Geometry and Radiometry Invariant Matched Manifold Detection

Ran Sharon, Joseph M. Francos, Rami R. Hagege

Research output: Contribution to journalArticlepeer-review

7 Scopus citations


Consider a set of deformable objects undergoing geometric and radiometric transformations. As a result of the action of these transformations, the set of different realizations of each object is generally a manifold in the space of observations. Assuming the geometric deformations an object undergoes, belong to some finite dimensional family, it has been shown that the universal manifold embedding (UME) provides a set of nonlinear operators that universally maps each of the different manifolds, where each manifold is generated by the set all of possible appearances of a single object, into a distinct linear subspace of an Euclidean space. In this paper, we generalize this framework to the case where the observed object undergoes both an affine geometric transformation, and a monotonic radiometric transformation, and present a novel framework for the detection and recognition of the deformable objects. Applying to each of the observations an operator that makes it invariant to monotonic amplitude transformations, but is geometry-covariant with the affine transformation, the set of all possible observations on that object is mapped by the UME into a single linear subspace - invariant with respect to both the geometric and radiometric transformations. The embedding of the space of observations is independent of the specific observed object; hence it is universal. The invariant representation of the object is the basis of a matched manifold detection and tracking framework of objects that undergo complex geometric and radiometric deformations: the observed surface is tessellated into a set of tiles such that the deformation of each one is well approximated by an affine geometric transformation and a monotonic transformation of the measured intensities. Since each tile is mapped by the radiometry invariant UME to a distinct linear subspace, the detection and tracking problems are solved by evaluating distances between linear subspaces. Classification in this context becomes a problem of determining which labeled subspace in a Grassmannian is closest to a subspace in the same Grassmannian, where the latter has been generated by radiometry invariant UME from an unlabeled observation.

Original languageEnglish
Article number7913573
Pages (from-to)4363-4377
Number of pages15
JournalIEEE Transactions on Image Processing
Issue number9
StatePublished - 1 Sep 2017


  • Distance learning
  • image analysis
  • matched filters
  • object detection

ASJC Scopus subject areas

  • Software
  • Computer Graphics and Computer-Aided Design


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