Huihui Wang, Yan Lv, Yonghai Sun, Xuejun Wang, Xueheng Tao, Jixin Yang
언어
영어(ENG)
URL
https://www.earticle.net/Article/A214486
※ 원문제공기관과의 협약기간이 종료되어 열람이 제한될 수 있습니다.
원문정보
초록
영어
In this study, an identification model based on computer vision and artificial neural network technologies is proposed for the identification of the ripeness of fresh corn ears. For collected images of corn ears, 2D discrete wavelet transform is performed to extract information of low-frequency sub-band as color features, and discrete Fourier transform is performed to extract energy spectrum information as texture features. Principle component analysis is employed for the fusion and dimensionality reduction of color and texture features, and the first three principle components are chosen as inputs of the network model in order to establish probabilistic neural network model for the automated ripeness identification of fresh corn ears. Simulation analysis demonstrates that the identification accuracy of this model reaches 90.67%.
목차
Abstract 1. Introduction 2. Theory of Image-based Ripeness Identification for Fresh Corn Ears 3. Image-based Ripeness Identification Model for Fresh Corn Ears 3.1. Extraction of color features 3.2. Extraction of texture features 3.3. Optimization of feature parameters 3.4. Establishment of the ripeness identification model 4. Simulation Analysis 5. Conclusions Acknowledgements References
보안공학연구지원센터(IJSIP) [Science & Engineering Research Support Center, Republic of Korea(IJSIP)]
설립연도
2006
분야
공학>컴퓨터학
소개
1. 보안공학에 대한 각종 조사 및 연구
2. 보안공학에 대한 응용기술 연구 및 발표
3. 보안공학에 관한 각종 학술 발표회 및 전시회 개최
4. 보안공학 기술의 상호 협조 및 정보교환
5. 보안공학에 관한 표준화 사업 및 규격의 제정
6. 보안공학에 관한 산학연 협동의 증진
7. 국제적 학술 교류 및 기술 협력
8. 보안공학에 관한 논문지 발간
9. 기타 본 회 목적 달성에 필요한 사업
간행물
간행물명
International Journal of Signal Processing, Image Processing and Pattern Recognition
간기
격월간
pISSN
2005-4254
수록기간
2008~2016
십진분류
KDC 505DDC 605
이 권호 내 다른 논문 / International Journal of Signal Processing, Image Processing and Pattern Recognition Vol.6 No.6