Learning Probabilistic Models for Contour Completion.
Using a large set of human segmented natural images, we study the
statistics of region boundaries. We observe several power law distributions
which likely arise from both multi-scale structure within individual objects
and from arbitrary viewing distance. Accordingly, we develop a scale-invariant
representation of images from the bottom up, using a piecewise linear
approximation of contours and constrained Delaunay triangulation to complete
gaps. We model curvilinear grouping on top of this graphical/geometric
structure using a conditional random field to capture the statistics of
continuity and different junction types. Quantitative evaluations on several
large datasets show that our contour grouping algorithm consistently dominates
and significantly improves on local edge detection.
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Text Reference
Xiaofeng Ren, Charless Fowlkes, and Jitendra Malik. Learning probabilistic models for contour completion. IJCV, 77:47–63, 2008.BibTeX Reference
@article{RenFM_IJCV_2008,AUTHOR = "Ren, Xiaofeng and Fowlkes, Charless and Malik, Jitendra",
TITLE = "Learning Probabilistic Models for Contour Completion.",
JOURNAL = "IJCV",
YEAR = "2008",
VOLUME = "77",
PAGES = "47-63",
TAG = "grouping,ecological_statistics"
}