C
Chia-Hoang Lee
Researcher at National Chiao Tung University
Publications - 61
Citations - 2798
Chia-Hoang Lee is an academic researcher from National Chiao Tung University. The author has contributed to research in topics: Fuzzy logic & Fuzzy classification. The author has an hindex of 24, co-authored 61 publications receiving 2682 citations. Previous affiliations of Chia-Hoang Lee include Purdue University & University of Maryland, College Park.
Papers
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Journal ArticleDOI
Handling forecasting problems using fuzzy time series
TL;DR: A new method to forecast university enrollments is proposed, where the historical enrollments of the University of Alabama shown in [7,8] are used to illustrate the forecasting process.
Book
Improved methods of estimating shape from shading using the light source coordinate system
Chia-Hoang Lee,Azriel Rosenfeld +1 more
TL;DR: An alternative formulation of the surface slant probability density is developed that takes the discrete nature of digital images into account, and that yields a better estimate of the light source direction.
Journal ArticleDOI
Improved methods of estimating shape from shading using the light source coordinate system
Chia-Hoang Lee,Azriel Rosenfeld +1 more
TL;DR: In this paper, the surface tilt is related to the direction of the gray level gradient at the given point, and an alternative formulation of the surface slant probability density is developed that takes the discrete nature of digital images into account, and that yields a better estimate of the light source direction.
Journal ArticleDOI
Motion and structure from orthographic projections
Thomas S. Huang,Chia-Hoang Lee +1 more
TL;DR: Two results are presented: two orthographic views allow an uncountably infinite number of solutions to motion/structure of a rigid body, and a linear algorithm for solving motion/Structure from four point correspondences over three views.
Journal ArticleDOI
A fall detection system using k-nearest neighbor classifier
TL;DR: A fall incident detection system is developed to detect fall incident events and the experiment shows that it could reduce the effect of upper limb activities and the system has a correct rate of 84.44% on fall detection and lying down event detection.