Fronts propagating with curvature-dependent speed: algorithms based on Hamilton-Jacobi formulations
Journal of Computational Physics
Normalized Cuts and Image Segmentation
IEEE Transactions on Pattern Analysis and Machine Intelligence
Fast Approximate Energy Minimization via Graph Cuts
IEEE Transactions on Pattern Analysis and Machine Intelligence
Mean Shift: A Robust Approach Toward Feature Space Analysis
IEEE Transactions on Pattern Analysis and Machine Intelligence
Graphcut textures: image and video synthesis using graph cuts
ACM SIGGRAPH 2003 Papers
Learning a Classification Model for Segmentation
ICCV '03 Proceedings of the Ninth IEEE International Conference on Computer Vision - Volume 2
Computing Geodesics and Minimal Surfaces via Graph Cuts
ICCV '03 Proceedings of the Ninth IEEE International Conference on Computer Vision - Volume 2
Efficient Graph-Based Image Segmentation
International Journal of Computer Vision
ACM SIGGRAPH 2004 Papers
An Experimental Comparison of Min-Cut/Max-Flow Algorithms for Energy Minimization in Vision
IEEE Transactions on Pattern Analysis and Machine Intelligence
Geometric Context from a Single Image
ICCV '05 Proceedings of the Tenth IEEE International Conference on Computer Vision (ICCV'05) Volume 1 - Volume 01
Guiding Model Search Using Segmentation
ICCV '05 Proceedings of the Tenth IEEE International Conference on Computer Vision - Volume 2
Graph Cuts and Efficient N-D Image Segmentation
International Journal of Computer Vision
Video abstraction: A systematic review and classification
ACM Transactions on Multimedia Computing, Communications, and Applications (TOMCCAP)
VideoTrace: rapid interactive scene modelling from video
ACM SIGGRAPH 2007 papers
IEEE Transactions on Pattern Analysis and Machine Intelligence
Object Recognition by Integrating Multiple Image Segmentations
ECCV '08 Proceedings of the 10th European Conference on Computer Vision: Part III
TurboPixels: Fast Superpixels Using Geometric Flows
IEEE Transactions on Pattern Analysis and Machine Intelligence
Recovering human body configurations: combining segmentation and recognition
CVPR'04 Proceedings of the 2004 IEEE computer society conference on Computer vision and pattern recognition
Learning and incorporating top-down cues in image segmentation
ECCV'06 Proceedings of the 9th European conference on Computer Vision - Volume Part I
Multichannel segmentation using contour relaxation: fast super-pixels and temporal propagation
SCIA'11 Proceedings of the 17th Scandinavian conference on Image analysis
A web interface for 3D visualization and interactive segmentation of medical images
Proceedings of the 17th International Conference on 3D Web Technology
Geodesic saliency using background priors
ECCV'12 Proceedings of the 12th European conference on Computer Vision - Volume Part III
Streaming hierarchical video segmentation
ECCV'12 Proceedings of the 12th European conference on Computer Vision - Volume Part VI
SEEDS: superpixels extracted via energy-driven sampling
ECCV'12 Proceedings of the 12th European conference on Computer Vision - Volume Part VII
Partition cortical surfaces into supervertices: method and application
MeshMed'12 Proceedings of the 2012 international conference on Mesh Processing in Medical Image Analysis
Efficient pixel-grouping based on dempster's theory of evidence for image segmentation
ACCV'12 Proceedings of the 11th Asian conference on Computer Vision - Volume Part I
Automatic noise modeling for ghost-free HDR reconstruction
ACM Transactions on Graphics (TOG)
Computer Vision and Image Understanding
Visual tracking using superpixel-based appearance model
ICVS'13 Proceedings of the 9th international conference on Computer Vision Systems
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Many methods for object recognition, segmentation, etc., rely on a tessellation of an image into "superpixels". A superpixel is an image patch which is better aligned with intensity edges than a rectangular patch. Superpixels can be extracted with any segmentation algorithm, however, most of them produce highly irregular superpixels, with widely varying sizes and shapes. A more regular space tessellation may be desired. We formulate the superpixel partitioning problem in an energy minimization framework, and optimize with graph cuts. Our energy function explicitly encourages regular superpixels. We explore variations of the basic energy, which allow a trade-off between a less regular tessellation but more accurate boundaries or better efficiency. Our advantage over previous work is computational efficiency, principled optimization, and applicability to 3D "supervoxel" segmentation. We achieve high boundary recall on images and spatial coherence on video. We also show that compact superpixels improve accuracy on a simple application of salient object segmentation.