no code implementations • 19 Dec 2022 • Jyri Maanpää, Iaroslav Melekhov, Josef Taher, Petri Manninen, Juha Hyyppä
Robustness of different pattern recognition methods is one of the key challenges in autonomous driving, especially when driving in the high variety of road environments and weather conditions, such as gravel roads and snowfall.
no code implementations • 10 Oct 2021 • Iaroslav Melekhov, Zakaria Laskar, Xiaotian Li, Shuzhe Wang, Juho Kannala
Fully-supervised CNN-based approaches for learning local image descriptors have shown remarkable results in a wide range of geometric tasks.
no code implementations • 29 Sep 2021 • Dmitry Senushkin, Iaroslav Melekhov, Mikhail Romanov, Anton Konushin, Juho Kannala, Arno Solin
We present a novel gradient-based multi-task learning (MTL) approach that balances training in multi-task systems by aligning the independent components of the training objective.
1 code implementation • ICCV 2021 • Shuzhe Wang, Zakaria Laskar, Iaroslav Melekhov, Xiaotian Li, Juho Kannala
For several emerging technologies such as augmented reality, autonomous driving and robotics, visual localization is a critical component.
no code implementations • 28 Oct 2020 • Jyri Maanpää, Josef Taher, Petri Manninen, Leo Pakola, Iaroslav Melekhov, Juha Hyyppä
Autonomous driving is challenging in adverse road and weather conditions in which there might not be lane lines, the road might be covered in snow and the visibility might be poor.
no code implementations • 16 Aug 2020 • Iaroslav Melekhov, Gabriel J. Brostow, Juho Kannala, Daniyar Turmukhambetov
Local features that are robust to both viewpoint and appearance changes are crucial for many computer vision tasks.
no code implementations • 8 Aug 2019 • Roman Solovyev, Iaroslav Melekhov, Timo Lesonen, Elias Vaattovaara, Osmo Tervonen, Aleksei Tiulpin
In contrast to the previous art, we, for the first time, propose to estimate CTR with uncertainty bounds.
4 code implementations • 29 Jul 2019 • Aleksei Tiulpin, Iaroslav Melekhov, Simo Saarakkala
This paper addresses the challenge of localization of anatomical landmarks in knee X-ray images at different stages of osteoarthritis (OA).
no code implementations • 15 Apr 2019 • Zakaria Laskar, Iaroslav Melekhov, Hamed R. -Tavakoli, Juha Ylioinas, Juho Kannala
The main contribution is a geometric correspondence verification approach for re-ranking a shortlist of retrieved database images based on their dense pair-wise matching with the query image at a pixel level.
1 code implementation • 19 Oct 2018 • Iaroslav Melekhov, Aleksei Tiulpin, Torsten Sattler, Marc Pollefeys, Esa Rahtu, Juho Kannala
This paper addresses the challenge of dense pixel correspondence estimation between two images.
Ranked #2 on
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no code implementations • 31 Oct 2017 • Iaroslav Melekhov, Juho Kannala, Esa Rahtu
In this work we propose a neural network based image descriptor suitable for image patch matching, which is an important task in many computer vision applications.
no code implementations • 31 Jul 2017 • Zakaria Laskar, Iaroslav Melekhov, Surya Kalia, Juho Kannala
The camera location for the query image is obtained via triangulation from two relative translation estimates using a RANSAC based approach.
no code implementations • 23 Mar 2017 • Iaroslav Melekhov, Juha Ylioinas, Juho Kannala, Esa Rahtu
In this paper, we propose an encoder-decoder convolutional neural network (CNN) architecture for estimating camera pose (orientation and location) from a single RGB-image.
1 code implementation • 5 Feb 2017 • Iaroslav Melekhov, Juha Ylioinas, Juho Kannala, Esa Rahtu
This paper presents a convolutional neural network based approach for estimating the relative pose between two cameras.