Spectral total-variation local scale signatures for image manipulation and fusion

Ester Hait, Guy Gilboa

Research output: Contribution to journalArticlepeer-review

Abstract

We propose a unified framework for isolating, comparing, and differentiating objects within an image. We rely on the recently proposed total-variation transform, yielding a continuous, multi-scale, fully edge-preserving, and local descriptor, referred to as spectral total-variation local scale signatures. We show and analyze several useful merits of this framework. Signatures are sensitive to size, local contrast, and composition of structures; are invariant to translation, rotation, flip, and linear illumination change; and texture signatures are robust to the underlying structures. We prove exact conditions in the 1D case. We propose several applications for this framework: Saliency map extraction for fusion of thermal and optical images or for medical imaging, clustering of vein-like features, and size-based image manipulation.

Original languageEnglish
Article number8476587
Pages (from-to)880-895
Number of pages16
JournalIEEE Transactions on Image Processing
Volume28
Issue number2
DOIs
StatePublished - Feb 2019

Keywords

  • Spectral total-variation
  • clustering
  • edge detection
  • image fusion
  • image segmentation
  • medical imagery
  • saliency
  • size differentiation
  • thermal imagery

All Science Journal Classification (ASJC) codes

  • Software
  • Computer Graphics and Computer-Aided Design

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