Browsing by Author "Hong, Chao-Ting"

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Face recognition using nonparametric-weighted Fisherfaces
(2012)
Li, Dong-Lin
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Prasad, Mukesh
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Hsu, Sheng-Chih
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Hong, Chao-Ting
;
Lin, Chin-Teng
This study presents an appearance-based face recognition scheme called the nonparametric-weighted Fisherfaces (NW-Fisherfaces). Pixels in a facial image are considered as coordinates in a high-dimensional space and are transformed into a face subspace for analysis by using nonparametric-weighted feature extraction (NWFE). According to previous studies of hyperspectral image classification, NWFE is a powerful tool for extracting hyperspectral image features. The Fisherfaces method maximizes the ratio of between-class scatter to that of within-class scatter. In this study, the proposed NW-Fisherfaces weighted the between-class scatter to emphasize the boundary structure of the transformed face subspace and, therefore, enhances the separability for different persons' face. The proposed NW-Fisherfaces was compared with Orthogonal Laplacianfaces, Eigenfaces, Fisherfaces, direct linear discriminant analysis, and null space linear discriminant analysis methods for tests on five facial databases. Experimental results showed that the proposed approach outperforms other feature extraction methods for most databases.
SMOKE DETECTION USING SPATIAL AND TEMPORAL ANALYSES
(2012-07-01)
Lee, Chen-Yu
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Lin, Chin-Teng
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Hong, Chao-Ting
;
Su, Miin-Tsair
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電機工程學系
"Video-based fire detection is currently a fairly common application with the growth in the number of installed surveillance video systems. Moreover, the related processing units are becoming more powerful. Smoke is an early sign of most fires; therefore, selecting an appropriate smoke-detection method is essential. However, detecting smoke without creating a false alarm remains a challenging problem for open or large spaces with the disturbances of common moving objects, such as pedestrians and vehicles. This study proposes a novel video-based smoke-detection method that can be incorporated into a surveillance system to provide early alerts. In this study, the process of extracting smoke features from candidate regions was accomplished by analyzing the spatial and temporal characteristics of video sequences for three important features: edge blurring, gradual energy changes, and gradual chromatic configuration changes. The proposed spatial-temporal analysis technique improves the feature extraction of gradual energy changes. In order to make the video smoke-detection results more reliable, these three features Were combined using a support vector machine (SVM) technique and a temporal-based alarm decision unit (AD U) was also introduced. The effectiveness of the proposed algorithm was evaluated on a PC with an Intel (R) Core (TM) 2 Duo CPU (2.2 GHz) and 2 GB RAM. The average processing time was 32.27 ms per frame; i.e., the proposed algorithm can process 30.98 frames per second. Experimental results showed that the proposed system can detect smoke effectively with a low false-alarm rate and a short reaction time in many real-world scenarios."