T
Tae-Seong Kim
Researcher at Kyung Hee University
Publications - 193
Citations - 6161
Tae-Seong Kim is an academic researcher from Kyung Hee University. The author has contributed to research in topics: Hidden Markov model & Activity recognition. The author has an hindex of 34, co-authored 190 publications receiving 4771 citations. Previous affiliations of Tae-Seong Kim include University of Southern California & Yonsei University.
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A Triaxial Accelerometer-Based Physical-Activity Recognition via Augmented-Signal Features and a Hierarchical Recognizer
TL;DR: An accelerometer sensor-based approach for human-activity recognition using a hierarchical scheme that recognizes three states and 15 activities with an average accuracy of 97.9% using only a single triaxial accelerometer attached to the subject's chest.
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KIKI-net: cross-domain convolutional neural networks for reconstructing undersampled magnetic resonance images.
TL;DR: To demonstrate accurate MR image reconstruction from undersampled k‐space data using cross‐domain convolutional neural networks (CNNs) using cross-domain Convolutional Neural Networks, a parallel version of TSP, is presented.
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Skin lesion segmentation in dermoscopy images via deep full resolution convolutional networks.
TL;DR: Using the full spatial resolutions of the input image could enable to learn better specific and prominent features, leading to an improvement in the segmentation performance.
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Simultaneous detection and classification of breast masses in digital mammograms via a deep learning YOLO-based CAD system
Mohammed A. Al-masni,Mugahed A. Al-antari,J. M. Park,G. Gi,Taeyeon Kim,Patricio Rivera,Edwin Valarezo,Mun-Taek Choi,Seung-Moo Han,Tae-Seong Kim +9 more
TL;DR: A novel Computer-Aided Diagnosis (CAD) system based on one of the regional deep learning techniques, a ROI-based Convolutional Neural Network (CNN) which is called You Only Look Once (YOLO) can handle detection and classification simultaneously in one framework.
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A fully integrated computer-aided diagnosis system for digital X-ray mammograms via deep learning detection, segmentation, and classification.
TL;DR: The results demonstrate that the proposed integrated CAD system, through all stages of detection, segmentation, and classification, outperforms the latest conventional deep learning methodologies.