Normalized Cuts and Image Segmentation
IEEE Transactions on Pattern Analysis and Machine Intelligence
Contour and Texture Analysis for Image Segmentation
International Journal of Computer Vision
Geodesic Active Regions and Level Set Methods for Supervised Texture Segmentation
International Journal of Computer Vision
Bayesian Estimation of Layers from Multiple Images
ECCV '02 Proceedings of the 7th European Conference on Computer Vision-Part III
Coupled Geodesic Active Regions for Image Segmentation: A Level Set Approach
ECCV '00 Proceedings of the 6th European Conference on Computer Vision-Part II
What Energy Functions Can Be Minimizedvia Graph Cuts?
IEEE Transactions on Pattern Analysis and Machine Intelligence
Learning to Detect Natural Image Boundaries Using Local Brightness, Color, and Texture Cues
IEEE Transactions on Pattern Analysis and Machine Intelligence
An Experimental Comparison of Min-Cut/Max-Flow Algorithms for Energy Minimization in Vision
IEEE Transactions on Pattern Analysis and Machine Intelligence
Spatially coherent clustering using graph cuts
CVPR'04 Proceedings of the 2004 IEEE computer society conference on Computer vision and pattern recognition
Knowledge from markers in watershed segmentation
CAIP'07 Proceedings of the 12th international conference on Computer analysis of images and patterns
Universal seed skin segmentation
ISVC'10 Proceedings of the 6th international conference on Advances in visual computing - Volume Part II
Color pair clustering for texture detection
ISVC'06 Proceedings of the Second international conference on Advances in Visual Computing - Volume Part II
Supervised texture detection in images
CAIP'05 Proceedings of the 11th international conference on Computer Analysis of Images and Patterns
Systematic skin segmentation: merging spatial and non-spatial data
Multimedia Tools and Applications
| Hi-index | 0.00 |
This paper generalizes the interactive method for region segmentation of grayscale images based on graph cuts by Boykov & Jolly (ICCV 2001) to colour and textured images. The main contribution lies in incorporating new functions handling colour and texture information into the graph representing an image, since the previous method works for grayscale images only. The suggested method is semi-automatic since the user provides additional constraints, i.e. s(he) establishes some seeds for foreground and background pixels. The method is steerable by a user since the change in the segmentation due to adding or removing seeds requires little computational effort and hence the evolution of the segmentation can easily be controlled by the user. The foreground and background regions may consist of several isolated parts. The results are presented on some images from the Berkeley database.