{"id":164476,"date":"2006-01-01T00:00:00","date_gmt":"2006-01-01T00:00:00","guid":{"rendered":"https:\/\/www.microsoft.com\/en-us\/research\/msr-research-item\/mesh-analysis-using-geodesic-mean-shift\/"},"modified":"2018-10-16T20:09:35","modified_gmt":"2018-10-17T03:09:35","slug":"mesh-analysis-using-geodesic-mean-shift","status":"publish","type":"msr-research-item","link":"https:\/\/www.microsoft.com\/en-us\/research\/publication\/mesh-analysis-using-geodesic-mean-shift\/","title":{"rendered":"Mesh analysis using geodesic mean-shift"},"content":{"rendered":"<div class=\"asset-content\">\n<p>In this paper we introduce a versatile and robust method for analyzing the feature space associated with a given mesh surface. The method is based on the mean-shift operator which was shown to be successful in image and video processing. Its strength lays in the fact that it works in a single joint space of geometry and attributes called the feature-space. The mean-shift procedure works as a gradient ascend finding maxima of an estimated probability density function in feature-space. Our method for using the meanshift technique on surfaces solves several difficulties. First, meshes as opposed to images do not present a regular and uniform sampling of domain. Second, on surfaces meshes the shifting procedure must be constrained to stay on the surface and preserve geodesic distances. We define a special local geodesic parameterizations scheme, and use it to generalize the mean-shift procedure to unstructured surface meshes. Our method can support piecewise linear attribute definitions as well as piecewise constant attributes.<\/p>\n<\/div>\n<p><!-- .asset-content --><\/p>\n","protected":false},"excerpt":{"rendered":"<p>In this paper we introduce a versatile and robust method for analyzing the feature space associated with a given mesh surface. The method is based on the mean-shift operator which was shown to be successful in image and video processing. Its strength lays in the fact that it works in a single joint space of [&hellip;]<\/p>\n","protected":false},"featured_media":0,"template":"","meta":{"msr-url-field":"","msr-podcast-episode":"","msrModifiedDate":"","msrModifiedDateEnabled":false,"ep_exclude_from_search":false,"_classifai_error":"","msr-author-ordering":null,"msr_publishername":"Springer-Verlag New York, Inc.","msr_publisher_other":"","msr_booktitle":"","msr_chapter":"","msr_edition":"Visual Computer","msr_editors":"","msr_how_published":"","msr_isbn":"","msr_issue":"","msr_journal":"Visual Computer","msr_number":"2","msr_organization":"","msr_pages_string":"99\u2013108","msr_page_range_start":"99","msr_page_range_end":"108","msr_series":"","msr_volume":"22","msr_copyright":"","msr_conference_name":"","msr_doi":"10.1007\/s00371-006-0370-2","msr_arxiv_id":"","msr_s2_paper_id":"","msr_mag_id":"","msr_pubmed_id":"","msr_other_authors":"Ariel Shamir, Daniel 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