Name
Title | ||
|---|---|---|
Eliminating Crop Shadows In Video Sequences By Probable Learning Pixel Classification |
Abstract | ||
|---|---|---|
Shadows have been one of the most serious problems for vegetation segmetation, espescially under conditions of natural random airflow and human or vehicle disturbance. A video sequence processing method has developed in this paper to identify and eliminate crop shadows. The method comprises pixel models and algorithms explained in a probable learning framework. Expectation maximization (EM) for mixture models is established and an incremental EM method is proposed. This method performs a probable reasoning unsupervised classification of pixels for real-time implementation. The results show that the method is quite robust and can successfully remove shadows under natural lighting conditions. |
Year | DOI | Venue |
|---|---|---|
2007 | 10.1007/978-0-387-77253-0_34 | COMPUTER AND COMPUTING TECHNOLOGIES IN AGRICULTURE, VOL 2 |
Keywords | Field | DocType |
probable learning, shadows, vegetation segmentation, video processing | Computer vision,Video processing,Pattern recognition,Computer science,Expectation–maximization algorithm,Pixel classification,Sequence processing,Airflow,Artificial intelligence,Pixel,Mixture model | Conference |
Volume | Issue | ISSN |
259 | null | 1571-5736 |
Citations | PageRank | References |
0 | 0.34 | 1 |
Authors | ||
2 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Tanghai Liu | 1 | 0 | 0.34 |
| Xiaoping Cheng | 2 | 2 | 0.74 |