%0 Journal Article %1 6693720 %A Pham, T. T. %A Chin, T. J. %A Yu, J. %A Suter, D. %D 2014 %J IEEE Transactions on Pattern Analysis and Machine Intelligence %K cluster clusters;fitting computer cuts;hypothesis fitting;Accuracy;Computational fitting;guided framework;computer generation;multiple generation;robust geometric hypothesis model model;random modeling;Data models;Generators;Labeling;Optimization;Robustness;Robust noise;minimal optimization;graph sampling;hypothesis sampling;measurement selection;random spin structures subsets systems;data theory;annealing theory;set vision;coupled vision;graph %N 8 %P 1658-1671 %R 10.1109/TPAMI.2013.2296310 %T The Random Cluster Model for Robust Geometric Fitting %V 36

@article{6693720, added-at = {2022-03-14T06:22:26.000+0100}, author = {Pham, T. T. and Chin, T. J. and Yu, J. and Suter, D.}, biburl = {https://www.bibsonomy.org/bibtex/28d1215361c64c9f95da350bb1880fa8e/dsuter}, doi = {10.1109/TPAMI.2013.2296310}, interhash = {495d53943c4fa4c63be6e4840d834040}, intrahash = {8d1215361c64c9f95da350bb1880fa8e}, issn = {0162-8828}, journal = {IEEE Transactions on Pattern Analysis and Machine Intelligence}, keywords = {cluster clusters;fitting computer cuts;hypothesis fitting;Accuracy;Computational fitting;guided framework;computer generation;multiple generation;robust geometric hypothesis model model;random modeling;Data models;Generators;Labeling;Optimization;Robustness;Robust noise;minimal optimization;graph sampling;hypothesis sampling;measurement selection;random spin structures subsets systems;data theory;annealing theory;set vision;coupled vision;graph}, month = Aug, number = 8, pages = {1658-1671}, timestamp = {2022-03-14T06:22:26.000+0100}, title = {The Random Cluster Model for Robust Geometric Fitting}, volume = 36, year = 2014 }