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
Steerable semi-automatic segmentation of textured images
SCIA'05 Proceedings of the 14th Scandinavian conference on Image Analysis
Knowledge based lacunas detection and segmentation for ancient paintings
VSMM'07 Proceedings of the 13th international conference on Virtual systems and multimedia
Automatic image segmentation by positioning a seed
ECCV'06 Proceedings of the 9th European conference on Computer Vision - Volume Part II
Systematic skin segmentation: merging spatial and non-spatial data
Multimedia Tools and Applications
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This paper presents a technique for texture segmentation in images. Providing a small template of a texture of interest results in the image being segmented into regions with similar properties and background (non-similar) regions. The core of the segmentation engine is based on the minimal cut/maximal flow algorithm in the graph representing an image. The main contribution lies in incorporating the template information (colour, texture) into the whole graph used for segmentation. The method brings the possibility to locate textured regions in the image having same property as the template patch and not only one-colored regions (as in much existing work). The method is supervised since the user provides a representative template of an object being searched for. The object may consist of several isolated parts. Experimental results are presented on some images from the Berkeley database.