Graph Cuts and Efficient N-D Image Segmentation
International Journal of Computer Vision
Dynamic Graph Cuts for Efficient Inference in Markov Random Fields
IEEE Transactions on Pattern Analysis and Machine Intelligence
Topology cuts: A novel min-cut/max-flow algorithm for topology preserving segmentation in N-D images
Computer Vision and Image Understanding
Computer Vision and Image Understanding
Track and cut: simultaneous tracking and segmentation of multiple objects with graph cuts
Journal on Image and Video Processing - Video Tracking in Complex Scenes for Surveillance Applications
GeoS: Geodesic Image Segmentation
ECCV '08 Proceedings of the 10th European Conference on Computer Vision: Part I
Real-Time Object Tracking for Augmented Reality Combining Graph Cuts and Optical Flow
ISMAR '07 Proceedings of the 2007 6th IEEE and ACM International Symposium on Mixed and Augmented Reality
Object segmentation in video via graph cut built on superpixels
Fundamenta Informaticae - Cognitive Informatics, Cognitive Computing, and Their Denotational Mathematical Foundations (II)
On Total Variation Minimization and Surface Evolution Using Parametric Maximum Flows
International Journal of Computer Vision
Foreground Segmentation via Segments Tracking
ICCVG 2008 Proceedings of the International Conference on Computer Vision and Graphics: Revised Papers
A Gradient Descent Approximation for Graph Cuts
Proceedings of the 31st DAGM Symposium on Pattern Recognition
Real-Time Online Video Object Silhouette Extraction Using Graph Cuts on the GPU
ICIAP '09 Proceedings of the 15th International Conference on Image Analysis and Processing
Joint tracking and segmentation of objects using graph cuts
ACIVS'07 Proceedings of the 9th international conference on Advanced concepts for intelligent vision systems
An improved interactive color image segmentation using region-based graph cuts
Machine Graphics & Vision International Journal
Geodesic image and video editing
ACM Transactions on Graphics (TOG)
An efficient graph cut algorithm for computer vision problems
ECCV'10 Proceedings of the 11th European conference on computer vision conference on Computer vision: Part III
Learning a nonlinear distance metric for supervised region-merging image segmentation
Computer Vision and Image Understanding
An attempt to segment foreground in dynamic scenes
ISVC'11 Proceedings of the 7th international conference on Advances in visual computing - Volume Part I
Solving multilabel MRFs using incremental α-expansion on the GPUs
ACCV'09 Proceedings of the 9th Asian conference on Computer Vision - Volume Part III
Interactive shadow removal from a single image using hierarchical graph cut
ACCV'09 Proceedings of the 9th Asian conference on Computer Vision - Volume Part I
Object segmentation in video via graph cut built on superpixels
Fundamenta Informaticae - Cognitive Informatics, Cognitive Computing, and Their Denotational Mathematical Foundations (II)
A survey of graph theoretical approaches to image segmentation
Pattern Recognition
Computer Vision and Image Understanding
Geo-clouds: visualizing news over geographical maps
Proceedings of the 4th ACM SIGSPATIAL International Workshop on GeoStreaming
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This paper adds a number of novel concepts into global s/t cut methods improving their efficiency and making them relevant for a wider class of applications in vision where algorithms should ideally run in real-time. Our new Active Cuts (AC) method can effectively use a good approximate solution (initial cut) that is often available in dynamic, hierarchical, and multi-label optimization problems in vision. In many problems AC works faster than the state-of-the-art max-flow methods [2] even if initial cut is far from the optimal one. Moreover, empirical speed improves several folds when initial cut is spatially close to the optima. Before converging to a global minima, Active Cuts outputs a multitude of intermediate solutions (intermediate cuts) that, for example, can be used be accelerate iterative learning-based methods or to improve visual perception of graph cuts realtime performance when large volumetric data is segmented. Finally, it can also be combined with many previous methods for accelerating graph cuts.