{"page":"2089 - 2096","doi":"10.1109/CVPR.2011.5995503","publication":"CVPR: Computer Vision and Pattern Recognition","day":"22","author":[{"first_name":"Chao","last_name":"Chen","id":"3E92416E-F248-11E8-B48F-1D18A9856A87","full_name":"Chen, Chao"},{"first_name":"Daniel","last_name":"Freedman","full_name":"Freedman, Daniel"},{"orcid":"0000-0001-8622-7887","last_name":"Lampert","first_name":"Christoph","full_name":"Lampert, Christoph","id":"40C20FD2-F248-11E8-B48F-1D18A9856A87"}],"type":"conference","publication_identifier":{"isbn":["978-1-4577-0394-2"],"eisbn":["978-1-4577-0395-9"]},"date_updated":"2023-02-23T12:23:56Z","month":"07","article_processing_charge":"No","citation":{"ieee":"C. Chen, D. Freedman, and C. Lampert, “Enforcing topological constraints in random field image segmentation,” in <i>CVPR: Computer Vision and Pattern Recognition</i>, Colorado Springs, CO, United States, 2011, pp. 2089–2096.","mla":"Chen, Chao, et al. “Enforcing Topological Constraints in Random Field Image Segmentation.” <i>CVPR: Computer Vision and Pattern Recognition</i>, IEEE, 2011, pp. 2089–96, doi:<a href=\"https://doi.org/10.1109/CVPR.2011.5995503\">10.1109/CVPR.2011.5995503</a>.","short":"C. Chen, D. Freedman, C. Lampert, in:, CVPR: Computer Vision and Pattern Recognition, IEEE, 2011, pp. 2089–2096.","ama":"Chen C, Freedman D, Lampert C. Enforcing topological constraints in random field image segmentation. In: <i>CVPR: Computer Vision and Pattern Recognition</i>. IEEE; 2011:2089-2096. doi:<a href=\"https://doi.org/10.1109/CVPR.2011.5995503\">10.1109/CVPR.2011.5995503</a>","apa":"Chen, C., Freedman, D., &#38; Lampert, C. (2011). Enforcing topological constraints in random field image segmentation. In <i>CVPR: Computer Vision and Pattern Recognition</i> (pp. 2089–2096). Colorado Springs, CO, United States: IEEE. <a href=\"https://doi.org/10.1109/CVPR.2011.5995503\">https://doi.org/10.1109/CVPR.2011.5995503</a>","ista":"Chen C, Freedman D, Lampert C. 2011. Enforcing topological constraints in random field image segmentation. CVPR: Computer Vision and Pattern Recognition. CVPR: Conference on Computer Vision and Pattern Recognition, 2089–2096.","chicago":"Chen, Chao, Daniel Freedman, and Christoph Lampert. “Enforcing Topological Constraints in Random Field Image Segmentation.” In <i>CVPR: Computer Vision and Pattern Recognition</i>, 2089–96. IEEE, 2011. <a href=\"https://doi.org/10.1109/CVPR.2011.5995503\">https://doi.org/10.1109/CVPR.2011.5995503</a>."},"publication_status":"published","date_published":"2011-07-22T00:00:00Z","conference":{"end_date":"2011-06-25","location":"Colorado Springs, CO, United States","start_date":"2011-06-20","name":"CVPR: Conference on Computer Vision and Pattern Recognition"},"abstract":[{"lang":"eng","text":"We introduce TopoCut: a new way to integrate knowledge about topological properties (TPs) into random field image segmentation model. Instead of including TPs as additional constraints during minimization of the energy function, we devise an efficient algorithm for modifying the unary potentials such that the resulting segmentation is guaranteed with the desired properties. Our method is more flexible in the sense that it handles more topology constraints than previous methods, which were only able to enforce pairwise or global connectivity. In particular, our method is very fast, making it for the first time possible to enforce global topological properties in practical image segmentation tasks."}],"language":[{"iso":"eng"}],"department":[{"_id":"HeEd"},{"_id":"ChLa"}],"_id":"3336","publist_id":"3294","date_created":"2018-12-11T12:02:45Z","user_id":"2DF688A6-F248-11E8-B48F-1D18A9856A87","oa_version":"None","scopus_import":"1","publisher":"IEEE","year":"2011","related_material":{"record":[{"relation":"earlier_version","id":"5386","status":"public"}]},"acknowledgement":"The first author is supported by the Austrian Science Fund (FWF) grant No. P20134-N13. The authors would like to thank Sebastian Nowozin for helpful discussions.","status":"public","title":"Enforcing topological constraints in random field image segmentation","quality_controlled":"1"}