Earticle

초록

영어

In this paper, we propose and implement a leaf recognition system using the leaf vein and shape that can be used for plant classification. The proposed approach uses major main vein and frequency domain data by using Fast Fourier Transform (hereinafter, FFT) methods with distance between contour and centroid on the detected leaf image. Total 21 leaf features were extracted for the leaf recognition, which they include ① the distance feature between centroid and all points on the leaf contour, ② frequency domain data by FFT that was performed using the distances. In summary, 10 features of all the 21 leaf features were extracted using distance, FFT magnitude, and phase, the other 10 features were extracted using t the digital morphological features using four basic geometric features and five vein features, and the last 1 feature was extracted using the convex hull. To verify the validity of the approach, images of 1907 leaves apply to classify 32 kinds of plants. In the experimental results, the proposed leaf recognition system showed an average recognition rate of 97.19%, and we can confirm that the recognition rate of the proposed leaf recognition system was better than that of the existed leaf recognition method.

목차

Abstract
 1. Introduction
 2. Leaf contour extraction
 3. Leaf direction decision using main vein
  3.1. Leaf vein extraction
  3.2. Main vein extraction and leaf direction decision
 4. Leaf feature extraction
  4.1. Leaf feature (1): Distance and FFT-based frequency domain analysis
  4.2. Leaf feature (2): Geometric and digital morphological features
  4.3. Leaf feature (3): Convex hull
 5. Leaf recognition system
 6. Experiments and results
 7. Conclusions
 Acknowledgements
 References

키워드

저자

• Kue-Bum Lee [ College of Information and Communication Engineering, Sungkyunkwan University ]
• Kwang-Seok Hong [ College of Information and Communication Engineering, Sungkyunkwan University ]

참고문헌

발행기관

간행물

표지보기 관심저널 등록

• 간행물명

  International Journal of Bio-Science and Bio-Technology

• 간기

  격월간

• pISSN

  2233-7849

• 수록기간

  2009~2016

• 등재여부

  SCOPUS

• 십진분류

  KDC 505 DDC 605