Search Results for author: Egor Ershov

Found 6 papers, 4 papers with code

Multiple Light Source Dataset for Colour Research

1 code implementation16 Aug 2019 Anna Smagina, Egor Ershov, Anton Grigoryev

We present a collection of 24 multiple object scenes each recorded under 18 multiple light source illumination scenarios.

Benchmarking Image Segmentation +2

The Cube++ Illumination Estimation Dataset

1 code implementation19 Nov 2020 Egor Ershov, Alex Savchik, Illya Semenkov, Nikola Banić, Alexander Belokopytov, Daria Senshina, Karlo Koscević, Marko Subašić, Sven Lončarić

In this paper, a new illumination estimation dataset is proposed that aims to alleviate many of the mentioned problems and to help the illumination estimation research.

Color Constancy

Illumination Estimation Challenge: experience of past two years

no code implementations31 Dec 2020 Egor Ershov, Alex Savchik, Ilya Semenkov, Nikola Banić, Karlo Koscević, Marko Subašić, Alexander Belokopytov, Zhihao LI, Arseniy Terekhin, Daria Senshina, Artem Nikonorov, Yanlin Qian, Marco Buzzelli, Riccardo Riva, Simone Bianco, Raimondo Schettini, Sven Lončarić, Dmitry Nikolaev

The main advantage of testing a method on a challenge over testing in on some of the known datasets is the fact that the ground-truth illuminations for the challenge test images are unknown up until the results have been submitted, which prevents any potential hyperparameter tuning that may be biased.

Color Constancy Vocal Bursts Valence Prediction

On the properties of some low-parameter models for color reproduction in terms of spectrum transformations and coverage of a color triangle

no code implementations21 Oct 2021 Alexey Kroshnin, Viacheslav Vasilev, Egor Ershov, Denis Shepelev, Dmitry Nikolaev, Mikhail Tchobanou

One of the classical approaches to solving color reproduction problems, such as color adaptation or color space transform, is the use of low-parameter spectral models.

Physically-Plausible Illumination Distribution Estimation

1 code implementation ICCV 2023 Egor Ershov, Vasily Tesalin, Ivan Ermakov, Michael S. Brown

Motivated by this observation, we revisit AWB to predict a distribution of plausible illuminations for use in white balance.

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