Contour Grouping Based on Contour-Skeleton Duality
International Journal of Computer Vision
Interactive Image Segmentation Based on Hierarchical Graph-Cut Optimization with Generic Shape Prior
ICIAR '09 Proceedings of the 6th International Conference on Image Analysis and Recognition
Constrained spectral clustering via exhaustive and efficient constraint propagation
ECCV'10 Proceedings of the 11th European conference on Computer vision: Part VI
Enhancing interactive image segmentation with automatic label set augmentation
ECCV'10 Proceedings of the 11th European conference on Computer vision: Part VI
Nugget-cut: a segmentation scheme for spherically- and elliptically-shaped 3D objects
Proceedings of the 32nd DAGM conference on Pattern recognition
Automated segmentation of 3D CT images based on statistical atlas and graph cuts
MCV'10 Proceedings of the 2010 international MICCAI conference on Medical computer vision: recognition techniques and applications in medical imaging
Image segmentation by iterated region merging with localized graph cuts
Pattern Recognition
Interactive segmentation with super-labels
EMMCVPR'11 Proceedings of the 8th international conference on Energy minimization methods in computer vision and pattern recognition
Image segmentation with a shape prior based on simplified skeleton
EMMCVPR'11 Proceedings of the 8th international conference on Energy minimization methods in computer vision and pattern recognition
Orientation histograms as shape priors for left ventricle segmentation using graph cuts
MICCAI'11 Proceedings of the 14th international conference on Medical image computing and computer-assisted intervention - Volume Part III
Segmentation based features for lymph node detection from 3-D chest CT
MLMI'11 Proceedings of the Second international conference on Machine learning in medical imaging
Iterated graph cuts for image segmentation
ACCV'09 Proceedings of the 9th Asian conference on Computer Vision - Volume Part II
A survey of graph theoretical approaches to image segmentation
Pattern Recognition
Hausdorff distance constraint for multi-surface segmentation
ECCV'12 Proceedings of the 12th European conference on Computer Vision - Volume Part I
Adaptive shape prior in graph cut image segmentation
Pattern Recognition
MCV'12 Proceedings of the Second international conference on Medical Computer Vision: recognition techniques and applications in medical imaging
Graph cut segmentation with a statistical shape model in cardiac MRI
Computer Vision and Image Understanding
Kidney segmentation using graph cuts and pixel connectivity
Pattern Recognition Letters
Computer Vision and Image Understanding
On maximum weight objects decomposable into based rectilinear convex objects
WADS'13 Proceedings of the 13th international conference on Algorithms and Data Structures
Interactive segmentation with direct connectivity priors
Proceedings of the 12th ACM SIGGRAPH International Conference on Virtual-Reality Continuum and Its Applications in Industry
TouchCut: Fast image and video segmentation using single-touch interaction
Computer Vision and Image Understanding
Iterative Graph Cuts for Image Segmentation with a Nonlinear Statistical Shape Prior
Journal of Mathematical Imaging and Vision
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In recent years, segmentation with graph cuts is increasingly used for a variety of applications, such as photo/video editing, medical image processing, etc. One of the most common applications of graph cut segmentation is extracting an object of interest from its background. If there is any knowledge about the object shape (i.e. a shape prior), incorporating this knowledge helps to achieve a more robust segmentation. In this paper, we show how to implement a star shape prior into graph cut segmentation. This is a generic shape prior, i.e. it is not specific to any particular object, but rather applies to a wide class of objects, in particular to convex objects. Our major assumption is that the center of the star shape is known, for example, it can be provided by the user. The star shape prior has an additional important benefit - it allows an inclusion of a term in the objective function which encourages a longer object boundary. This helps to alleviate the bias of a graph cut towards shorter segmentation boundaries. In fact, we show that in many cases, with this new term we can achieve an accurate object segmentation with only a single pixel, the center of the object, provided by the user, which is rarely possible with standard graph cut interactive segmentation.