FS-Net: Full Scale Network and Adaptive Threshold for Improving Extraction of Micro-Retinal Vessel Structures

no code implementations • 14 Nov 2023 • Melaku N. Getahun, Oleg Y. Rogov, Dmitry V. Dylov, Andrey Somov, Ahmed Bouridane, Rifat Hamoudi

To address these issues in retinal vessel segmentation, we propose a comprehensive micro-vessel extraction mechanism based on an encoder-decoder neural network architecture.

Decoder Retinal Vessel Segmentation +1

NTIRE 2021 Challenge on Quality Enhancement of Compressed Video: Methods and Results

1 code implementation • 21 Apr 2021 • Ren Yang, Radu Timofte, Jing Liu, Yi Xu, Xinjian Zhang, Minyi Zhao, Shuigeng Zhou, Kelvin C. K. Chan, Shangchen Zhou, Xiangyu Xu, Chen Change Loy, Xin Li, Fanglong Liu, He Zheng, Lielin Jiang, Qi Zhang, Dongliang He, Fu Li, Qingqing Dang, Yibin Huang, Matteo Maggioni, Zhongqian Fu, Shuai Xiao, Cheng Li, Thomas Tanay, Fenglong Song, Wentao Chao, Qiang Guo, Yan Liu, Jiang Li, Xiaochao Qu, Dewang Hou, Jiayu Yang, Lyn Jiang, Di You, Zhenyu Zhang, Chong Mou, Iaroslav Koshelev, Pavel Ostyakov, Andrey Somov, Jia Hao, Xueyi Zou, Shijie Zhao, Xiaopeng Sun, Yiting Liao, Yuanzhi Zhang, Qing Wang, Gen Zhan, Mengxi Guo, Junlin Li, Ming Lu, Zhan Ma, Pablo Navarrete Michelini, Hai Wang, Yiyun Chen, Jingyu Guo, Liliang Zhang, Wenming Yang, Sijung Kim, Syehoon Oh, Yucong Wang, Minjie Cai, Wei Hao, Kangdi Shi, Liangyan Li, Jun Chen, Wei Gao, Wang Liu, XiaoYu Zhang, Linjie Zhou, Sixin Lin, Ru Wang

This paper reviews the first NTIRE challenge on quality enhancement of compressed video, with a focus on the proposed methods and results.

AI-enabled Prediction of eSports Player Performance Using the Data from Heterogeneous Sensors

1 code implementation • 7 Dec 2020 • Anton Smerdov, Evgeny Burnaev, Andrey Somov, Anton Stepanov

For this reason, we collected the physiological, environmental, and the game chair data from Pro and amateur players.

Feature Engineering Feature Importance +5

Detecting Video Game Player Burnout with the Use of Sensor Data and Machine Learning

1 code implementation • 29 Nov 2020 • Anton Smerdov, Andrey Somov, Evgeny Burnaev, Bo Zhou, Paul Lukowicz

In this article, we propose the methods based on the sensor data analysis for predicting whether a player will win the future encounter.

BIG-bench Machine Learning Interpretable Machine Learning +8

Collection and Validation of Psychophysiological Data from Professional and Amateur Players: a Multimodal eSports Dataset

3 code implementations • 2 Nov 2020 • Anton Smerdov, Bo Zhou, Paul Lukowicz, Andrey Somov

An important feature of the dataset is simultaneous data collection from five players, which facilitates the analysis of sensor data on a team level.

Feature Importance Person Re-Identification +6

eSports Pro-Players Behavior During the Game Events: Statistical Analysis of Data Obtained Using the Smart Chair

1 code implementation • 18 Aug 2019 • Anton Smerdov, Evgeny Burnaev, Andrey Somov

Today's competition between the professional eSports teams is so strong that in-depth analysis of players' performance literally crucial for creating a powerful team.

Feature Engineering Feature Importance +5

Understanding Cyber Athletes Behaviour Through a Smart Chair: CS:GO and Monolith Team Scenario

1 code implementation • 18 Aug 2019 • Anton Smerdov, Anastasia Kiskun, Rostislav Shaniiazov, Andrey Somov, Evgeny Burnaev

eSports is the rapidly developing multidisciplinary domain.

BIG-bench Machine Learning Feature Engineering +6