seed that have similar features in clusters based on morphological and color features 5

6. Procedure image acquisition rice seed preparation feature quantiﬁcation clustering analysis & illustrate 6

7. Image acquisition Equipment Fusion 7

8. Image acquisition -Equipment Canon 450D Adaptor Microscope 8

9. Canon 450D adaptor microscope Image acquisition -Equipment 9

10. Canon 450D - CMOS APS-C - 4272 * 2848 pixel - EF-s lens mount -ISO 100 to 1600 adaptor microscope Image acquisition -Equipment 10

11. Canon 450D Adaptor Microscope Image acquisition -Equipment 11

12. Canon 450D Adaptor Microscope Image acquisition -Equipment 12

13. Canon 450D Adaptor Microscope - objective len 2X Image acquisition -Equipment 13

14. Canon 450D Adaptor Microscope Image acquisition -Equipment 14

15. Image acquisition -Fusion 15

16. Feature quantiﬁcation Coefﬁcient of Fourier descriptor Geometrical features Color feature 16

17. Chain code Feature quantiﬁcation -coefﬁcient of Fourier descriptor 17 04 2 13 5 6 7

18. Chain code Feature quantiﬁcation -coefﬁcient of Fourier descriptor 18 0005676644422123

19. Assuming that the chain code is followed at constant speed, the time needed to traverse a particular link ai, is Feature quantiﬁcation -coefﬁcient of Fourier descriptor 19

20. The changes in the x, y projections of the chain as the link ai are traversed Feature quantiﬁcation -coefﬁcient of Fourier descriptor 20

21. The projections on x and y of the first p links of the chain are Feature quantification -coefficient of Fourier descriptor 21

22. The Fourier series expansion for the x and y projection of the chain code of the complete contour are defined as Feature quantification -coefficient of Fourier descriptor 22

23. Area Perimeter Major diameter Aspect ratio Feature quantiﬁcation -geometrical features 23

24. Area Perimeter Major diameter Aspect ratio Feature quantiﬁcation -geometrical features 24

25. Area Perimeter Major diameter Aspect ratio Feature quantiﬁcation -geometrical features 25

26. Area Perimeter Major diameter Aspect ratio Feature quantiﬁcation -geometrical features 26 major diameter minordiameter

27. Area Perimeter Major diameter Aspect ratio Feature quantiﬁcation -geometrical features 27 major diameter minordiameter

28. Feature quantiﬁcation -color feature 28 mapping function

29. Future work Acquire images of 360 kinds of rice grain Describe the contour of the grain in Fourier descriptor Extract the color information from the raw ﬁles Gather different kinds of rice seed in clusters based on morphological and color features 29

30. Thanks for your attention! 30

140515 andrew kuo

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