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Image Morphing in Deep Feature Spaces: Theory and Applications.

Alexander Effland1, Erich Kobler1, Thomas Pock1

  • 1Institute of Computer Graphics and Vision, Graz University of Technology, Graz, Austria.

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Summary
This summary is machine-generated.

This study integrates deep learning features with image metamorphosis, enhancing image analysis. Deep features improve image metamorphosis accuracy over traditional intensity-based methods.

Keywords:
Convolutional neural networksImage morphingMetamorphosis modelMosco convergenceVariational time discretization

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Area of Science:

  • Computer Vision
  • Image Processing
  • Computational Mathematics

Background:

  • Image metamorphosis models analyze image variations using geometric transformations.
  • Existing models often rely on intensity information, limiting their ability to capture semantic content.
  • Deep learning offers powerful feature extraction capabilities for complex data.

Purpose of the Study:

  • To integrate deep features into an established image metamorphosis framework.
  • To develop a structure-sensitive, anisotropic flow regularization for the metamorphosis model.
  • To investigate the theoretical and numerical properties of the proposed discrete model.

Main Methods:

  • Incorporation of deep features into the image metamorphosis model by treating images as maps to a high-dimensional feature space.
  • Development of a variational time discretization for the Riemannian path energy.
  • Spatial discretization using finite differences in image space and spline approximation in deformation space.
  • Optimization of the fully discrete model using the iPALM algorithm.

Main Results:

  • Demonstration of the existence of discrete geodesic paths minimizing the Riemannian path energy.
  • Investigation of the convergence of discrete geodesic paths to continuous geodesic paths.
  • Numerical experiments showing superior performance of semantic deep features compared to intensity-based methods.

Conclusions:

  • The integration of deep features significantly enhances image metamorphosis.
  • The proposed discrete model provides a robust framework for analyzing image variations.
  • This approach offers a promising direction for advanced image analysis and computer vision applications.