LEAF BOUNDARY EXTRACTION AND GEOMETRIC MODELING OF VEGETABLE SEEDLINGS

1. LEAF BOUNDARY EXTRACTION AND GEOMETRICMODELING 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. Leaf image acquisition Image binarization and blob analysis Determination of basic morphological features Determination of Fourier descriptors Boundary edge detection Searching leaf tip and base by discontinuity Bezier curve approximation No Error small enough? Petiole designation Bezier curve normalization Determination of Bezier features IMAGE PROCESSING ALGORITHM 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. Compactness L Roundness W Elongation A Roughness H P Basic quantity descriptors Dimensionless shape factors LEAF FEATURE EXTRACTION Conventional Morphological Features

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

9. 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 LEAF FEATURE EXTRACTION Fourier descriptors • Steps to extract Fourier descriptors

10. Original Image Binary Image N=256 N=128 N=64 N=32 N=16 N=8 N=4 N=2 LEAF FEATURE EXTRACTION Fourier descriptors 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. P1 P2 P0 P3 Bezier curve 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 LEAF FEATURE EXTRACTION Bezier descriptors

13. LEAF FEATURE EXTRACTION Bezier descriptors Steps to obtain Bezier descriptors C A B 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 • 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 Fourier Descriptors Leaf Image Plant Identification Bezier Features

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

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