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Detecting eyes in images is fundamental for many computer vision applications including face detection, face recognition, and human-computer interaction. Most existing methods are designed and tested on datasets acquired under controlled lab settings (e.g., fixed scale, known poses, clean background, etc.), leaving their performance to be further examined on real-world, uncontrolled images, such as...
In this paper multilinear mean component analysis (MMCA) is introduced as a new algorithm for gait recognition. Compared with traditional PCA and MPCA, the new method MMCA is based on dimensionality reduction by preserving the squared length, and implicitly also the direction of the mean vector of the each mode's original input. The solution is not necessarily corresponding to the top eigenvalues...
Anomaly traffic often breaks out without any omen and brings a breakdown to networks in a short time, and thus the adaptive detection of anomalies in network traffic is an important and challenging task. In this paper, we propose a wavelet-based adaptive approach to detect anomalies in network traffic. We can use wavelet packet transform and continuous wavelet transform to perform the adaptive detect...
Solar radiation is an important factor in forecasting outputs of photovoltaic power systems. A method for solar radiation prediction is proposed based on wavelet transform. The data, including solar radiation and potential input variables, are decomposed into several time-frequency areas via wavelet transform. Given that some variables are relevant in some areas of solar radiation but irrelevant in...
This paper introduces a novel pattern classification approach called l1 norm nearest neighbor convex hull (l1 NNCH) approach and applies it for PCA-based face classification. In l1 NNCH, l1 norm distance from a query to a convex hull of a class is defined as the similarity of nearest neighbor rule. Principle component analysis (PCA), as an efficient technology for extracting feature, is applied...
In this paper, we review the major achievements on the research of fault diagnosis in control systems (FDCS) from three aspects which including fault detection, fault isolation and hybrid intelligent fault diagnosis. Fault detection and isolation (FDI) are two important stages in the diagnosis process while hybrid intelligent fault diagnosis is the hot issue in current research field. The particular...
An approach is proposed for abnormal sections detection in video sequences. In this approach, firstly the histogram is selected to describe the color change in the section, and then the histograms of the frames selected from the section compose the histogram matrix. In order to improve the process efficiency, the principal components analysis (PCA) is used to reduce dimensions of the histogram matrix...
We study the use of kernel subspace methods for learning low-dimensional representations for classification. We propose a kernel pooled local discriminant subspace method and compare it against several competing techniques: Principal Component Analysis (PCA), Kernel PCA (KPCA), and linear local pooling in classification problems. We evaluate the classification performance of the nearest-neighbor rule...
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