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

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

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.

IMAGE PROCESSING ALGORITHM No 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

LEAF FEATURE EXTRACTION Conventional morphological features Fourier descriptors Bezier features

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)

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

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

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

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

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

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

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

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

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

LEAF FEATURES AT DIFFERENT GROWTH STAGES

LEAF FEATURES AT DIFFERENT GROWTH STAGES

LEAF FEATURES AT DIFFERENT GROWTH STAGES

LEAF FEATURES AT DIFFERENT GROWTH STAGES

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

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

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

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

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

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

APPLICATIONS Leaf Shape Comparisons and Plant Identification

APPLICATIONS Leaf Shape Comparisons and Plant Identification

APPLICATIONS Leaf Shape Comparisons and Plant Identification

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.

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.

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