Relaxation labelling is an image treatment methodology. Its goal is to associate a label to the pixels of a given image or nodes of a given graph.[1]
See also
editReferences
edit- ^ A. Dave Marshall; Ralph R. Martin (1992), Computer vision, models, and inspection, World Scientific, p. 149, ISBN 978-981-02-0772-4
Further reading
edit- Marcello Pelillo (1997). "The dynamics of nonlinear relaxation labeling processes". Journal of Mathematical Imaging and Vision. 7 (4): 309–323. doi:10.1023/A:1008255111261. S2CID 16789724.309-323&rft.date=1997&rft_id=info:doi/10.1023/A:1008255111261&rft_id=https://api.semanticscholar.org/CorpusID:16789724#id-name=S2CID&rft.au=Marcello Pelillo&rfr_id=info:sid/en.wikipedia.org:Relaxation labelling" class="Z3988"> (Full text: [1])
- Kuner, Peter; Ueberreiter, Birgit (1988). "Pattern Recognition by Graph Matching – Combinatorial Versus Continuous Optimization". International Journal of Pattern Recognition and Artificial Intelligence. 02 (3): 527–542. doi:10.1142/S0218001488000303. Retrieved January 3, 2012.527-542&rft.date=1988&rft_id=info:doi/10.1142/S0218001488000303&rft.au=Kuner, Peter&rft.au=Ueberreiter, Birgit&rft_id=http://www.worldscinet.com/ijprai/02/0203/S0218001488000303.html&rfr_id=info:sid/en.wikipedia.org:Relaxation labelling" class="Z3988"> (Full text: [2])
- Andrew Lewis; Sanaz Mostaghim; Marcus Randall (2009), Biologically-inspired Optimisation Methods: Parallel Algorithms, Systems and Applications, Springer, p. 110, ISBN 978-3-642-01261-7
- Terry Caelli; Walter F. Bischof (1997), Machine learning and image interpretation, Springer, p. 160, ISBN 978-0-306-45761-6
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