Ido Cohen, Tom Berkov, arXiv preprint Code Abstract The space-discrete Total Variation (TV) flow is analyzed using several mode decomposition techniques. In the one-dimensional case, we provide analytic formulations to Dynamic Mode Decomposition (DMD) and to Koopman Mode Decomposition (KMD) of the TV-flow and compare the obtained modes to TV spectral decomposition. We propose a […]
Eyal Gofer, Guy Gilboa, arXiv preprint Abstract The Moore-Penrose inverse is widely used in physics, statistics and various fields of engineering. Among other characteristics, it captures well the notion of inversion of linear operators in the case of overcomplete data. In data science, nonlinear operators are extensively used. In this paper we define and […]
The 32nd British Machine Vision Conference (BMVC), Nov. 2021. Damian Kaliroff and Guy Gilboa BMVC link to paper, video and code Abstract We propose a new and completely data-driven approach for generating a photo- consistent image transform. We show that simple classical algorithms which operate in the transform domain become extremely resilient to illumination changes. […]
Accepted to IEEE Trans. Image Processing, 2021. Eyal Gofer, Shachar Praisler, Guy Gilboa, arXiv Project details Abstract This work considers the problem of depth completion, with or without image data, where an algorithm may measure the depth of a prescribed limited number of pixels. The algorithmic challenge is to choose pixel positions strategically and […]
Ido Cohen, Guy Gilboa, arXiv preprint 2107.07456, 2021 Abstract This work binds the existence of Koopman Eigenfunctions (KEF’s), the geometric of the dynamics, and the validity of Dynamic Mode Decomposition (DMD) to one coherent theory. Viewing the dynamic as a curve in the state-space allows us to formulate an existence condition of KEF’s and […]
Ido Cohen, Tom Berkov, Guy Gilboa, “Total-Variation Mode Decomposition”, Proc. SSVM 2021, pp. 52-64 Abstract In this work we analyze the Total Variation (TV) flow applied to one dimensional signals. We formulate a relation between Dynamic Mode Decomposition (DMD), a dimensionality reduction method based on the Koopman operator, and the spectral TV decomposition. DMD is adapted by time rescaling […]
Jonathan Brokman, Guy Gilboa, Proc. SSVM, “Nonlinear Spectral Processing of Shapes via Zero-homogeneous Flows”, pp. 40-51, 2021 Abstract In this work we extend the spectral total-variation framework, and use it to analyze and process 2D manifolds embedded in 3D. Analysis is performed in the embedding space – thus “spectral arithmetics” manipulate the shape directly. This […]
Accepted to SIAM J. on Imaging Scienes, 2021 Leon Bungert, Ester Hait-Fraenkel , Nicolas Papadakis and Guy Gilboa, arXiv Neural networks have revolutionized the field of data science, yielding remarkable solutions in a data-driven manner. For instance, in the field of mathematical imaging, they have surpassed traditional methods based on convex regularization. However, a fundamental […]
Accepted to SIAM J. on Imaging Sciences, 2021 Ido Cohen, Omri Azencot, Pavel Lifshitz, Guy Gilboa, arXiv, July 2020 Finding latent structures in data is drawing increasing attention in broad and diverse fields such as fluid dynamics, signal processing, and machine learning. In this work, we formulate Dynamic Mode Decomposition (DMD) for two types […]
Ester Hait-Fraenkel, Guy Gilboa, Revealing stable and unstable modes of denoisers through nonlinear eigenvalue analysis. J. Vis. Commun. Image Represent. 75: 103041 (2021) Ester Hait-Fraenkel, Guy Gilboa, arXiv In this paper, we propose to analyze stable and unstable modes of generic image denoisers through nonlinear eigenvalue analysis. We attempt to find input images for which […]
Eyal Gofer, Guy Gilboa, arxiv preprint The most prominent feedback models for the best expert problem are the full information and bandit models. In this work we consider a simple feedback model that generalizes both, where on every round, in addition to a bandit feedback, the adversary provides a lower bound on the loss of […]
Guy Gilboa, arXiv preprint A chapter to appear in Handbook of Mathematical Models and Algorithms in Computer Vision and Imaging. In this chapter we are examining several iterative methods for solving nonlinear eigenvalue problems. These arise in variational image-processing, graph partition and classification, nonlinear physics and more. The canonical eigenproblem we solve is $T(u)=\lambda […]
Tamara G. Grossmann, Yury Korolev, Guy Gilboa, Carola-Bibiane Schönlieb, arXiv 2020 Accepted for NeurIPS 2020. Non-linear spectral decompositions of images based on one-homogeneous functionals such as total variation have gained considerable attention in the last few years. Due to their ability to extract spectral components corresponding to objects of different size and contrast, such decompositions […]
SIAM Conf on Imaging Sciences 2020 was held virtually this year. Ety and Ido presented talks at the minisymposium Nonlinear Spectral Analysis with Applications in Imaging and Data Science (a minisymposium organized by Leon Bungert, Ido Cohen and Guy Gilboa) Jonathan and Guy presented a joint work at the minisymposium Are We Ready for Semi-Supervised […]
A new website for the group has been constructed. Thx Yossi!
