T
Thanarat H. Chalidabhongse
Researcher at Chulalongkorn University
Publications - 60
Citations - 2411
Thanarat H. Chalidabhongse is an academic researcher from Chulalongkorn University. The author has contributed to research in topics: Background subtraction & Object detection. The author has an hindex of 12, co-authored 55 publications receiving 2279 citations. Previous affiliations of Thanarat H. Chalidabhongse include King Mongkut's Institute of Technology Ladkrabang.
Papers
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Journal ArticleDOI
Real-time foreground-background segmentation using codebook model
TL;DR: A real-time algorithm for foreground-background segmentation that can handle scenes containing moving backgrounds or illumination variations, and it achieves robust detection for different types of videos is presented.
Proceedings ArticleDOI
Background modeling and subtraction by codebook construction
TL;DR: A new fast algorithm for background modeling and subtraction that can handle scenes containing moving backgrounds or illumination variations (shadows and highlights), and it achieves robust detection for compressed videos.
Proceedings ArticleDOI
2D/3D Vision-Based Mango's Feature Extraction and Sorting
TL;DR: A vision system that can extract 2D and 3D visual properties of mango such as size, projected area, volume, and surface area from images and use them in sorting and the results show the technique could be a good alternative and more feasible method for sorting mango comparing to human's manual sorting.
Proceedings ArticleDOI
An adaptive real-time background subtraction and moving shadows detection
TL;DR: The paper presents a statistical adaptive realtime background subtraction algorithm that is very robust to moving shadows and dynamic scene environment, and proposes a novel "vivacity factor" to measure the activities of foreground objects.
Proceedings ArticleDOI
Players tracking and ball detection for an automatic tennis video annotation
TL;DR: The algorithms for players tracking and ball detection for an automatic broadcast tennis video annotation using a robust non-parametric procedure for estimating density gradients called the mean shift algorithm can precisely classify the players' action into back hand ground stroke and forehand ground stroke.