Watersheds in Digital Spaces: An Efficient Algorithm Based on Immersion Simulations
IEEE Transactions on Pattern Analysis and Machine Intelligence
Normalized Cuts and Image Segmentation
IEEE Transactions on Pattern Analysis and Machine Intelligence
Video Segmentation by MAP Labeling of Watershed Segments
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
ACM SIGGRAPH 2005 Papers
Guiding Model Search Using Segmentation
ICCV '05 Proceedings of the Tenth IEEE International Conference on Computer Vision - Volume 2
Supervised Learning of Edges and Object Boundaries
CVPR '06 Proceedings of the 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Volume 2
Seam carving for content-aware image resizing
ACM SIGGRAPH 2007 papers
IEEE Transactions on Pattern Analysis and Machine Intelligence
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The computational cost of video and motion analysis can be dramatically reduced by over-segmenting each frame of video into "superpixels". But most superpixel algorithms are themselves computationally expensive, and are thus inappropriate for use with real-time video. This paper advocates and analyzes the use of superpixels derived from minimum-cost paths that can be computed by dynamic programming. It is shown that superpixels can be computed comfortably in real time using such methods (30-40 times faster than the most efficient alternative), while sacrificing about 3% in the accuracy of the superpixels. The efficacy of the approach is demonstrated with a simple video analysis application.