Name
Abstract | ||
|---|---|---|
Image segmentation is an important stage for object recognition. Many methods have been proposed in the last few years for grayscale and color images. In this paper, we present a deep review of the state of the art on color image segmentation methods; through this paper, we explain the techniques based on edge detection, thresholding, histogram-thresholding, region, feature clustering and neural networks. Because color spaces play a key role in the methods reviewed, we also explain in detail the most commonly color spaces to represent and process colors. In addition, we present some important applications that use the methods of image segmentation reviewed. Finally, a set of metrics frequently used to evaluate quantitatively the segmented images is shown. |
Year | DOI | Venue |
|---|---|---|
2018 | 10.1016/j.neucom.2018.01.091 | Neurocomputing |
Keywords | Field | DocType |
Color spaces,Image segmentation,Quantitative evaluation | Color space,Pattern recognition,Edge detection,Segmentation,Image segmentation,Artificial intelligence,Thresholding,Cluster analysis,Grayscale,Mathematics,Cognitive neuroscience of visual object recognition | Journal |
Volume | ISSN | Citations |
292 | 0925-2312 | 9 |
PageRank | References | Authors |
0.54 | 117 | 4 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Farid GarcíA Lamont | 1 | 69 | 9.58 |
| Jair Cervantes | 2 | 176 | 18.08 |
| Asdrúbal López Chau | 3 | 87 | 11.62 |
| Lisbeth Rodríguez-Mazahua | 4 | 24 | 6.43 |