Yonsei University · 情報科学
Professor Hyeran Byun's research lab specializes in computer vision and pattern recognition, with a focus on real-time and robust visual understanding in dynamic environments. The lab develops advanced deep learning and GPU-accelerated methods for applications such as traffic sign detection, action recognition, and object-aware video analysis. Key research directions include multimodal feature learning, illumination-invariant perception, and hierarchical modeling for complex visual recognition tasks. The lab emphasizes practical deployment of intelligent vision systems under real-world challenges such as low light and variable scene conditions.
Figures are computed from collected data and may differ slightly.
In this paper, we present a survey on pattern recognition applications of Support Vector Machines (SVMs). Since SVMs show good generalization performance on many real-life data and the approach is properly motivated theoretically, it has been applied to wide range of applications. This paper describes a brief introduction of SVMs and summarizes its various pattern recognition applications.
We present a General Purpose Graphics Processing Unit (GPGPU) based real-time traffic sign detection and recognition method that is robust against illumination changes. There have been many approaches to traffic sign recognition in various research fields; however, previous approaches faced several limitations when under low illumination or wide variance of light conditions. To overcome these drawbacks and improve processing speeds, we propose a method that 1) is robust against illumination chan
In action recognition research, two primary types of information are appearance and motion information that is learned from RGB images through visual sensors. However, depending on the action characteristics, contextual information, such as the existence of specific objects or globally-shared information in the image, becomes vital information to define the action. For example, the existence of the ball is vital information distinguishing "kicking" from "running". Furthermore, some actions share
Open papers in the app to read, cite, and organize with AI.
