Hanseok Ko
고려대학교 컴퓨터학과 · 컴퓨터과학
한세옥 교수의 연구실은 딥러닝 기반의 비정형 데이터 처리와 실시간 시스템 설계에 초점을 맞추고 있습니다. 특히 음성 감정 인식, 지진파형 생성, 수중 영상 향상, 실시간 다중 객체 추적 등 실제 응용이 가능한 비디오 및 시각적·청각적 신호 처리 기술을 개발하고 있습니다. 강력한 생성 모델(GAN 기반)과 온라인 학습 기반의 추적 알고리즘을 활용해 데이터 부족이나 복잡한 환경에서도 높은 정확도를 확보하는 데 주력하고 있습니다. 연구는 실생활 문제 해결을 목표로 하며, 실제 시스템에 적용 가능한 기술 혁신을 지속적으로 추구합니다.
표시된 성과는 수집된 데이터 기준으로 산출되며, 일부 차이가 있을 수 있습니다.
Speech emotion recognition predicts the emotional state of a speaker based on the person's speech. It brings an additional element for creating more natural human-computer interactions. Earlier studies on emotional recognition have been primarily based on handcrafted features and manual labels. With the advent of deep learning, there have been some efforts in applying the deep-network-based approach to the problem of emotion recognition. As deep learning automatically extracts salient features c
In this paper, we propose a novel underwater image enhancement method. Typical deep learning models for underwater image enhancement are trained by paired synthetic dataset. Therefore, these models are mostly effective for synthetic image enhancement but less so for real-world images. In contrast, cycle-consistent generative adversarial networks (CycleGAN) can be trained with unpaired dataset. However, performance of the CycleGAN is highly dependent upon the dataset, thus it may generate unreali
This study addresses the automatic multi‐person tracking problem in complex scenes from a single, static, uncalibrated camera. In contrast with offline tracking approaches, a novel online multi‐person tracking method is proposed based on a sequential tracking‐by‐detection framework, which can be applied to real‐time applications. A two‐stage data association is first developed to handle the drifting targets stemming from occlusions and people's abrupt motion changes. Subsequently, a novel online
Realistic synthetic data can be useful for data augmentation when training deep learning models to improve seismological detection and classification performance. In recent years, various deep learning techniques have been successfully applied in modern seismology. Due to the performance of deep learning depends on a sufficient volume of data, the data augmentation technique as a data-space solution is widely utilized. In this paper, we propose a Generative Adversarial Networks (GANs) based mode
This paper addresses the problem of multi-object tracking in complex scenes by a single, static, uncalibrated camera. Tracking-by-detection is a widely used approach for multi-object tracking. Challenges still remain in complex scenes, however, when this approach has to deal with occlusions, unreliable detections (e.g., inaccurate position/size, false positives, or false negatives), and sudden object motion/appearance changes, among other issues. To handle these problems, this paper presents a n
