{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/object-proposal-generation-applying-the","title":"Object proposal generation applying the distance dependent Chinese restaurant process","arxiv_id":"1704.03706","date":"2017-04-12","proceeding":null,"authors":["Mikko Lauri","Simone Frintrop"],"abstract":"In application domains such as robotics, it is useful to represent the\nuncertainty related to the robot's belief about the state of its environment.\nAlgorithms that only yield a single \"best guess\" as a result are not\nsufficient. In this paper, we propose object proposal generation based on\nnon-parametric Bayesian inference that allows quantification of the likelihood\nof the proposals. We apply Markov chain Monte Carlo to draw samples of image\nsegmentations via the distance dependent Chinese restaurant process. Our method\nachieves state-of-the-art performance on an indoor object discovery data set,\nwhile additionally providing a likelihood term for each proposal. We show that\nthe likelihood term can effectively be used to rank proposals according to\ntheir quality.","url_abs":"http://arxiv.org/abs/1704.03706v1","url_pdf":"http://arxiv.org/pdf/1704.03706v1.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"object-proposal-generation-applying-the","repo_url":"https://github.com/laurimi/ddcrp-gibbs","is_official":1,"mentioned_in_paper":0,"mentioned_in_github":0,"framework":"none","reach":null}],"tasks":[{"task_slug":"bayesian-inference","task_name":"Bayesian Inference"},{"task_slug":"object","task_name":"Object"},{"task_slug":"object-discovery","task_name":"Object Discovery"},{"task_slug":"object-proposal-generation","task_name":"Object Proposal Generation"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}