1. LEAF BOUNDARY EXTRACTION AND GEOMETRIC MODELING OF VEGETABLE SEEDLINGS
Ta-Te Lin, Yud-Tse Chi, Wen-Chi Liao
Department of Bio-Industrial Mechatronics Engineering,
National Taiwan University,
Taipei, Taiwan, ROC

2. INTRODUCTION Plant growth measurement and modeling
Image processing technique
Seedling characteristics
Applications

3. OBJECTIVES
To develop image processing algorithms for leaf boundary extraction.
To model leaf boundary with Bezier curves and develop leaf features based on Bezier curve.
To determined leaf features of selected vegetable seedlings based on basic morphological descriptors, Fourier descriptors, and Bezier curve descriptors.
To examine the variation of leaf features at different growth stages.
To graphically simulate the growth of seedling leaves.

4. IMAGE PROCESSING ALGORITHMNo
Leaf image acquisition
Image binarization and blob analysis
Searching leaf tip and base by discontinuity
Boundary edge detection
Determination of basic
morphological features
Bezier curve approximation
Petiole designation
Error small enough?
Determination of Bezier features
Determination of Fourier descriptors
Bezier curve normalization
Yes

5. LEAF FEATURE EXTRACTION
Conventional morphological features
Fourier descriptors
Bezier features

6. LEAF FEATURE EXTRACTION
Conventional Morphological Features
Basic quantity descriptors
Area (A)
Perimeter (P)
Maximum length (L)
Maximum width (W)
Convex hull (H)
Dimensionless shape factors
Compactness (C)
Roundness (R)
Elongation (E)
Roughness (G)

7. LEAF FEATURE EXTRACTION
Conventional Morphological Features
Compactness
Roundness
Elongation
Roughness
Dimensionless shape factors
Basic quantity descriptors L W A P H

8. LEAF FEATURE EXTRACTION
Fourier descriptors
x(k) and y(k) are x-y coordinates of leaf boundary pixels

9. LEAF FEATURE EXTRACTION
Fourier descriptors
Steps to extract Fourier descriptors
Find the major axis of seedling leaf
with Hotelling transform
Rotate seedling leaf to horizontal position
and select 256 points on the leaf boundary
Convert x-y coordinates of boundary points
to complex number
Use FFT algorithm to obtain
Fourier transform coefficient
Normalization of Fourier transform
coefficients to obtain Fourier descriptors

10. LEAF FEATURE EXTRACTION
Fourier descriptors
Original Image
Binary Image
N=256
N=128
N=64
N=32
N=16
N=8
N=4
N=2
Cabbage

11. LEAF FEATURE EXTRACTION
Fourier descriptors
Lettuce
Original Image
Binary Image
N=256
N=128
N=64
N=32
N=16
N=8
N=4
N=2

12. LEAF FEATURE EXTRACTION
Bezier descriptors
where m = n – 1, xk+1, yk+1 are the coordinates of the n control points, and Bk,m(u) are the Bezier blending coefficients P1 P0 P2 P3
Bezier curve

13. LEAF FEATURE EXTRACTION
Bezier descriptors
Steps to obtain Bezier descriptors A B C
Image acquisition
Image segmentation
Boundary detection D E F
Finding leaf tip and
leaf base
Fitting boundary with
Bezier curves
Normalization and
obtain bezier descriptors

14. LEAF FEATURE EXTRACTION
Bezier descriptors
Bezier descriptors
Leaf tip angle
Leaf base angle
Left control line ratio
Right control line ratio
Normalized control point coordinates

15. RESULTS Leaf features at different growth stages Applications
Basic morphologic features
Bezier descriptors
Applications
Geometric Modeling of Seedling Leaves
Leaf Shape Comparisons and Plant Identification

16. LEAF FEATURES AT DIFFERENT GROWTH STAGES

17. LEAF FEATURES AT DIFFERENT GROWTH STAGES

18. LEAF FEATURES AT DIFFERENT GROWTH STAGES

19. LEAF FEATURES AT DIFFERENT GROWTH STAGES

20. APPLICATIONS Geometric Modeling of Seedling Leaves Elliptical Model
Wire Frame Model
Perspective View
Mapping with Texture

21. APPLICATIONS Geometric Modeling of Seedling Leaves Bezier Curve Model
Wire Frame Model
Perspective View
Mapping with Texture

22. APPLICATIONS 3D Reconstruction of Seedling Structure Real Image
Graphics Simulation
Side View
Top View
Graphic Simulation of Cabbage Seedling

23. APPLICATIONS 3D Reconstruction of Seedling Structure Real Image
Graphics Simulation
Side View
Top View
Graphic Simulation of Chinese Mustard Seedling

24. APPLICATIONS Leaf Shape Comparisons and Plant Identification Leaf
Feature
Extraction
Morphological
Features
Pattern
Recognition
Statistical Analysis
Neural Network
Cluster Analysis
Genetic Algorithm
Applications
Leaf Image
Fourier
Descriptors
Plant
Identification
Bezier
Features

25. APPLICATIONS Leaf Shape Comparisons and Plant Identification
Chinese Mustard
Chinese Heading Cabbage
Cabbage
Lettuce

26. APPLICATIONS
Leaf Shape Comparisons and Plant Identification

27. APPLICATIONS
Leaf Shape Comparisons and Plant Identification

28. APPLICATIONS
Leaf Shape Comparisons and Plant Identification

29. CONCLUSIONS
An image processing algorithm was developed to quantitatively describe vegetable seedling leaf shape.
The leaf shape descriptors can be classified into basic morphological descriptors, Bezier curve descriptors, and Fourier descriptors.
The Bezier curve can be successfully used to fit the leaf boundary of selected vegetable seedlings. Features deduced from Bezier curves, such as leaf tip angle, leaf base angle, normalized control points, and control line ratios, can be used to characterize leaf shape.

30. CONCLUSIONS
The use of Fourier descriptors to model leaf shape was demonstrated.
The effect of leaf development on the variation of leaf features was investigated. Leaf features invariant to the leaf size were identified.
The measured features of seedling leaves allowed for 3D reconstruction of the vegetable seedling for graphic display and leaf shape comparison.

31. THANK YOU
謝 謝