Hyeran Byun
연세대학교 공과대학 전기전자공학과 · 컴퓨터과학
이 교수의 연구실은 컴퓨터 비전과 기계학습 기반의 실시간 행동 인식 및 교통 표지 인식 기술을 핵심으로 연구를 진행하고 있습니다. 특히, 조도 변화에 강한 알고리즘 설계와 GPGPU를 활용한 실시간 처리 기술을 접목하여 실제 환경에서의 적용 가능성을 높이고 있습니다. 또한, 시각적 패턴 인식에서 공간적, 시간적 정보뿐 아니라 맥락 정보까지 통합하는 다중 스트림 신경망 아키텍처 개발에 주력하고 있습니다.
표시된 성과는 수집된 데이터 기준으로 산출되며, 일부 차이가 있을 수 있습니다.
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
