?
Real-time Vision-based Depth Reconstruction with NVidia Jetson
P. 1–6.
В книге
Prague: IEEE, 2019.
G-quadruplexes (GQs) are non-canonical DNA structures encoded by G-flipons with potential roles in gene regulation and chromatin structure. Here, we explore the role of G-flipons in tissue specification. We present a deep learning-based framework for the genome-wide G-flipon predictions across 14 human tissue types. The model was trained using high-confidence experimental maps of GQ-forming sequences ...
Добавлено: 8 августа 2025 г.
Бекназаров Н. С., , in: Z-DNA: Methods and Protocols.: United States of America: Springer, 2023. P. 217–226.
Here we describe an approach that uses deep learning neural networks such as CNN and RNN to aggregate information from DNA sequence; physical, chemical, and structural properties of nucleotides; and omics data on histone modifications, methylation, chromatin accessibility, and transcription factor binding sites and data from other available NGS experiments. We explain how with the ...
Добавлено: 26 декабря 2023 г.
Depth estimation has been an essential task for many computer vision applications, especially in autonomous driving, where safety is paramount. Depth can be estimated not only with traditional supervised learning but also via a self-supervised approach that relies on camera motion and does not require ground truth depth maps. Recently, major improvements have been introduced ...
Добавлено: 1 февраля 2022 г.
Макаров И. А., Guschenko-Cheverda I., , in: Proceedings of IEEE 21st International Symposium on Computational Intelligence and Informatics (CINTI'21), 18-20 Nov. 2021.: NY: IEEE, 2021. P. 000115–000120.
Добавлено: 19 января 2022 г.
Dmitrii Maslov, Макаров И. А., , in: Advances in Computational Intelligence: 16th International Work-Conference on Artificial Neural Networks, IWANN 2021, Virtual Event, June 16–18, 2021, Proceedings, Part I* 1. Vol. 12861.: Springer, 2021. Ch. 38 P. 456–467.
In this paper, we study depth reconstruction via RGB-based, Sparse-Depth, and RGBd approaches. We showed that combination of RGB and Sparse Depth approach in RGBd scenario provides the best results. We also proved that the models performance can be further tuned via proper selection of architecture blocks and number of depth points guiding RGB-to-depth reconstruction. ...
Добавлено: 1 сентября 2021 г.
Rzaev E., Khanaev A., Американов А. А., , in: 2021 International Conference on Industrial Engineering, Applications and Manufacturing (ICIEAM).: IEEE, 2021. P. 719–723.
Добавлено: 4 июля 2021 г.
Computational methods to predict Z-DNA regions are in high demand to understand the functional role of Z-DNA. The previous state-of-the-art method Z-Hunt is based on statistical mechanical and energy considerations about B- to Z-DNA transition using sequence information. Z-DNA CHiP-seq experiment results showed little overlap with Z-Hunt predictions implying that sequence information only is not ...
Добавлено: 11 декабря 2020 г.
Bokovoy A., Muravyev K., Яковлев К. С., , in: Artificial Intelligence. RCAI 2020.: Switzerland: Springer, 2020. P. 46–60.
Добавлено: 2 ноября 2020 г.
Dmitrii Maslov, Макаров И. А., PeerJ Computer Science 2020 Vol. 6 No. e317 P. 1–22
Autonomous driving highly depends on depth information for safe driving. Recently, major improvements have been taken towards improving both supervised and self-supervised methods for depth reconstruction. However, most of the current approaches focus on single frame depth estimation, where quality limit is hard to beat due to limitations of supervised learning of deep neural networks ...
Добавлено: 27 октября 2020 г.
Alisa Korinevskaya, Макаров И. А., , in: Proceedings of IEEE International Symposium on Mixed and Augmented Reality (ISMAR'18).: NY: IEEE, 2019. P. 117–122.
Depth map super-resolution is a challenging computer vision problem. In this paper, we present two deep convolutional neural networks solving the problem of single depth map super-resolution. Both networks learn residual decomposition and trained with specific perceptual loss improving sharpness and perceptive quality of the upsampled depth map. Several experiments on various depth super-resolution benchmark ...
Добавлено: 29 июля 2019 г.
Макаров И. А., Alisa Korinevskaya, Vladimir Aliev, , in: Proceedings of the 2018 ACM ICMR Workshop on Multimedia for Real Estate Tech.: NY: Association for Computing Machinery (ACM), 2018. P. 18–21.
We consider the problem of depth reconstruction from downsampled sparse depth values. We compare our approach with semi-dense depth map interpolation and direct RGB-to-Depth reconstruction solutions on several datasets, including Matterport 3D dataset containing RGB and depth images of 90 building-scale scenes. We demonstrate that the proposed model can produce approximate depth map for over ...
Добавлено: 3 сентября 2018 г.
Макаров И. А., Alisa Korinevskaya, Vladimir Aliev, , in: Proceedings of 2018 41st International Conference on Telecommunications and Signal Processing (TSP).: NY: IEEE, 2018. P. 1–5.
The problem of dense depth map inference from sparse depth values is considered in this paper. We address this issue in situation when one has low-cost sensor data and limited computational resources. We propose a method that performs interpolation and then super-resolution while comparing our approach with the state-of-the-art direct RGB-to-Dense reconstruction solutions. In particular, ...
Добавлено: 3 сентября 2018 г.