N
Nir Sochen
Researcher at Tel Aviv University
Publications - 217
Citations - 7559
Nir Sochen is an academic researcher from Tel Aviv University. The author has contributed to research in topics: Image processing & Image segmentation. The author has an hindex of 40, co-authored 213 publications receiving 7085 citations. Previous affiliations of Nir Sochen include Technion – Israel Institute of Technology & French Alternative Energies and Atomic Energy Commission.
Papers
More filters
Journal ArticleDOI
Trajectories in parallel optics
TL;DR: In this paper, a series of shifted responses of auxiliary optics (named trajectories), where a complicated hardware filter is replaced by postprocessing, is used to improve the optical system's matrix condition.
Journal ArticleDOI
Spectral Analysis of a Non-Equilibrium Stochastic Dynamics on a General Network
Inbar Seroussi,Nir Sochen +1 more
TL;DR: A stochastic dynamical model with a multiplicative noise living on a graph, similar to approaches used in population dynamics or directed polymers in random media, is studied, showing that this general model has different phases depending on the topology of the network.
Proceedings ArticleDOI
Propagating distributions for segmentation of brain atlas
Tammy Riklin-Raviv,Nir Sochen,Nahum Kiryati,N. Ben-Zadok,S. Gefen,L. Bertand,Jonathan Nissanov +6 more
TL;DR: A novel method for segmentation of anatomical structures in histological data carried out slice-by-slice where the success of one section provides a prior for the subsequent one, which compares well with manual segmentation.
Proceedings ArticleDOI
Images as embedding maps and minimal surfaces: a unified approach for image diffusion
Ron Kimmel,R. Malladi,Nir Sochen +2 more
TL;DR: A functional called "Polyakov (1981) action", borrowed from high energy physics, is shown to be useful for image enhancement in color, texture, volumetric medical data, movies, and more.
Posted Content
Solving the functional Eigen-Problem using Neural Networks.
TL;DR: This work explores the ability of NN (Neural Networks) to serve as a tool for finding eigen-pairs of ordinary differential equations, and suggests an alternative to numeric methods ofFinding eigenpairs which may potentially be more robust and have the ability to solve more complex problems.