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 language | English |
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Article number | 8476587 |
Pages (from-to) | 880-895 |
Number of pages | 16 |
Journal | IEEE Transactions on Image Processing |
Volume | 28 |
Issue number | 2 |
DOIs | |
State | Published - 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