A Theory for Optical Flow-based Transport on Image Manifolds

Abstract

An image articulation manifold (IAM) is the collection of images formed when an object is articulated in front of a camera. IAMs arise in a variety of image processing and computer vision applications, where they provide a natural lowdimensional embedding of the collection of high-dimensional images. To date IAMs have been studied as embedded submanifolds of Euclidean spaces. Unfortunately their promise has not been realized in practice, because real world imagery typically contains sharp edges that render an IAM non-differentiable and hence non-isometric to the low-dimensional parameter space under the Euclidean metric. As a result, the standard tools from differential geometry, in particular using linear tangent spaces to transport along the IAM, have limited utility. In this paper, we explore a nonlinear transport operator for IAMs based on the optical flow between images and develop new analytical tools reminiscent of those from differential geometry using the idea of optical flow manifolds (OFMs). We define a new metric for IAMs that satisfies certain local isometry conditions, and we show how to use this metric to develop a new tools such as flow fields on IAMs parallel flow fields, parallel transport, as well as a intuitive notion of curvature. The space of optical flow fields along a path of constant curvature has a natural multi-scale structure via a monoid structure on the space of all flow fields along a path. We also develop lower bounds on approximation errors while approximating non-parallel flow fields by parallel flow fields.

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Document Details

Document Type
Technical Report
Publication Date
Nov 21, 2011
Accession Number
ADA557381

Entities

People

  • Aswin C. Sankaranarayanan
  • Richard G. Baraniuk
  • Sriram Nagaraj

Organizations

  • Rice University

Tags

Communities of Interest

  • C4I

DTIC Thesaurus Topics

  • Computations
  • Computer Vision
  • Computers
  • Curvature
  • Differential Geometry
  • Dimensionality Reduction
  • Flow Fields
  • Geometric Forms
  • Geometry
  • Image Processing
  • Lie Groups
  • Lines (Geometry)
  • Machine Learning
  • Recognition
  • Three Dimensional
  • Transport Ships
  • Two Dimensional

Fields of Study

  • Computer science

Readers

  • Computer Vision.
  • Graph Algorithms and Convex Optimization.

Technology Areas

  • AI & ML
  • AI & ML - Machine Learning Algorithms
  • Space
  • Space - Space Objects