no code implementations • 9 Jul 2023 • Nati Ofir, Jean-Christophe Nebel
For each such pair, RGB and NIR, the network is trained for seconds to deduce the final fusion.
no code implementations • 21 Mar 2023 • Nati Ofir, Yotam Ben Shoshan, Ran Badanes, Boris Sherman
This work is addressing the problem of defect anomaly detection based on a clean reference image.
no code implementations • 7 Feb 2022 • Nati Ofir, Ran Yacobi, Omer Granoviter, Boris Levant, Ore Shtalrid
We train a U-net shape network to segment defects using a dataset of clean background images.
1 code implementation • 21 Dec 2021 • Nati Ofir, Jean-Christophe Nebel
This paper focuses on the task of fusing color (RGB) and near-infrared (NIR) images as this the typical RGBT sensors, as in multispectral cameras for detection, fusion, and dehazing.
no code implementations • 24 Jan 2021 • Nati Ofir, Jean-Christophe Nebel
Computer vision and image processing address many challenging applications.
1 code implementation • 26 Mar 2018 • Nati Ofir, Yosi Keller
As our experiments show, we achieve high-quality results in the three aspects of faint edge detection, noisy image classification and natural image denoising.
no code implementations • 16 Jan 2018 • Nati Ofir, Shai Silberstein, Hila Levi, Dani Rozenbaum, Yosi Keller, Sharon Duvdevani Bar
Our algorithm detects corners by Harris and matches them by a patch-metric learned on top of CIFAR-10 network descriptor.
no code implementations • 5 Nov 2017 • Nati Ofir, Shai Silberstein, Dani Rozenbaum, Yosi Keller, Sharon Duvdevani Bar
In this paper we introduce a fully end-to-end approach for multi-spectral image registration and fusion.
no code implementations • 25 Jun 2017 • Ofer Bartal, Nati Ofir, Yaron Lipman, Ronen Basri
We present a novel embedding method that maps pixels to normals on the unit hemisphere.
2 code implementations • 22 Jun 2017 • Nati Ofir, Meirav Galun, Sharon Alpert, Achi Brandt, Boaz Nadler, Ronen Basri
A fundamental question for edge detection in noisy images is how faint can an edge be and still be detected.
3 code implementations • CVPR 2016 • Nati Ofir, Meirav Galun, Boaz Nadler, Ronen Basri
Detecting edges is a fundamental problem in computer vision with many applications, some involving very noisy images.