원문정보
보안공학연구지원센터(IJAST)
International Journal of Advanced Science and Technology
Vol.44
2012.07
pp.113-124
피인용수 : 0건 (자료제공 : 네이버학술정보)
초록
영어
This paper reports the results of experiments in improving performance of leaf identification system using Principal Component Analysis (PCA). The system involved combination of features derived from shape, vein, color, and texture of leaf. PCA was incorporated to the identification system to convert the features into orthogonal features and then the results were inputted to the classifier that used Probabilistic Neural Network (PNN). This approach has been tested on two datasets, Foliage and Flavia, that contain various color leaves (foliage plants) and green leaves respectively. The results showed that PCA can increase the accuracy of the leaf identification system on both datasets.
목차
Abstract
1. Introduction
2. Basic of the Proposed System
2.1 Features of the System
2.2 PNN for Leaf Identification
2.3 Principal Component Analysis
3. Model of the Proposed System
4. Experimental Results
5. Conclusions
References
1. Introduction
2. Basic of the Proposed System
2.1 Features of the System
2.2 PNN for Leaf Identification
2.3 Principal Component Analysis
3. Model of the Proposed System
4. Experimental Results
5. Conclusions
References
저자정보
참고문헌
자료제공 : 네이버학술정보