Browsing by Author "Yang, Chien-Ting"

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A Hybridization of Immune Algorithm with Particle Swarm Optimization for Neuro-Fuzzy Classifiers
(2008-09-01)
Lin, Chin-Teng
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Yang, Chien-Ting
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Su, Miin-Tsair
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電控工程研究所
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Institute of Electrical and Control Engineering
In order to enhance the immune algorithm (IA) performance and find the optimal solution when dealing with difficult problems, we propose an efficient immune-based particle swarm optimization (IPSO) for neuro-fuzzy classifiers to solve the skin color detection problem. The proposed IPSO combines the immune algorithm (IA) and particle swarm optimization (PSO) to perform parameter learning. The IA uses the clonal selection principle, such that antibodies between others of high similar degree are affected, and these antibodies, after the process, will have higher quality, accelerating the search and increasing the global search capacity. The PSO algorithm has proved to be very effective for solving global optimization. It is not only a recently invented high-performance optimizer that is easy to understand and implement, but it also requires little computational bookkeeping and generally only a few lines of code. Hence, we employed the advantages of PSO to improve the mutation mechanism of the immune algorithm. Simulations have shown the performance and applicability of the proposed method.
An Efficient and Robust Moving Shadow Removal Algorithm and Its Applications in ITS
(2010)
Lin, Chin-Teng
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Yang, Chien-Ting
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Shou, Yu-Wen
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Shen, Tzu-Kuei
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電控工程研究所
We propose an efficient algorithm for removing shadows of moving vehicles caused by non-uniform distributions of light reflections in the daytime. This paper presents a brand-new and complete structure in feature combination as well as analysis for orientating and labeling moving shadows so as to extract the defined objects in foregrounds more easily in each snapshot of the original files of videos which are acquired in the real traffic situations. Moreover, we make use of Gaussian Mixture Model (GMM) for background removal and detection of moving shadows in our tested images, and define two indices for characterizing non-shadowed regions where one indicates the characteristics of lines and the other index can be characterized by the information in gray scales of images which helps us to build a newly defined set of darkening ratios (modified darkening factors) based on Gaussian models. To prove the effectiveness of our moving shadow algorithm, we carry it out with a practical application of traffic flow detection in ITS (Intelligent Transportation System)-vehicle counting. Our algorithm shows the faster processing speed, 13.84 ms/frame, and can improve the accuracy rate in 4% similar to 10% for our three tested videos in the experimental results of vehicle counting.
Multiclient Identification System Using Adaptive Probabilistic Model
(2010)
Lin, Chin-Teng
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Siana, Linda
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Shou, Yu-Wen
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Yang, Chien-Ting
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電控工程研究所
This paper aims at integrating detection and identification of human faces in a more practical and real-time face recognition system. The proposed face detection system is based on the cascade Adaboost method to improve the precision and robustness toward unstable surrounding lightings. Our Adaboost method innovates to adjust the environmental lighting conditions by histogram lighting normalization and to accurately locate the face regions by a region-based-clustering process as well. We also address on the problem of multi-scale faces in this paper by using 12 different scales of searching windows and 5 different orientations for each client in pursuit of the multi-view independent face identification. There are majorly two methodological parts in our face identification system, including PCA (principal component analysis) facial feature extraction and adaptive probabilistic model (APM). The structure of our implemented APM with a weighted combination of simple probabilistic functions constructs the likelihood functions by the probabilistic constraint in the similarity measures. In addition, our proposed method can online add a new client and update the information of registered clients due to the constructed APM. The experimental results eventually show the superior performance of our proposed system for both offline and real-time online testing.