Multimodal Partial Estimates Fusion

Jiang Xu, Junsong Yuan, Ying Wu

Research output: Contribution to journalConference articlepeer-review

5 Scopus citations


Fusing partial estimates is a critical and common problem in many computer vision tasks such as part-based detection and tracking. It generally becomes complicated and intractable when there are a large number of multimodal partial estimates, and thus it is desirable to find an effective and scalable fusion method to integrate these partial estimates. This paper presents a novel and effective approach to fusing multimodal partial estimates in a principled way. In this new approach, fusion is related to a computational geometry problem of finding the minimumvolume orthotope, and an effective and scalable branch and bound search algorithm is designed to obtain the global optimal solution. Experiments on tracking articulated objects and occluded objects show the effectiveness of the proposed approach.

Original languageEnglish (US)
Pages (from-to)2177-2184
Number of pages8
JournalProceedings of the IEEE International Conference on Computer Vision
StatePublished - 2009
Event12th International Conference on Computer Vision, ICCV 2009 - Kyoto, Japan
Duration: Sep 29 2009Oct 2 2009

ASJC Scopus subject areas

  • Software
  • Computer Vision and Pattern Recognition


Dive into the research topics of 'Multimodal Partial Estimates Fusion'. Together they form a unique fingerprint.

Cite this