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Phill Kyu Rhee
Researcher at Inha University
Publications - 114
Citations - 802
Phill Kyu Rhee is an academic researcher from Inha University. The author has contributed to research in topics: Facial recognition system & Face detection. The author has an hindex of 9, co-authored 114 publications receiving 709 citations. Previous affiliations of Phill Kyu Rhee include Sewanee: The University of the South.
Papers
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Book ChapterDOI
Multi-class Multi-object Tracking Using Changing Point Detection
TL;DR: This paper presents a robust multi-class multi-object tracking (MCMOT) formulated by a Bayesian filtering framework and comparison to state-of-the-art video tracking techniques shows very encouraging results.
Journal ArticleDOI
Web personalization expert with combining collaborative filtering and association rule mining technique
TL;DR: This paper presents a framework of personalization expert by combining collaborative filtering method and association rule mining technique to overcome problems that traditional personalized systems have.
Proceedings ArticleDOI
Color Based Hand and Finger Detection Technology for User Interaction
TL;DR: The methodology for hand detection and the finger detection method are presented and the detected hand and finger can be used to implement the non-contact mouse which can be use to control the home devices such as curtain and television.
Journal ArticleDOI
Active and semi-supervised learning for object detection with imperfect data
TL;DR: This paper addresses the combination of the active learning and semi-supervised learnings, called ASSL, to leverage the strong points of the both learning paradigms for improving the performance of object detection, and demonstrates outstanding performance compared with state-of-art methods on the challenging Caltech pedestrian detection dataset.
Journal ArticleDOI
Embedded face recognition based on fast genetic algorithm for intelligent digital photography
TL;DR: This paper uses a feature selection and feature extraction method based on Gabor wavelet using a fast genetic algorithm (FGA) for efficient FR VLSI design and certify that this method shows recognition rate of over 97.27 % for FERET dataset, which exceeds the performance of the other popular methods.