S
Shu-Ching Chen
Researcher at Florida International University
Publications - 366
Citations - 8797
Shu-Ching Chen is an academic researcher from Florida International University. The author has contributed to research in topics: Deep learning & Image retrieval. The author has an hindex of 43, co-authored 356 publications receiving 7636 citations. Previous affiliations of Shu-Ching Chen include Pennsylvania State University & United States Department of the Navy.
Papers
More filters
Journal ArticleDOI
A progressive morphological filter for removing nonground measurements from airborne LIDAR data
TL;DR: A progressive morphological filter was developed to detect nonground LIDAR measurements and shows that the filter can remove most of the nong round points effectively.
Journal ArticleDOI
A Survey on Deep Learning: Algorithms, Techniques, and Applications
Samira Pouyanfar,Saad Sadiq,Yilin Yan,Haiman Tian,Yudong Tao,Maria Presa Reyes,Mei-Ling Shyu,Shu-Ching Chen,S. Sitharama Iyengar +8 more
TL;DR: A comprehensive review of historical and recent state-of-the-art approaches in visual, audio, and text processing; social network analysis; and natural language processing is presented, followed by the in-depth analysis on pivoting and groundbreaking advances in deep learning applications.
Proceedings Article
A Novel Anomaly Detection Scheme Based on Principal Component Classifier
TL;DR: A novel scheme that uses robust principal component classifier in intrusion detection problems where the training data may be unsupervised and outperforms the nearest neighbor method, density-based local outliers (LOF) approach, and the outlier detection algorithm based on Canberra metric is proposed.
Journal ArticleDOI
Data-Driven Techniques in Disaster Information Management
Tao Li,Ning Xie,Chunqiu Zeng,Wubai Zhou,Li Zheng,Yexi Jiang,Yimin Yang,Hsin-Yu Ha,Wei Xue,Yue Huang,Shu-Ching Chen,Jainendra K. Navlakha,S. Sitharama Iyengar +12 more
TL;DR: A general overview of the requirements and system architectures of disaster management systems is presented and state-of-the-art data-driven techniques that have been applied on improving situation awareness as well as in addressing users’ information needs in disaster management are summarized.
Journal ArticleDOI
Automatic Construction of Building Footprints From Airborne LIDAR Data
TL;DR: A framework that applies a series of algorithms to automatically extract building footprints from airborne light detection and ranging (LIDAR) measurements and demonstrated that the proposed framework identified building footprints well.