no code implementations • ICCV 2023 • Jihong Ju, Ching Wei Tseng, Oleksandr Bailo, Georgi Dikov, Mohsen Ghafoorian
A key challenge in neural 3D scene reconstruction from monocular images is to fuse features back projected from various views without any depth or occlusion information.
1 code implementation • Computer Vision and Image Understanding 2022 • Francois Rameau, Jinsun Park, Oleksandr Bailo, In So Kweon
In this paper, we present MC-Calib, a novel and robust toolbox dedicated to the calibration of complex synchronized multi-camera systems using an arbitrary number of fiducial marker-based patterns.
1 code implementation • 2 Mar 2021 • Jonghyuk Park, Sukhyun Cho, Dongwoo Kim, Oleksandr Bailo, Heewoong Park, Sanghoon Hong, Jonghun Park
Furthermore, in order to compute the motion similarity from these datasets, we propose a deep learning model that produces motion embeddings suitable for measuring the similarity between different motions of each human body part.
no code implementations • 18 Jan 2019 • Oleksandr Bailo, DongShik Ham, Young Min Shin
In this paper, we describe how to apply image-to-image translation techniques to medical blood smear data to generate new data samples and meaningfully increase small datasets.
1 code implementation • Pattern Recognition Letters 2018 • Oleksandr Bailo, Francois Rameau, Kyungdon Joo, Jinsun Park, Oleksandr Bogdan, In So Kweon
Keypoint detection usually results in a large number of keypoints which are mostly clustered, redundant, and noisy.
1 code implementation • PSIVT 2017 • Oleksandr Bogdan, Oleg Yurchenko, Oleksandr Bailo, Francois Rameau, Donggeun Yoo, In So Kweon
This paper proposes a wearable system for visually impaired people that can be utilized to obtain an extensive feedback about their surrounding environment.
1 code implementation • 20 Oct 2017 • Oleksandr Bailo, Francois Rameau, In So Kweon
In this paper, we propose an efficient algorithm for robust place recognition and loop detection using camera information only.
3 code implementations • ICCV 2017 • Seokju Lee, Junsik Kim, Jae Shin Yoon, Seunghak Shin, Oleksandr Bailo, Namil Kim, Tae-Hee Lee, Hyun Seok Hong, Seung-Hoon Han, In So Kweon
In this paper, we propose a unified end-to-end trainable multi-task network that jointly handles lane and road marking detection and recognition that is guided by a vanishing point under adverse weather conditions.
Ranked #1 on Lane Detection on Caltech Lanes Washington