Name
Abstract | ||
|---|---|---|
In the agronomic domain, the simplification of crop counting is a very important and fastidious step for technical institutes such as Arvalis, which has then proposed us to use image processing to detect the number of wheat cars in images acquired directly in a field. Texture image segmentation techniques based on feature extraction by first and higher order statistical methods have been developped for unsupervised pixel classification. The K-Means algorithm is implemented before the choice of a threshold to highlight the ears. Three methods have been tested with very heterogeneous results, except the run length technique for which the results are closed to the visual counting with an average error of 6%. Although the evaluation of the quality of the detection is visually done, automatic evaluation algorithms are currently implementing. Moreover, other statistical methods of higher order must be implemented in the future jointly with methods based on spatio-frequential transforms and specific filtering. |
Year | Venue | Keywords |
|---|---|---|
2007 | VISAPP 2007: PROCEEDINGS OF THE SECOND INTERNATIONAL CONFERENCE ON COMPUTER VISION THEORY AND APPLICATIONS, VOLUME IU/MTSV | image processing, texture analysis, pattern recognition, agronomy |
Field | DocType | Citations |
Computer vision,Pattern recognition,Computer science,Artificial intelligence | Conference | 0 |
PageRank | References | Authors |
0.34 | 1 | 3 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| M. Bakhouche | 1 | 0 | 0.34 |
| Frederic Cointault | 2 | 16 | 4.79 |
| Pierre Gouton | 3 | 85 | 16.94 |