D
Dominique Zosso
Researcher at University of California, Los Angeles
Publications - 38
Citations - 4842
Dominique Zosso is an academic researcher from University of California, Los Angeles. The author has contributed to research in topics: Image segmentation & Segmentation. The author has an hindex of 14, co-authored 37 publications receiving 2802 citations. Previous affiliations of Dominique Zosso include Montana State University & University of Basel.
Papers
More filters
Journal ArticleDOI
Variational Mode Decomposition
TL;DR: This work proposes an entirely non-recursive variational mode decomposition model, where the modes are extracted concurrently and is a generalization of the classic Wiener filter into multiple, adaptive bands.
Book ChapterDOI
Two-Dimensional Variational Mode Decomposition
TL;DR: An entirely non-recursive 2D variational mode decomposition (2D-VMD) model, where the modes are extracted concurrently and the model looks for a number of 2D modes and their respective center frequencies, such that the bandlimited modes reproduce the input image.
Journal ArticleDOI
Efficient Algorithm for Level Set Method Preserving Distance Function
Virginia Estellers,Dominique Zosso,Rongjie Lai,Stanley Osher,Jean-Philippe Thiran,Xavier Bresson +5 more
TL;DR: The proposed algorithm is inspired by recent efficient l1 optimization techniques and it naturally preserves the level set function as a distance function during the evolution, which avoids the classical re-distancing problem in level set methods.
Journal ArticleDOI
Non-Local Retinex---A Unifying Framework and Beyond
TL;DR: This paper reinterpret the gradient thresholding model as variational models with sparsity constraints as well as defining the unifying Retinex model in two similar, but more general, steps.
Proceedings ArticleDOI
A unifying retinex model based on non-local differential operators
TL;DR: Within a single framework new retinex instances particularly suited for texture-preserving shadow removal, cartoon-texture decomposition, color and hyperspectral image enhancement are defined, and entirely novel retineX formulations are derived by using more interesting non-local versions for the sparsity and fidelity prior.