A progressive morphological filter for removing nonground measurements from airborne LIDAR data
- Keqi Zhang, Shu‐Ching Chen, D. Whitman, M. Shyu, Jianhua Yan, Chengcui Zhang
- Environmental ScienceIEEE Transactions on Geoscience and Remote…
- 5 June 2003
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.
A Novel Anomaly Detection Scheme Based on Principal Component Classifier
- M. Shyu, Shu‐Ching Chen, Kanoksri Sarinnapakorn, LiWu Chang
- Computer Science
- 2003
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.
Automatic Construction of Building Footprints From Airborne LIDAR Data
- Keqi Zhang, Jianhua Yan, Shu‐Ching Chen
- Environmental ScienceIEEE Transactions on Geoscience and Remote…
- 21 August 2006
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.
A Survey on Deep Learning
- Samira Pouyanfar, Saad Sadiq, S. S. Iyengar
- Computer ScienceACM Computing Surveys
- 18 September 2018
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.
Histology Image Classification Using Supervised Classification and Multimodal Fusion
- Tao Meng, Lin Lin, M. Shyu, Shu‐Ching Chen
- Computer ScienceIEEE International Symposium on Multimedia
- 13 December 2010
A framework based on the novel and robust Collateral Representative Subspace Projection Modeling (C-RSPM) supervised classification model for general histology image classification is proposed and experimenting shows that the proposed framework outperforms other well-known classifiers in the comparison and gives better results than the highest accuracy reported previously.
Computational Health Informatics in the Big Data Age
- R. Fang, Samira Pouyanfar, Yimin Yang, Shu‐Ching Chen, S. S. Iyengar
- Computer ScienceACM Computing Surveys
- 14 June 2016
A comprehensive overview of the existing challenges, techniques, and future directions for computational health informatics in the big data age, with a structured analysis of the historical and state-of-the-art methods.
Survey of data management and analysis in disaster situations
- Vagelis Hristidis, Shu‐Ching Chen, Tao Li, Steven Luis, Yi Deng
- Computer ScienceJournal of Systems and Software
- 1 October 2010
Video Semantic Event/Concept Detection Using a Subspace-Based Multimedia Data Mining Framework
- M. Shyu, Zongxing Xie, Min Chen, Shu‐Ching Chen
- Computer ScienceIEEE transactions on multimedia
- 1 February 2008
The promising experimental performance on goal/corner event detection and sports/commercials/building concepts extraction from soccer videos and TRECVID news collections demonstrates the effectiveness of the proposed framework and indicates the great potential of extending the proposed multimedia data mining framework to a wide range of different application domains.
Web document classification based on fuzzy association
- C. Haruechaiyasak, M. Shyu, Shu‐Ching Chen, Xiuqi Li
- Computer ScienceProceedings 26th Annual International Computer…
- 26 August 2002
A method of automatically classifying web documents into a set of categories using the fuzzy association concept is proposed, and shows that this approach yields higher accuracy compared to the vector space model.
Function approximation using robust wavelet neural networks
- Sheng-Tun Li, Shu‐Ching Chen
- Computer Science14th IEEE International Conference on Tools with…
- 4 November 2002
A robust wavelet neural network based on the theory of robust regression for dealing with outliers in the framework of function approximation is proposed by adaptively adjusting the number of training data involved during training, and the efficiency loss in the presence of Gaussian noise is accommodated.
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