Name
Abstract | ||
|---|---|---|
Automatic segmentation and classification of color images is a problem of great practical interest in different areas. This paper presents an algorithm for this purpose which is divided in three steps. Firstly, the regions of interest are isolated from the rest of the image based on threshold functions defined in the YUV and YIQ color spaces, producing a set of connected components. Then, a set of features is computed to enable a quantitative evaluation of the segmented objects. Finally, the image is classified by means of a decision rule based on the analysis of the differences between the computed measures and a set of ideally segmented images, according to experts' assessment. The algorithm was applied to a decision support tool for estrus detection in cattle. This approach constitutes a valuable alternative to improve this process, as it may replace the visual observation by the automatic analysis of pictures taken to cows in controlled environments. Experimental results show that the segmentations obtained with this method are highly satisfactory and they allow a precise classification of the images with low computational complexity. |
Year | DOI | Venue |
|---|---|---|
2006 | 10.1007/BF03181760 | J. Visualization |
Keywords | DocType | Volume |
Image segmentation,Thresholding,Feature extraction,Classification | Journal | 9 |
Issue | ISSN | Citations |
2 | 1343-8875 | 2 |
PageRank | References | Authors |
0.73 | 5 | 5 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| M. del Fresno | 1 | 23 | 3.03 |
| A. Macchi | 2 | 2 | 0.73 |
| Z. Marti | 3 | 2 | 0.73 |
| A. Dick | 4 | 2 | 0.73 |
| A. Clausse | 5 | 42 | 5.63 |