Name
Abstract | ||
|---|---|---|
Leaf is one of the most important organs of plant and often used as one of the basic characters in plant classification. The developmental condition of leaf can provide us with lots of critical information, such as the plant's health condition, the prospection of crop yield and so on. Leaf image processing by computer has been widely used for the extraction and dissection of leaf images in relevant researches. Image processing of leaf also offers an effective platform for plant classification and growth observation. A basic problem of leaf image processing is detecting and dislodging the petiole from the whole leaf image. Here this paper presents an algorithm which combines the dual-channel pulse coupled neural network (PCNN) model and HSI color space for leaf petiole detection. Totally 169 sorts of leaf images are tested by the proposed algorithm. The experimental results show that our method has potential availability in reducing mis-evaluation and increasing application scale as a tool in relative study. |
Year | Venue | Field |
|---|---|---|
2015 | 2015 IEEE 28TH CANADIAN CONFERENCE ON ELECTRICAL AND COMPUTER ENGINEERING (CCECE) | Computer vision,Signal processing,Color space,Computer science,Image processing,Algorithm,Artificial intelligence,Artificial neural network,Petiole (botany) |
DocType | ISSN | Citations |
Conference | 0840-7789 | 0 |
PageRank | References | Authors |
0.34 | 5 | 7 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Zhaobin Wang | 1 | 170 | 10.17 |
| Xu Zheng | 2 | 0 | 0.34 |
| Xiaoguang Sun | 3 | 15 | 1.94 |
| Hao Wang | 4 | 0 | 0.34 |
| Ying Zhu | 5 | 8 | 2.15 |
| Jianpeng Liu | 6 | 0 | 0.34 |
| Yide Ma | 7 | 459 | 34.74 |