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Learning to see in 3D

Title
Learning to see in 3D
Funding
ARC | Discovery Projects
Contract (GA) number
DP120101172
Start Date
2012/01/01
End Date
2014/12/31
Open Access mandate
no
Organizations
-
More information
http://purl.org/au-research/grants/arc/DP120101172

 

  • Large-scale Binary Quadratic Optimization Using Semidefinite Relaxation and Applications

    Wang, Peng; Shen, Chunhua; Hengel, Anton van den; Torr, Philip H. S. (2014)
    Projects: ARC | Continuously learning to see (FT120100969), ARC | Learning to see in 3D (DP120101172)
    In computer vision, many problems such as image segmentation, pixel labelling, and scene parsing can be formulated as binary quadratic programs (BQPs). For submodular problems, cuts based methods can be employed to efficiently solve large-scale problems. However, general nonsubmodular problems are significantly more challenging to solve. Finding a solution when the problem is of large size to be of practical interest, however, typically requires relaxation. Two standard relaxation methods are...
  • No project research data found
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