{"id":182408,"date":"2008-07-17T00:00:00","date_gmt":"2009-10-31T09:37:56","guid":{"rendered":"https:\/\/new-cm-edgedigital.pages.dev\/en-us\/research\/msr-research-item\/3d-object-localization-and-shape-matching\/"},"modified":"2024-10-03T13:31:46","modified_gmt":"2024-10-03T20:31:46","slug":"3d-object-localization-and-shape-matching","status":"publish","type":"msr-video","link":"https:\/\/new-cm-edgedigital.pages.dev\/en-us\/research\/video\/3d-object-localization-and-shape-matching\/","title":{"rendered":"3D Object Localization and Shape Matching"},"content":{"rendered":"<div class=\"asset-content\">\n<p>This talk consists of two short talks.<\/p>\n<p>Localization of 3D Audio-Visual Objects Using Unsupervised Clustering<br \/>\nWe address the problem of localizing objects that can be both seen and heard. We exploit the benefits of a human-like configuration of sensors (binaural and binocular) for gathering auditory and visual observations. It is shown that the localization problem can be recast as the task of clustering the audio-visual observations into coherent groups. We propose a probabilistic generative model that captures the relations between audio and visual observations. This model maps the data into a common audio-visual 3D representation via a pair of mixture models. Inference is performed by a version of EM which provides cooperative estimates of both the auditory activity and the 3D position of each object. We describe several experiments with single- and multiple-speaker localization, in the presence of other audio sources.<\/p>\n<p>Robust Shape and Graph Matching using Laplacian Embedding and EM<br \/>\nShape matching is a central topic in computational vision, medical image analysis, etc. One instance of shape matching is to find dense correspondences between point representations. The problem of matching 3-D articulated shapes remains very difficult, mainly because it is not clear how to choose a transformation group under whose action the shapes could be studied. One possible approach is to represent shapes by locally connected sets of points, i.e. sparse graphs, and to use a spectral embedding method in order to map these graphs onto a lower dimensional space. As a result, a dense match between shapes can be found through rigid point registration of their embeddings. We will describe in detail the matching method and show numerous results with voxel- and mesh-data.<\/p>\n<\/div>\n<p><!-- .asset-content --><\/p>\n","protected":false},"excerpt":{"rendered":"<p>This talk consists of two short talks. Localization of 3D Audio-Visual Objects Using Unsupervised Clustering We address the problem of localizing objects that can be both seen and heard. We exploit the benefits of a human-like configuration of sensors (binaural and binocular) for gathering auditory and visual observations. It is shown that the localization problem [&hellip;]<\/p>\n","protected":false},"featured_media":1089465,"template":"","meta":{"msr-url-field":"","msr-podcast-episode":"","msrModifiedDate":"","msrModifiedDateEnabled":false,"ep_exclude_from_search":false,"_classifai_error":"","msr_hide_image_in_river":0,"footnotes":""},"research-area":[13551],"msr-video-type":[],"msr-locale":[268875],"msr-post-option":[],"msr-session-type":[],"msr-impact-theme":[],"msr-pillar":[],"msr-episode":[],"msr-research-theme":[],"class_list":["post-182408","msr-video","type-msr-video","status-publish","has-post-thumbnail","hentry","msr-research-area-graphics-and-multimedia","msr-locale-en_us"],"msr_download_urls":"","msr_external_url":"https:\/\/youtu.be\/3jmB6w763tc","msr_secondary_video_url":"","msr_video_file":"http:\/\/0","_links":{"self":[{"href":"https:\/\/new-cm-edgedigital.pages.dev\/en-us\/research\/wp-json\/wp\/v2\/msr-video\/182408","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/new-cm-edgedigital.pages.dev\/en-us\/research\/wp-json\/wp\/v2\/msr-video"}],"about":[{"href":"https:\/\/new-cm-edgedigital.pages.dev\/en-us\/research\/wp-json\/wp\/v2\/types\/msr-video"}],"version-history":[{"count":1,"href":"https:\/\/new-cm-edgedigital.pages.dev\/en-us\/research\/wp-json\/wp\/v2\/msr-video\/182408\/revisions"}],"predecessor-version":[{"id":1089468,"href":"https:\/\/new-cm-edgedigital.pages.dev\/en-us\/research\/wp-json\/wp\/v2\/msr-video\/182408\/revisions\/1089468"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/new-cm-edgedigital.pages.dev\/en-us\/research\/wp-json\/wp\/v2\/media\/1089465"}],"wp:attachment":[{"href":"https:\/\/new-cm-edgedigital.pages.dev\/en-us\/research\/wp-json\/wp\/v2\/media?parent=182408"}],"wp:term":[{"taxonomy":"msr-research-area","embeddable":true,"href":"https:\/\/new-cm-edgedigital.pages.dev\/en-us\/research\/wp-json\/wp\/v2\/research-area?post=182408"},{"taxonomy":"msr-video-type","embeddable":true,"href":"https:\/\/new-cm-edgedigital.pages.dev\/en-us\/research\/wp-json\/wp\/v2\/msr-video-type?post=182408"},{"taxonomy":"msr-locale","embeddable":true,"href":"https:\/\/new-cm-edgedigital.pages.dev\/en-us\/research\/wp-json\/wp\/v2\/msr-locale?post=182408"},{"taxonomy":"msr-post-option","embeddable":true,"href":"https:\/\/new-cm-edgedigital.pages.dev\/en-us\/research\/wp-json\/wp\/v2\/msr-post-option?post=182408"},{"taxonomy":"msr-session-type","embeddable":true,"href":"https:\/\/new-cm-edgedigital.pages.dev\/en-us\/research\/wp-json\/wp\/v2\/msr-session-type?post=182408"},{"taxonomy":"msr-impact-theme","embeddable":true,"href":"https:\/\/new-cm-edgedigital.pages.dev\/en-us\/research\/wp-json\/wp\/v2\/msr-impact-theme?post=182408"},{"taxonomy":"msr-pillar","embeddable":true,"href":"https:\/\/new-cm-edgedigital.pages.dev\/en-us\/research\/wp-json\/wp\/v2\/msr-pillar?post=182408"},{"taxonomy":"msr-episode","embeddable":true,"href":"https:\/\/new-cm-edgedigital.pages.dev\/en-us\/research\/wp-json\/wp\/v2\/msr-episode?post=182408"},{"taxonomy":"msr-research-theme","embeddable":true,"href":"https:\/\/new-cm-edgedigital.pages.dev\/en-us\/research\/wp-json\/wp\/v2\/msr-research-theme?post=182408"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}