Integral Geometric Dual Distributions of Multilinear Models

Research output: Working paperResearch


We propose an integral geometric approach for computing dual distributions for the parameter distributions of multilinear models. The dual distributions can be computed from, for example, the parameter distributions of conics, multiple view tensors, homographies, or as simple entities as points, lines, and planes. The dual distributions have analytical forms that follow from the asymptotic normality property of the maximum likelihood estimator and an application of integral transforms, fundamentally the generalised Radon transforms, on the probability density of the parameters. The approach allows us, for instance, to look at the uncertainty distributions in feature distributions, which are essentially tied to the distribution of training data, and helps us to derive conditional distributions for interesting variables and characterise confidence intervals of the estimates.
Original languageEnglish
Publication statusPublished - 22 Nov 2018


  • multiple view geometry
  • integral geometry
  • computer vision


Dive into the research topics of 'Integral Geometric Dual Distributions of Multilinear Models'. Together they form a unique fingerprint.

Cite this