The MSc Seminar of Damian Kaliroff was performed through Zoom on 23.4.2020. In the image – our group (after escape-room + lazer-tag event) in RGB and in the new representation. Here are links to Seminar recording (in Hebrew, download the file to play the full 48 minutes talk) Presentation Paper (D. Kaliroff, G. Gilboa, arXiv […]
Adam Wolff, Shachar Praisler, Ilya Tcenov and Guy Gilboa, “Super-Pixel Sampler – a Data-driven Approach for Depth Sampling and Reconstruction”, accepted to ICRA (Int. Conf. on Robotics and Automation) 2020. Paper See the video of our mechanical prototype Abstract Depth acquisition, based on active illumination, is essential for autonomous and robotic navigation. LiDARs (Light Detection […]
Yossi, Shachar & Ilya – gathering data with the drone operators.
Damian Kaliroff, Guy Gilboa, arXiv We propose a new and completely data-driven approach for generating an unconstrained illumination invariant representation of images. Our method trains a neural network with a specialized triplet loss designed to emphasize actual scene changes while downplaying changes in illumination. For this purpose we use the BigTime image dataset, which contains […]
Ido Cohen, Guy Gilboa, “Energy dissipating flows for solving nonlinear eigenpair problems”, Journal of Computational Physics 375 (2018), 1138-1158. This work is concerned with computing nonlinear eigenpairs, which model solitary waves and various other physical phenomena. We aim at solving nonlinear eigenvalue problems of the general form $T(u)=\lambda Q(u)$. In our setting $T$ is a […]
Many thanks to the whole group for contributing, commenting and proof-reading the new book. The online version can be downloaded at the Springer site
A 4 year grant NoMADS, Nonlocal Methods for Arbitrary Data Sources, as part of the RISE program, started on March 2018. Includes Collaboration between universities (Munster, Cambridge, UCLA, Bordeaux, Carnegie Mellon, Technion and more..) and industry.
The paper, based on the master thesis of Raz Nossek is now accepted (Sept 2017): R. Nossek & G. Gilboa, “Flows generating nonlinear eigenfunctions”, Journal of Scientific Computing. (a pdf of the accepted version will be published soon, see arXiv version)
Ety’s paper in IEEE Trans. on Image Processing is published, “Blind Facial Image Quality Enhancement Using Non-Rigid Semantic Patches “.
Tal and Guy participated in a workshop in Cambridge (Newton Institute) on Variational methods, new optimisation techniques and new fast numerical algorithms. Tal presented a poster on Spectral TV Hashing. Guy presented a talk: Nonlinear spectral analysis – beyond the convex case
Participating in a workshop on Mathematical Imaging and Surface Processing at the Oberwolfach Math Institute, Germany, January 2016.
Requirements Strong mathemetical and theoretical background. Knowledge in PDE’s, variational methods and convex analysis. PhD from departments of Applied Math/EE/CS. Solid publication record. Creativity. Knowledge in image processing – an advantage (not a must). Knowledge in Matlab – an advantage (not a must). English – high writing and speaking skills. Description of Position Performing research […]
Giving 2 talks (and being a co-author in 3 others) in SIAM Conf on Imaging Sciences.
Talk at the Department of Applied Mathematics and Theoretical Physics, University of Cambridge, at the Applied and Computational Analysis Seminar on “Processing Textures in the Spectral Total-Variation Domain”, Oct. 2015.
The International Congress on Industrial and Applied Mathematics (ICIAM) is the premier international congress in the field of applied mathematics held every four years under the auspices of the International Council for Industrial and Applied Mathematics. From August 10 to 14, 2015, mathematicians from around the world will gather in Beijing, China for the 8th […]
Presenting papers in Scale Space and Variational Methods in Computer Vision (SSVM) in Lège Cap Ferret, France (June 2015).
Matlab code of some articles is given in a new Code section.
See more details.
I will be giving a talk at RICAM (Radon Institute for Computational and Applied Mathematics) in October. This is part of the Special Semester on New Trends in Calculus of Variations and is within the second workshop on Variational Methods in Imaging.
The renovation of the 3D Lab is finished! (July 2014) New projects concerning 3D video processing and analysis are planned to be conducted in the lab. It is located on the 6th floor of Meyer Bldg (room 661) and is part of the VISL lab. The 3D animation is thanks to Jason Hise.
See more on the Technion TCE confernce . Abstract of the talk Peeling Images – A Structured Layer Representation Using the TV Transform
The talk is given in Hong-Kong on May at SIAM-IS 2014 on texture analysis of color images using the TV transform (within the minisymposium on Color Perception and Image Enhancement, see program) .