Ester Hait and Guy Gilboa
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, 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 changes; 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.
Concept and Algorithm:
Some experimental Results:
Paper, Supplementary and Matlab Code:
Hait, Ester, and Guy Gilboa. “Spectral Total-Variation Local Scale Signatures for Image Manipulation and Fusion.” IEEE Transactions on Image Processing 28.2 (2019): 880 – 895.
Early access (peer-reviewed and accepted) version in IEEE Transactions on Image Processing: