Haebom Lee
PhD student at BOSCH and Heidelberg University
I am a BOSCH PhD student under Prof. Dr. Carsten Rother's supervison. Prior to coming to Germany, I obtained my Master's and Bachelor's degree in Computer Science at KAIST in Korea. My research interests include Computer Vision, Image Processing, and Deep Learning.
My full CV is [here].
LinkedIn page is [here].
Contact me via haebom.lee at uni-heidelberg.de
News
My lighting estimation paper using transformer network is accepted to IJCV.
My lighting estimation paper is accepted to GCPR 2021.
I started working at Bosch Hildesheim since August 2019.
Our automatic shipment label validation method will be presented at ICIP 2019
My GAN based oversampling for imbalanced data on bearing fault detection paper got accepted to Applied Sciences.
My image processing technique for VR devices paper got accepted to ACM SIGGRAPH Symposium on Interactive 3D Graphics and Games (i3D) 2018
I won a scholarship from LG Electronics
Publications
Haebom Lee, Robert Herzog, Jan Rexilius, Carsten Rother
DAGM German Conference on Pattern Recognition 2021
Sungho Suh, Haebom Lee, Paul Lukowicz, Yong Oh Lee
Neural Networks 2021
Sungho Suh, Haebom Lee, Yong Oh Lee, Paul Lukowicz, Jongwoon Hwang
IEEE International Conference on Image Processing 2019
Sungho Suh*, Haebom Lee*, Jun Jo, Paul Lukowicz and Yong Oh Lee
Applied Sciences 2019, 9(4), 746
*Co-first authors
Haebom Lee, Jun Jo, Yong Oh Lee, Nuriye Korkmaz Zirpel, Leon Abelmann
International Conference on Practical Applications of Computational Biology & Bioinformatics. Springer, Cham, 2018.
Presented at International Conference on PACBB 2018, Toledo, Spain
Haebom Lee, Piotr Didyk
Proceedings of the ACM on Computer Graphics and Interactive Techniques 1.1 (2018): 19.
Presented at ACM SIGGRAPH Symposium on Interactive 3D Graphics and Games 2018, Montreal, Canada
Giljoo Nam, Haebom Lee, Sungsoo Oh, Min H. Kim
IEEE Transactions on Instrumentation and Measurement, Oct. 19, 2015, 65(2), pp.297-304
Haebom Lee, Min H. Kim
Proc. Int. Conf. Image and Signal Processing 2014, Lecture Notes in Computer Science, 8509, June 2014, pp. 26-34 (oral presentation)
Byungsoo Kim, Haebom Lee, Kee-Eung Kim
Undergraduate Research Participation (URP) Program, KAIST, 2008 Fall
Projects
Kaggle Statoil/C-CORE Iceberg Classifier Challenge
Kaggle is well known for hosting deep learning related competitions. Iceberg Classifier Challenge was one of them, encouraging researchers to distinguish icebergs from ships by examining a set of radar images. I designed a CNN model as well as image processing algorithms for preprocessing.
Deep Learning based Segmentation for Autonomous Driving
One of the most important techniques for autonomous driving is image segmentation. Exploiting deep learning in this area is already common because of its accuracy and flexibility. In this project, I tested several state-of-the-art deep learning segmentation algorithms. Furthermore, an efficient method to generate street view images from a simulated scene was also investigated.
Real-time Apparent Resolution Enhancement for VR devices
There have been several apparent resolution enhancement techniques for high-framerate displays using an expensive optimization or a vibrating motor. Those characteristics made the methods unable to be performed on head mounted displays, which are usually showing real-time contents. The proposed technique approximates the time consuming optimization using a set of filters. While there is a slight loss of clarity, it improves apparent resolution in real time as shown in a user experiment. The details will be provided in this page.
Smartphone Display Inspection System
In order to make the smartphone display inspection more reliable, quantified, and automatized, a system based on HDR imaging and image appearance modeling was developed for LG Electronics. The system efficiently classified faulty products, which are containing yellowish regions or black dots. The details are presented in this paper.
PACT Engine for Mobile Devices
PACT Engine is an OpenGL-based 3D game engine developed in Com2uS, a mobile game company. The engine consists of a viewer, a 3ds Max plugin and libraries for iOS and Android. It supports keyframe-based animations for bones, vertices and textures
Android Wrapper Framework for Game Development
An OS-independent development environment gets rid of problems occur in porting a game from one platform to another. The 'Wrapper' project was therefore initiated in Com2uS to enable the developers to focus only on a game itself. The project is categorized into 'iOS Wrapper' and 'Android Wrapper'. I took the charge of the Android wrapper and successfully launched the first Android game of Com2uS.
Mobile Game Development
I participated as an engine programmer in several game developments: Homerun Battle 3D, Homerun Battle 2, Heavy Gunner 3D, Zombie Runaway, Swing Shot, and Slice It!.
Netflix Prize
Netflix set $1,000,000 prize for the accurate recommendation system. I implemented k-Nearest Neighbors, k-Means Clustering, and Naive Bayesian Classifier on a Hadoop-based distributed system to participate in the Netflix Prize.