This paper proposes a novel method to evaluate Traffic Signal control System(TSCS) based on Artificial Transportation systems(ATS). Using this method, we can generate travel demand based on individual's activities...
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Learning control has been an active topic of research for several decades, and is of theoretical, as well as practical, significance. Current theories and developments in learning control are discussed. Following a br...
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A novel image deblurring method based on high-order non-local range Markov Random Field (NLR-MRF) prior is proposed in the paper. NLR-MRF is an effective statistical framework to model prior knowledge of natural image...
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Wireless sensor networks are characterized by multihop network. Some nodes in network are required to forward a disproportionately high amount of traffic and die early, leaving the unmonitored areas in network and lea...
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This paper presents a novel closed-loop method for a multilink robotic fish to mimic the C-start maneuver, in which the turning speed and precision are emphasized. The turning speed is maximized by carefully designed ...
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Modern power grid is a typical multi-level complex giant system. The conventional analytical methods based on reductionism can't provide sufficient guidance for its operation and management. complex system theory,...
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The far-field intensity is detected from far-field image to estimate the piston distance between two gratings. The image processing algorithm includes projections along the horizontal and vertical directions, search f...
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Polarity shifting has been a challenge to automatic sentiment classification. In this paper, we create a corpus which consists of polarity-shifted sentences in various kinds of product reviews. In the corpus, both the...
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As an efficient business process execution language which supports web services, BPEL4WS is widely supported by the academic and the industrial circles. According to the shortcomings such as number of computer terms, ...
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Histogram features, such as SIFT, HOG, LBP et al, are widely used in modern computer vision algorithms. According to [18], chi-square distance is an effective measure for comparing histogram features. In this paper, w...
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ISBN:
(纸本)9781457701221
Histogram features, such as SIFT, HOG, LBP et al, are widely used in modern computer vision algorithms. According to [18], chi-square distance is an effective measure for comparing histogram features. In this paper, we propose a new method, named the Quadric-chi similarity metric learning (QCSML) for histogram features. The main contribution of this paper is that we propose a new metric learning method based on chi-square distance, in contrast with traditional Mahalanobis distance metric learning methods. The use of quadric-chi similarity in our method leads to an effective learning algorithm. Our method is tested on SIFT features for face identification, and compared with the state-of-art metric learning method (LDML) on the benchmark dataset, the Labeled Faces in the Wild (LFW). Experimental results show that our method can achieve clear performance gains over LDML.
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