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Realizing the automated and online detection of crowd anomalies from surveillance CCTVs is a research-intensive and application-demanding task. This research proposes a novel technique for detecting crowd abnormalities through analyzing the spatial and temporal features of the input video signals. This integrated solution defines an image descriptor that reflects the global motion information over...
To determine the real-time traffic state accurately in road network or intersections, traffic state identification method is proposed based on image processing technology. During the image process, by analyzing the image texture features, the multi-scale block local binary patterns are taken as the features. The road traffic state identification model is established based on support vector classification...
Feature selection is often considered as a key step in text categorization. In this paper, we proposed a new feature selection algorithm, named AD, which comprehensively measures the degree of relevance and distinction of terms occur in document set. We evaluated AD on three benchmark document collections, 20-Newsgroups, Reuters-21578 and WebKB, using two classification algorithms, Naive Bayes and...
Personal credit assessment is carried out by setting up a mathematical model to count, calculate and analyze the personal credit data. At present personal credit assessment has already became a kind of worldwide industry. In this paper we combine kernel principal component analysis and support vector machine to propose a new mathematical model based on KPCA and SVM. We extract personal credit data...
In apple harvesting robot stereo vision system, fruit recognition based on least squares support vector machine (LS-SVM) and calibration based on binocular vision are proposed, in order to gain the location information of apples including depth. Firstly, vector median filtering, opening and closing operations are employed, then feature vectors, H and S components in HIS color model and shape features,...
According to the non-stationary feature of ECG signal, a new classification method of arrhythmia is introduced. This method combines empirical mode decomposition (EMD) with singular value decomposition (SVD), using support vector machines (SVM) for classifying. First, ECG signal is decomposed into a set of intrinsic mode function (IMF) using empirical mode decomposition method. The initial feature...
Feature extraction is of great importance in condition monitoring and fault diagnosis of rolling machinery. Nonlinear dimensionality reduction (NDR) theories brought a new idea for recognizing and predicting the underlying nonlinear behavior. In this paper, we propose a NDR based feature extraction method for fault classification of rolling element bearing. Original feature spaces are constructed...
In the robot vision system of the apple harvesting robot, the key is to recognize and locate the apple. To solve recognition questions such as high error rate, too much calculation and time consuming, a new recognizing method, support vector machine (SVM) is applied to improve recognition accuracy and efficiency. At first, vector median filter is used to remove the color images noise of apple fruit...
A non-intrusive objective measurement for estimating the quality of output speech without input clean speech is proposed for both narrowband and wideband speech based on Gaussian mixture model (GMM) and support vector regression (SVR). Perceptual linear predictive (PLP) features are extracted and clustered by GMM as an artificial reference model from clean speech. Input speech is separated into three...
A non-intrusive objective assessment method is proposed to estimate the quality of output speech without the input reference speech based on narrowband speech test database. From clean speech Perceptual Linear Predictive (PLP) features are extracted and clustered by Gaussian Mixture Model (GMM) as an artificial reference model. Input speech is separated into three classes, for which the consistency...
A novel method is presented in this paper to study the use of SVM classifiers for multiple feature classification. While commonly multiple binary SVM classifiers are trained on features individually and the outputs of the classifiers are linearly combined for multiple feature classification, our method trains and combines these classifiers simultaneously with lower complexity. To obtain the optimal/suboptimal...
Text classification has been considered as a hot research area in data mining. This paper presents a new approach combining hidden Markov model (HMM) with support vector machine (SVM) for text classification. HMMs are used to as a feature extractor and then a new feature vector is normalized as the input of SVMs, so the trained SVMs can classify unknown texts successfully. The experimental results...
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